Panax ginseng has anti-proliferation, anti-invasion, and anti-metastatic activity. However, the mechanisms by which G-Rh2 induces apoptosis of lung cancer cells are unclear. In the present work, a G-Rh2 target-lung cancer network was constructed and analyzed by the network pharmacology approach. A total of 91 compound-targets of G-Rh2 was obtained based on the compound-target network analysis, and 217 targets were identified for G-Rh2 against lung cancer by PPI network analysis. The 217 targets were significantly enriched in 103 GO terms with FDR <0.05 as threshold in the GO enrichment analysis. In KEGG pathway enrichment analysis, all the candidate targets were significantly enriched in 143 pathways, among of which PI3K-Akt signaling pathway was identified as one of the top enriched pathway. Besides, G-Rh2 induced apoptosis in human lung epithelial (A549) cells was verified in this work. G-Rh2 significantly inhibited the proliferation of A549 cells in a dose-dependent manner, and the apoptosis rate significantly increased from 4.4% to 78.7% using flow cytometry. Western blot analysis revealed that the phosphorylation levels of p85, PDK1, Akt and IκBα were significantly suppressed by G-Rh2. All the experimental findings were consistent with the network pharmacology results. Research findings in this work will provide potential therapeutic value for further mechanism investigations.Ginsenoside Rh2 (G-Rh2), a rare protopanaxadiol (PPD)-type triterpene saponin, from  Lung cancer is one of the most diagnosed cancers and a leading cause of cancer deaths. Smoking is the main cause (∼80% of cases) of lung cancer . Other cPanax ginseng -protopanaxadiol saponin extracted from the root of  ginseng , has beeer cells , stimulaer cells . G-Rh2 wer cells . G-Rh2 ier cells . HoweverNetwork pharmacology is a systematic approach that integrates pharmacologic, computational, and experimental methods to illuminate the molecular mechanisms of drugs . It can G-Rh2 was purchased from Chengdu Phytoelite Bio-Technology Co., Ltd.  and the purity (>98%) was determined by high-performance liquid chromatography . 3-  (http://www.swisstargetprediction.ch/) (http://sea.bkslab.org/)  , STITCH ion 5.0) , SwissTaion.ch/) , and Simab.org/)  to obtaihttps://geneticassociationdb.nih.gov/), which is a database of genetic association data from complex diseases and disorders. Lung cancer was imported as a keyword and the disease targets associated with it were provided in the database.The potential targets of Lung cancer were identified by the Genetic Association Database (TM), Biological General Repository for Interaction Datasets (BioGRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction database (MINT), and biomolecular interaction network database (BIND) using the plug-in Bisogenet  were analysed by the Database of Interacting Proteins  (http://kobas.cbi.pku.edu.cn/) that assigned Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for pathway analysis  . Functioanalysis . The sig2.The A549 lung cancer cell line was purchased from the China Center for Type Culture Collection  and cultured in MEM supplemented with 10% FBS, 100 U/mL penicillin and 100 μg/ml streptomycin at 37°C in a humidified atmosphere with 5% CO4 cells/well. After adhered to the plates for overnight, cells were treated with different concentrations of G-Rh2 for 24 and 48 h. The medium was then removed and supplemented with 100 µL MTT solution for 4 h, following with 100 µL stopping buffer. The absorbance was determined at 550 nm using a microplate reader .The viability of A549 cells was measured by MTT assay. Briefly, cells were seeded into 96-well plates at 2 × 10The apoptosis ratio of cells was analyzed by Annexin V-FITC Apoptosis Detection Kit according to the instruction of the manufacturer. In brief, the cells treated with different concentration of G-Rh2 for 24 h were harvested and centrifuged at 1,500 rpm for 3 min to remove the medium. The precipitation was then resuspended in 100 µL 1 × binding buffer. Subsequently, the cells were stained with Annexin-FITC and PI for 15 min in the dark. After added 400 µL 1 × binding buffer, the samples were evaluated by a Accuri C6 Plus flow cytometer .6 A549 cells were seeded into 40 mm Petri dish and grown for overnight. Then the cells were treated with G-Rh2 for different time points. After collection, the cells were lysed by RIPA lysis buffer and the protein content was determined by Bradford reagent, using bovine serum albumin (BSA) as a standard. Total protein were separated by 20% SDS-polyacrylamide gels for 1 h at 100 V, transferred to PVDF membranes, and blocked with 5% BSA in Tris-buffered saline containing Tween 20 (1 × TTBS) for 2 h. After washed with 1 × TTBS, the PVDF membranes were then incubated with primary antibodies (1:500–1:2000) in 5% BSA at 4°C for overnight, followed by washing and incubated for 1 h with HRP-conjugated secondary anti-IgG (1:500). The bands were then visualized by the eECL Kit and photographed using Tanon 5200 Multi imaging system .A total of 1 × 10p < 0.05 indicated significant differences.The results have been represented as the mean ± SD. Variances among two groups were analyzed by Student’s t-test. Data analysis was completed using SPSS 20.0 . We evaluated the ability of G-Rh2 to inhibit the viability and induce apoptosis of A549 cells using a network pharmacology approach. The compound-target network was created by one approach. The network of G-Rh2 and its targets from PharmMapper server, SwissTargetPrediction, a similarity ensemble approach, and the STITCH database was constructed as shown in Biological networked systems include functional units or protein complexes in PPI networks and disease- or drug-target genes can be analyzed according to complex network theory . PPIs arThe interaction network of G-Rh2 comprised 91 putative targets of G-Rh2 and 431 lung cancer-related targets. The interaction network encompassed 512 interacting proteins and 1,589 interactions . The strDAVID was used to carry out GO analysis to elucidate the function of 217 candidate targets in biological process, cellular component, and molecular function. One hundred and three GO terms with FDR <0.05 were significantly enriched: 73 in biological process, 11 in cellular component, and 19 in molecular function. The top 10 GO terms enriched in each sub-ontology are shown in p < 0.005) including pathways in cancer, the PI3K-Akt signaling pathway, proteoglycans in cancer, focal adhesion, the Jak-STAT signaling pathway, the FoxO signaling pathway, and apoptosis were obtained. via the PI3K-Akt signaling pathway. In addition, the Wnt/β-catenin, mTOR, VEGF, EGFR, and metabolic signaling pathways are important in lung cancer (The KEGG pathway analysis was performed by KOBAS and 143 significantly enriched pathways (corrected g cancer .The significantly enriched pathways and genes were used to construct a gene-pathway network. As shown in 50 of G-Rh2 on A549 cells at 24 and 48 h were calculated respectively as 42.75 and 36.25 µM which was consist with previous report (G-Rh2 suppresses cell proliferation, causes G1-phase arrest, enhances the activity of capase-3, and induces apoptosis in A549 cells . To valis report .Apoptosis, an orderly process of programmed cell death, is central to development of cancers. It involves of the activation, expression and regulation of a series of genes, and inhibits the growth of tumor cells. Apoptosis may not the main way for the death of many cancers response to common treatments . G-Rh2 wvia inhibiting the PI3K/Akt signaling pathway. Similarly, the inactivation of IκBα means G-Rh2 suppress the cell survival via inhibiting the NF-κB signaling pathway and to induces cancer cells death. These results are consistent with that deduced from network pharmacology analyses.The PI3K/Akt signaling pathway operates by phosphorylation and dephosphorylation of the substrate-level . PI3K, pvia inhibiting the PI3K-Akt signaling pathway. It is consistent to the predicted results in the network pharmacology analyses.The potential targets and signaling pathways of G-Rh2 on human lung cancer were predicted in this work by network pharmacology approach. According to the network pharmacology analyses results, 217 genes/proteins were predicted as potential targets for G-Rh2. Besides, bioinformatics analysis on the predicted targets revealed that over 140 pathways, of which PI3K-Akt signaling pathway is top signaling pathway involved in the underlying mechanisms of G-Rh2. Confirmatory experiments showed that G-Rh2 inhibits proliferation of human lung cancer A549 cells, and induces cells apoptosis


Sodium-glucose cotransporter 2 (SGLT2) inhibitors have cardiorenal protective effects regardless of whether they are combined with type 2 diabetes mellitus, but their specific pharmacological mechanisms remain undetermined.We used databases to obtain information on the disease targets of “Chronic Kidney Disease,” “Heart Failure,” and “Type 2 Diabetes Mellitus” as well as the targets of SGLT2 inhibitors. After screening the common targets, we used Cytoscape 3.8.2 software to construct SGLT2 inhibitors' regulatory network and protein-protein interaction network. The clusterProfiler R package was used to perform gene ontology functional analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analyses on the target genes. Molecular docking was utilized to verify the relationship between SGLT2 inhibitors and core targets.Seven different SGLT2 inhibitors were found to have cardiorenal protective effects on 146 targets. The main mechanisms of action may be associated with lipid and atherosclerosis, MAPK signaling pathway, Rap1 signaling pathway, endocrine resistance, fluid shear stress, atherosclerosis, TNF signaling pathway, relaxin signaling pathway, neurotrophin signaling pathway, and AGEs-RAGE signaling pathway in diabetic complications were related. Docking of SGLT2 inhibitors with key targets such as GAPDH, MAPK3, MMP9, MAPK1, and NRAS revealed that these compounds bind to proteins spontaneously.Based on pharmacological networks, this study elucidates the potential mechanisms of action of SGLT2 inhibitors from a systemic and holistic perspective. These key targets and pathways will provide new ideas for future studies on the pharmacological mechanisms of cardiorenal protection by SGLT2 inhibitors. Diabetes mellitus (DM) is one of the world's most common public health issues, with the 10th edition of the International Diabetes Federation estimating a prevalence of 537 million people in 2021, and ~783.2 million people are expected to have DM by 2045 –8. T2DM Sodium-glucose cotransporter 2 (SGLT2) is a key glucose transporter protein in the kidney that accounts for nearly 90% of glucose reabsorption from primary urine, and SGLT2 inhibitors are novel oral drugs for the treatment of T2DM. The United States Food and Drug Administration and the European Union have currently approved four SGLT2 inhibitors  . IpragliNetwork pharmacology is a discipline based on systems biology and multidirectional pharmacology that uses specific nodes and adopts biomolecular network analysis methods to conduct drug molecular design and target analysis . When cohttps://pubchem.ncbi.nlm.nih.gov/) (https://www.drugbank.com/) with probability>0 screening (https://www.drugbank.com/) was also utilized to forecast the targets  . The strcreening . To pred targets . The inthttp://www.genecards.org/), Disgenet (https://www.disgenet.org/), Online Mendelian Inheritance in Man , Pharmacogenomics Knowledgebase  and Therapeutic Target Database .In order to obtain potential targets, the keywords “Chronic Kidney Disease,” “Heart Failure” and “Type 2 Diabetes Mellitus” were retrieved from Genecards (Venn diagrams were created using R packages (“VennDiagram”) from the intersection of medication targets acquired in item 2.1 and illness targets obtained in item 2.2. The regulatory network was constructed using the Cytoscape3.8.2 software, with SGLT2 inhibitors and intersection targets serving as nodes.https://www.string-db.org/), screened with an interaction score ≥0.4, unlinked nodes in the network were hidden, the remaining parameters were left unchanged, and the obtained results were exported in text form into the Cytoscape 3.8.2 software for visualization . The obtained KEGG pathway was brought into Cytoscape 3.8.2, and the “pathway-gene” network was built based on the degree value.Gene ontology (GO) categorizes genes into three groups: biological process (BP), cellular component (CC), and molecular function (MF). The Kyoto encyclopedia of genes and genomes (KEGG) was used to investigate the signaling pathways involved in genes. GO and KEGG enrichment analysis visualization is completed using R packages “org.hs.eg. db,” “DOSE,” “clusterProfiler,” “enrichPlot,” “Colorspace,” “stringi” and “GGploT2,” and output the enriched bubble plots in accordance the number and significance of enrichment (https://www.rcsb.org/)  , and opesb.org/) . The Autsb.org/) .SwissTargetPrediction was utilized to obtain 71 Ertugliflozin targets, 103 Sotagliflozin targets, 53 Canagliflozin targets, 60 Dapagliflozin targets, 17 Empagliflozin targets, 57 Tofogliflozin targets, and 59 Ipragliflozin targets. Eight Canagliflozin targets, 11 Dapagliflozin targets, and 10 Empagliflozin targets were found in Drugbank. After de-duplicating and combining, 164 SGLT2 inhibitor targets were found.After de-duplication and merging, a total of 12,132 HF-related targets, 12,091 CKD-related targets, and 9,910 T2DM-related targets were obtained by searching in Genecards, DisGeNet, OMIM, PharmGKB, and TTD databases. A Venn diagram was used to identify the 146 targets of SGLT2 inhibitors that intersected with disease . The 146The PPI network had 1,049 pairs of interactions, and the top 10 key targets in the PPI network obtained by the Cytohubba plug-in were: SRC, EGFR, MAPK3, GAPDH, ALB, MMP9, MMP2, ICAM1, VCAM1, and MMP1 ; the detP < 0.05. The enrichment results revealed a total of 1,089 BP, which mainly involved regulating protein serine/threonine kinase activity, peptidyl-tyrosine phosphorylation, and peptidyl-tyrosine modification. The overall number of CC was 66, with most of them relating to membrane raft and membrane microdomains. The total number of MF included 145, with protein serine/threonine kinase activity and protein tyrosine kinase activity. GO functional analysis was performed on 146 intersecting targets, and the results were screened at The cardiorenal protective effect of SGLT2 inhibitors may be related to 147 KEGG signaling pathways in diabetic complications, including lipid and atherosclerosis, MAPK signaling pathway, Rap1 signaling pathway, endocrine resistance, fluid shear stress, and atherosclerosis, TNF signaling pathway, relaxin signaling pathway, neurotrophin signaling pathway, and AGEs-RAGE signaling pathway . The topThe intersection targets of PPI network and pathways-targets network GAPDH, MAPK3, MMP9, MAPK1, and NRAS were docked with SGLT2 inhibitors. Binding energy <0 indicates that the ligand and receptor are spontaneous bindings, and the lower the affinity value in the docking result, the more stable the interaction between the target and the active ingredient. Molecular docking revealed that the five important targets mentioned above have good binding activity to SGLT2 inhibitors . The topThe main complications of T2DM are HF and CKD, and the three can interact. Patients with T2DM develop a variety of cardiac abnormalities, a phenomenon known as “diabetic cardiomyopathy” . FurtherIn the regulatory network, there are 7 SGLT2 inhibitors, namely Ertugliflozin, Sotagliflozin, Canagliflozin, Dapagliflozin, Empagliflozin, Tofogliflozin, and Ipragliflozin. Sotagliflozin is an SGLT2 inhibitor with minor SGLT1 inhibitory activity . SeveralThe key targets obtained by the PPI network are 3-phosphoglyceraldehyde dehydrogenase (GAPDH), mitogen-activated protein kinase 3 (MAPK3), MAPK1, NRAS and MMP9 combined with KEGG enrichment results. GAPDH is a simple “housekeeping” protein that plays a role in a variety of physiological and pathological processes and has a variety of non-glycolytic functions in addition to its part in glycolysis . These mThe results of GO functional enrichment are primarily related to protein serine/threonine kinase activity. Protein kinases and protein phosphatases maintain the balance of protein phosphorylation regulation as two major classes of proteins, phosphorylation, and dephosphorylation, respectively. Phosphorylation of proteins occurs most commonly at serines and threonines, where protein kinases transfer phosphate groups to these amino acid residues, usually using adenosine triphosphate as a substrate, and protein phosphatases hydrolyze phosphate from the amino acid residues of the protein. They collaborate to regulate protein function, stability, and signaling, as well as carrying out physiopathological tasks . Protein2+ handling proteins, and gap junction regulation in cardiomyocytes  signaling pathway, Endocrine resistance, Fluid shear stress, and atherosclerosis, tumor necrosis factor (TNF) signaling pathway, Relaxin signaling pathway, Neurotrophin signaling pathway, Advanced glycation end-products (AGEs)- receptor for advanced glycation end-products (RAGE) signaling pathway in diabetic complications. AS is a slow-progressing inflammatory illness that is a primary underlying pathology for CVD, including MI, HF, stroke, and peripheral artery disease, which are the top causes of mortality in DM and CKD patients . DM is amyocytes . Activatmyocytes . Anothermyocytes , 114. Inmyocytes . Empaglimyocytes . Empaglimyocytes . Canaglimyocytes . MAPK simyocytes . Rap1 ismyocytes . In ratsmyocytes . In the myocytes . Rap1 hamyocytes . Rap1 caTumor necrosis factor (TNF) is a tiny molecular protein secreted by monocyte-macrophages, including both TNF-α and TNF-β, that binds to two TNF receptors (TNFR) on the cell surface. Besides, it is a cytokine with inflammatory mediator effects . Either Network pharmacology is based on a variety of databases and analysis software that can predict the most probable protein targets of SGLT2 inhibitors, thus obtaining the key links in their mechanism of action. However, the main limitation of web-based pharmacology studies is the lack of experimental validation. Pharmacological research will thus be critical in further clarifying the cardiorenal protective effects of SGLT2 inhibitors.Clinical research is focused on cardiac and renal protection, regardless of whether DM is comorbid or not. Therefore, we have constructed a network between SGLT2 inhibitors and targets related to CKD, T2DM, and HF and analyzed the potential mechanisms of SGLT2 inhibitors' cardioprotective effects through network pharmacology, which contributes to deepening the understanding of SGLT2 and provide a theoretical for future studies.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.AW: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing—original draft, and visualization. ZL and SZ: conceptualization, methodology, and writing—review and editing. FG: investigation, formal analysis, and data curation. HZ: investigation and data curation. ZZ: formal analysis and visualization. GR: writing–review and editing and supervision. XM: conceptualization, writing–review and editing, supervision, and project administration. All authors contributed to the article and approved the submitted version.This work was supported by the National Key Research and Development Program of China (No. 2018YFC1707410-02). The funder had no role in the study design, data analysis, or decision to publish.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Aspidistra letreae, against non-small cell lung cancer (NSCLC) cells was reported. However, the anticancer molecular mechanism of other Aspiletreins from A. letreae remains unknown. Using in silico network pharmacology approaches, the targets of Aspiletreins were predicted using the Swiss Target Prediction database. In addition, key mediators in NSCLC were obtained from the Genetic databases. The compound-target interacting networks were constructed using the STRING database and Cytoscape, uncovering potential targets, including STAT3, VEGFA, HSP90AA1, FGF2, and IL2. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that several pathways were highly relevant to cancer pathogenesis. Additionally, molecular docking and molecular dynamic analyses revealed the interaction between key identified targets and Aspiletreins, including hydrogen bonding and Van der Waals interaction. This study provides potential targets of Aspiletreins in NSCLC, and its approach of integrating network pharmacology, bioinformatics, and molecular docking is a powerful tool for investigating the mechanism of new drug targets on a specific disease.Lung cancer is one of the leading cancers and causes of cancer-related deaths worldwide. Due to its high prevalence and mortality rate, its clinical management remains a significant challenge. Previously, the in vitro anticancer activity of Aspiletrein A, a steroid and a saponin from  Among the common subtypes of lung cancer, non-small cell lung cancer (NSCLC) is the primary subtype, accounting for 85% of the cases2. The standard therapy for NSCLC includes surgery, radiation, chemotherapy, targeted therapy, and immunotherapy3. In the past decade, despite advances in therapeutic approaches, the number of deaths has gradually increased due to NSCLC’s ability to metastasize, acquisition of chemotherapeutic resistance, and high recurrence rate; in addition, drug efficacy is limited by significant side effects5. Particularly, a five-year survival rate of only 7% was reported for patients with lung cancer at the metastatic stage1. Therefore, continuous drug discovery for lung cancer is necessary for improving the clinical outcome.Lung cancer remains an aggressive malignancy with high mortality and incidence rate because of its aggressive characteristicsAspidistra genus, discovered in Vietnam6, contain many active components, such as saponin, coumarin, and isoflavones8, with various demonstrated pharmacological activities, including antibacterial, antifungal, antitumor, and antiviral10. A previous study reported that Aspiletreins, mainly three steroidal saponins, exhibited cytotoxicity in breast, cervical, hepatocellular, gastric, and lung cancer cells11. These three steroidal saponins contain different sugar moiety side chains located at C3, suggesting distinct potencies  have not been elucidated.Plants are a versatile source of biologically active compounds. For example, the plants of the 12. This type of approach is a powerful tool for predicting the molecular targets of new chemical entities as well as disease pathways. In addition, molecular docking analysis reveals the potential intermolecular interactions between a target and compounds13. Furthermore, pharmacokinetics and pharmacodynamics were analyzed according to chemical properties and structures using the algorithm-based method14. These comprehensive approaches can help reduce the time and cost of preclinical experiments. In this study, we explored the molecular mechanism of Aspiletreins against NSCLC using network pharmacology and computation-based approaches.On the other hand, network pharmacology-based strategies, integrating complex systems of biology and the network analysis of multiple drug targets, have been established as a new paradigm for drug discoveryThe experiment procedures are presented in Fig. The 17 intersected targets, including VEGFA, FGF1, FGF2, HPSE, CDK1, HSP90AA1, ADRA2B, DRD2, CYP2D6, LGALS3, RORC, IL2, ADRA1A, STAT3, TRPV1, SLC6A2, TRPV1, and GLRA1, were imported to the STRING database, and the potential relationships among them were investigated. The PPI network with a confidence score of 0.9 was constructed Fig. A. The STGO enrichment analysis was conducted to analyze the impact of the targets in NSCLC. The classification of GO was set for three criteria, including biological processes Fig. A, GO mol15. Here, all of the binding energy scores analyzed were less than 0, suggesting high-affinity interactions among the targets and the Aspiletreins   in response to intracellular stress27. In addition to its intracellular roles, HSP90α acts as an inflammation-stimulated, secreted extracellular factor that promotes malignant cell motility and metastasis by activating NF-kB and STAT3 transcription programs27. In the case of growth factor FGF2, it possesses broad mitogenic functions under normal conditions, especially in embryonic development and tissue repairment. In contrast, in cancer, its gene becomes overexpressed, inducing uncontrolled proliferation and the metastasis of several malignant tumors34. In addition, IL2, a multifunctional glycoprotein of the interleukin family, is a growth factor involved in the immune system, promoting the growth of natural killer cells, B-cells, and T-cells36. IL2 also aids the immune system by improving the ability of specific white blood cells to identify and destroy cancer cells37. In lung cancer tissues, low expression of IL2 was associated with poor prognosis , while the log P information (Table http://biosig.unimelb.edu.au/pkcsm/), an online pharmacokinetic prediction model.The compounds information of Aspiletrein A (AA), Aspiletrein B (AB), and Aspiletrein C (AC) were identified using the J-GLOBAL Database (on Table  was obtahttp://www.swisstargetprediction.ch/)47. The compound-target network was visualized using Cytoscape (3.9.0)48.The canonical SMILE of the compounds was uploaded into the Swiss Target Prediction database (https://www.genecards.org/), DisGeNET (https://www.disgenet.org/), OMIM (https://www.omim.org/), and TTD (http://db.idrblab.net/ttd/). The targets of NSCLC and compounds  were integrated using VENNY (https://bioinfogp.cnb.csic.es/tools/venny_old/index.html) and the intersected targets were presented in a Venn diagram.The NSCLC-related targets were retrieved by searching for “non-small cell lung cancer” in the GeneCards database (https://string-db.org/)49. The settings for building the PPI network were established in accordance with the “Homo sapiens” model, and the confidence of the interaction between the targets was set at 0.950. The network nodes represented proteins, and the edges reflected the protein–protein interactions. For the core target protein identification, the Cytoscape data was sorted by using these parameters, including degree, closeness centrality, betweenness centrality, and clustering coefficient.A PPI network was constructed by uploading the genes to the STRING v_11.0 database (51 and Kyoto Encyclopedia of Genes (KEGG)52 were analyzed by plotting the data analysis obtained from STRING 11.5 database as a scatterplot to display the relationship between two continuous variables of gene count and adjusted P-value. The R software with ggplot253 was applied to construct a bubble scatterplot of the GO  and KEGG biological pathway. The target count and significance value (P < 0.05) were mapped to size and color, respectively.Gene Ontology (GO)https://www.rcsb.org/pages/policies). The interaction between each compound with its target was predicted using PyRx Virtual Screening Tools (Version 0.8)54. All the interactions among the target proteins and compounds were constructed using PyMOL 2.5 in the PDBQT format file55. Lastly, the 2-dimensional visualization of the interaction between compounds and target proteins was investigated using Discovery Studio Visualizer 2021.The possible interaction between compounds AA, AB, and AC and the targets, including STAT3 (PDB ID: 6NJS), VEGFA (PDB ID: 4KZN), HSP90AA1 (PDB ID: 4BQG), FGF2 (PDB ID: 2FGF), and IL-2 (PDB ID: 1M48), were modeled. Each protein was retrieved from RCSB Protein Data Bank (56 was used to create topology and parameter in CHARMM36 force field (version Jul 21)57 for AA and AB ligands. The topology and parameter of STAT3 complexes were patched and generated using pfsgen (version 2.0). The complexes were solvated by TIP3 water58 in 15 Å for each direction and ionized using VMD’s solvate and autoIonize plugins (version 1.9.2)59. The simulations were performed using NAMD (version 2.14)60. The simulation system was minimized for 1000 steps the simulated for 50,000 steps, 2 fs for each step using particle mesh Ewald, Langevin dynamic simulation under 300 K and 1 atm. The rmsd of molecular dynamic simulation were calculated using VMD’s NAMD Energy and RMSD Trajectory Tool plugins (version 1.9.2)59.SwissParam web serviceSupplementary Information.Supplementary Video S1.Supplementary Video S2.


Many classic decoctions of Chinese medicine including Radix Bupleuri are used to treat major depressive disorder (MDD). Saikosaponin D is a representative bioactive ingredient discovered in Radix Bupleuri. The mechanism of saikogenin G (SGG) as a metabolite in MDD remains unclear to date. This study aims to elucidate the mechanism of SGG in treating MDD with network pharmacology.We evaluated the drug likeness of SGG with SwissADME web tool and predicted its targets using the SwissTargetPrediction and PharmMapper. MDD-related targets were identified from the following databases: DisGeNET, DrugBank, Online Mendelian Inheritance in Man, and GeneCards. The common targets of SGG and MDD were imported to the STRING11.0 database, and then a protein–protein interaction network was constructed. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment were analyzed with DAVID 6.8 database.2 <140 A2, the octanol/water partition coefficient (Consensus LogP0/W) was 4.80, the rotatable bond was 1, the hydrogen bond donors was 3, and the hydrogen bond acceptors was 4. A total of 322 targets of SGG were obtained and there were 1724 MDD-related targets. A total of 78 overlapping genes were selected as targets of MDD treatment including albumin, insulin-like growth factor I, mitogen-activated protein kinase 1, proto-oncogene tyrosine-protein kinase Src, and epidermal growth factor receptor. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that proteoglycans in cancer, pathways in cancer, prostate cancer, hypoxia-inducible factor-1, central carbon metabolism in cancer, estrogen, PI3K-Akt, ErbB, Rap1, and prolactin signaling pathways played an important role(P < .0001).The molecular weight of SGG was 472.7 g/mol, the topological polar surface area was 69.92 AThis study showed that SGG exhibits good drug-like properties and elucidated the potential mechanisms of SGG in treating MDD with regulating inflammation, energy metabolism, monoamine neurotransmitters, neuroplasticity, phosphocreatine-creatine kinase circuits, and so on. About 322 million people in the world are suffering from depression. Recurrent and long-lasting depression is characterized by continuous sadness, lack of interest, sense of guilt, poor attention, and so on. People with depression cannot work and live normally, commit nonsuicidal self-injury, or even suicide in serious cases. Between 50% and 70% of suicide victims, which was 788,000 in 2015, may be affected by MDD.,3Depressive disorder is a common mental disorder. Main subcategories of depressive disorders include major depressive disorder (MDD) and dysthymia.Citrus reticulata, Radix Bupleuri, Chuanxiong Rhizoma, Cyperi Rhizoma, Aurantii Fructus, Paeoniae Radix Alba, licorice), Xiaoyao Powder  Wolf., licorice, Zingiber officinale Roscoe), Sini Power , and Xiaochaihu decoction  have been used for over thousands of years in China to treat MDD.Many classic decoctions of Chinese medicine are used to treat MDD. Chaihu Shugan Powder (–11 Saikosaponin D as a triterpene saponin glycoside is hydrolyzed to prosaikogenin G and then to saikogenin G (SGG), which is subsequently absorbed into the circulatory system. Previous studies focused on the biological effects of saikosaponin D. However, the bioactivity and mechanism of SGG in MDD remain unclear to date.Saikosaponins  are representative bioactive ingredients discovered in Radix Bupleuri, a common component of the mentioned decoctions above. Saikosaponin D protects the nervous system, alleviates neurological dysfunction, rescues sexual behavior deficits, improves depression-like behavior, and prevents Alzheimer disease, osteoarthritis, breast cancer, and leukemia.In the present study, we aimed to elucidate the mechanism of SGG in treating MDD with network pharmacology. We evaluated the druglikeness of SGG and predicted its targets against MDD. A protein–protein interaction (PPI) network was constructed with the common targets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed with the database. This study provided a thorough investigation and comprehensive analysis of SGG in treating MDD.https://pubchem.ncbi.nlm.nih.gov/). Lipinski rule of 5 (RO5) was evaluated by the SwissADME web tool (http://www.swissadme.ch/). The topological polar surface area, LogS, gastrointestinal absorption, blood–brain barrier, Log Kp (skin permeation), and druglikeness were also analyzed.The molecular structure file and canonical SMILES of SGG were obtained from the PubChem database (http://www.swisstargetprediction.ch/index.php) and PharmMapper (http://www.lilab-ecust.cn/pharmmapper/submitfile.html) databases were conducted to predict the targets of SGG.,15The SwissTargetPrediction (https://www.disgenet.org/search), DrugBank (https://go.drugbank.com/), Online Mendelian Inheritance in Man (http://www.omim.org), and GeneCards (https://www.genecards.org/) databases by using “major depressive disorder” as a key phrase for MMD. The SGG- and MDD-related targets were sent to the UniProt database (http://www.uniprot.org/) for normalization. The intersection of identified candidate targets was evaluated by Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html). The shared targets represent the targets of SGG against MDD.A comprehensive collection of disease-related genes were obtained from the DisGeNET (https://string-db.org/). The targets of SGG against MDD were imported to the STRING11.0 database, and then target interaction information was combined and analyzed with Cytoscape software .A PPI network was predicted based on the STRING 11.0 platform (https://david.ncifcrf.gov/) for GO analysis, including the biological process (BP), cell component, and molecular function, and KEGG analysis. A heatmap was plotted by http://www.bioinformatics.com.cn, an online platform for data analysis and visualization.Target genes of SGG against MDD were mapped into the Functional Annotation Tool DAVID 6.8 database  was 4.80 <5, the rotatable bond was 1 <10, the hydrogen bond donors was 3 no more than 5, and the hydrogen bond acceptors was 4 no more than 10. Results indicated that the properties of SGG were in line with the RO5, and GI absorption was high, suggesting that it had good drug-like properties . Li et al found that inhibitor of prolyl hydroxylase (FG-4592) reverses depression-like behavior through the activation of the HIF-1 signaling pathway. Kang et al assumed that elevated levels of HIF-1 activate the phosphocreatine-creatine kinase circuits and regularize the brain energy metabolism as a result of antidepressants via normalization of structural and functional alterations. Insulin, interleukin (IL)-1β, and epidermal growth factor induce the expression of HIF-1α by activating PI3K-Akt signaling pathways. In addition, a previous report revealed that the overexpression of HIF-1α upregulates the expression of inflammatory factors  by activating the epidermal growth factor receptor/PI3K/AKT pathway.Hypoxia-inducible factor-1 is an important transcriptional factor responding to hypoxia, a heterodimer composed of HIF-1α (regulatory subunit) and HIF-1β (constitutive subunit). HIF-1 signaling pathways regulate various BPs, including aging, proliferation and apoptosis, anaerobic metabolism, TCA cycle metabolism, oxygen consumption, inflammation, and angiogenesis.–31 Cao et al reported that icariin significantly ameliorates the seeming symptoms in rats with perimenopausal depression depending on the regulation of PI3K-Akt signaling. Insulin-like growth factor 1, creatine, schisandrin, and liquiritigenin exert an antidepressant effect on animals with depression induced by chronic stress or corticosterone through the PI3K-Akt pathway.–32PI3K-Akt signaling plays an important role in many diseases, including cancer, diabetes, obesity, cardiovascular diseases, and neurological diseases .,34 Depression has been associated with increased activity of the immune system and the release of proinflammatory cytokines , which are associated with hypothalamic–pituitary–adrenal axis abnormalities in clinical and translational studies.,36 Moreover, recent studies have focused on neuroimmune processes because they may involve the pathophysiology of MDD directly and indirectly through some neurobiological processes related to neuroplasticity-related systems, monoamine system, and the hypothalamic–pituitary–adrenal axis.Akt (a target of PI3K) acts as a pivot position in the treatment of depression through connecting with downstream targets, including glycogen synthase kinase-3 beta (GSK3β), mammalian target of rapamycin, IκB kinaseα, γ-aminobutyric acid A receptor, and forkhead box O, which are involved in the regulation of glucose and lipid metabolism, synaptic plasticity, angiogenesis, proliferation and apoptosis, synaptic signaling, and mitochondrial function. Moreover, PI3K and AKT activate immune cells by regulating key inflammatory cytokines  and are related to the action of dopamine and serotonin. Extracellular factors serve many functions, such as regulating the PI3K/AKT pathway and then inhibiting GSK3β by phosphorylation of Ser9. Studies using animal models have indicated that inhibiting the activation of Akt-GSK3β signaling impairs the survival and synaptic plasticity of hippocampal neurons and induces depressive behavior. Further study found that resveratrol may show antidepressant effect by activating the Akt/GSK3β signaling pathway. Phosphatase-mediated phosphorylation plays an important role in the processes of most signaling pathways mentioned above. These situations are consistent with our results that the enriched BP terms are protein autophosphorylation, phosphatidylinositol-mediated signaling, and peptidyl-tyrosine phosphorylation.GSK3β as a downstream target of AKT is crucial for the function of the central nervous system in humans. Phosphorylation on Tyr216 and Ser9 of GSK3β represents positive and negative activities, respectively.–42 Four closely related ErbB subtypes, namely, ErbB1, ErbB2, ErbB3, and ErbB4, have been identified in humans. Several researchers have reported that NRG1/ErbB signaling plays an important role in the modulation of the midbrain DA system.,43–47 A recent study has confirmed that ketamine exhibits antidepressant effects in adult mice depending on NRG1/ErbB4 signaling. In addition, ErbBs can activate the PI3K pathway directly or indirectly.The deficiency of central monoamine neurotransmitters dopamine (DA) and serotonin is closely related to depression. At present, selective serotonin reuptake inhibitors, such as fluoxetine, paroxetine, sertraline, fluvoxamine, and citalopram, are widely used in the treatment of depression. Previous studies have shown that the ErbB signaling pathway plays a critical role in depression. Estrogen, including estrone, estradiol, estriol, and estetrol, is a steroid hormone. Estrogen is involved in various physiological processes, including breast and sexual organ development, menstrual cycle, reproduction, cardiovascular protection, bone integrity, cognitive, neuroprotective effects, and antiinflammatory effects, by interacting with estrogen receptors.,49 Women show a higher incidence and prevalence of MDD than men. The occurrence of depression increases during the menopause and postpartum periods when the level of estrogen decreases significantly. Several studies have demonstrated that estrogen performs favorable effects in MDD via modulating monoamine neurotransmitters (DA and 5-hydroxytryptamine), inflammatory processes, and dendritic spine number.,51 The activation of ERK and PI3K elevates after acute treatment with 17β-estradiol. Estrogen is an important adjunct to antidepressant drug treatments clinically.Tadeus Reichstein, Adolf Butenandt, and Edward Adelbert Doisy were the first to isolate and characterize estrogen in 1929.The HIF-1, Estrogen, PI3K-Akt, and ErbB signaling pathways are interrelated and interact with each other. We identified key proteins targeted by SGG in the pathways and GO enrichment above and show the related biomechanism in Figure This study elucidated the potential mechanisms of SGG in treating MDD. Bioinformatics analysis showed that SGG exhibits good drug-like properties, with 78 genes selected as targets of SGG in MDD treatment. Our study also suggested that HIF-1, estrogen, PI3K-Akt, and ErbB signaling pathways regulate inflammation, energy metabolism, dendritic spine number, phosphocreatine-creatine kinase circuits, and BPs in the treatment. This study provided valuable information to investigate the comprehensive and underground effect of SGG in MDD treatment.Conceptualization: Lili Hu, Jue Wang, Xiaoge Zhao.Data curation: Lili Hu.Formal analysis: Lili Hu, Jue Wang, Donghui Cai.Funding acquisition: Lili Hu, Jue Wang.Investigation: Lili Hu, Xiaoge Zhao.Methodology: Lili Hu, Jue Wang, Donghui Cai.Project administration: Lili Hu, Donghui Cai.Resources: Lili Hu, Jue Wang, Donghui Cai.Software: Lili Hu.Supervision: Lili Hu.Validation: Lili Hu, Xiaoge Zhao.Visualization: Lili Hu, Xiaoge Zhao.Writing – original draft: Lili Hu.Writing – review & editing: Lili Hu, Donghui Cai.


Aspergillus, including Aspergillus flavus, an important ear rot pathogen in maize. Most commercial corn hybrids are susceptible to infection by A. flavus, and aflatoxin contaminated grain causes economic damage to farmers. The creation of inbred lines resistant to Aspergillus fungal infection or the accumulation of aflatoxins would be aided by knowing the pertinent alleles and metabolites associated with resistance in corn lines. Multiple Quantitative Trait Loci (QTL) and association mapping studies have uncovered several dozen potential genes, but each with a small effect on resistance. Metabolic pathway analysis, using the Pathway Association Study Tool (PAST), was performed on aflatoxin accumulation resistance using data from four Genome-wide Association Studies (GWAS). The present research compares the outputs of these pathway analyses and seeks common metabolic mechanisms underlying each. Genes, pathways, metabolites, and mechanisms highlighted here can contribute to improving phenotypic selection of resistant lines via measurement of more specific and highly heritable resistance-related traits and genetic gain via marker assisted or genomic selection with multiple SNPs linked to resistance-related pathways.Aflatoxins are carcinogenic secondary metabolites produced by several species of  Aspergillus fungi. Although aflatoxin production has been reported in as many as 17 Aspergillus species [A. flavus and A. parasiticus [Aspergillus flavus and A. parasiticus can colonize and contaminate almost any crop during storage, but they are also pre-harvest pathogens of oilseed crops, including maize [A. flavus, occurs worldwide. [A. flavus behave differently on different growth substrates, including growth rates, timing of reproduction, and level of aflatoxin production. These differences suggest that the production of primary and secondary metabolites depends on the food source of the fungus, and may hint at underlying cellular processes, in both the fungus and the infected plant [Aflatoxins are carcinogenic secondary metabolites secreted by some species of asiticus ,3. Asperng maize ,5. Aflatrldwide. ,7. Most rldwide. ,9,10,11.ed plant ,13,14,15A. flavus infection, growth, and aflatoxin production in particular [In maize, host-plant resistance to aflatoxin accumulation is a quantitative trait with relatively low heritability that interacts significantly with environmental effects . Susceptrticular . A few srticular ,18,19 anrticular ,21.A. flavus [Association mapping  is a powerful tool to map chromosomal regions associated with traits of interest, and to identify natural variation and genes related to pathogenicity in animals ,23, huma. flavus ,21. One . flavus ,28.Another way to obtain information about the genetic component of resistance to aflatoxin accumulation is through analysis of metabolic pathways. These studies focus on the combined effects of many genes that are grouped according to their shared biological function ,30. AnalA. flavus spores [A. flavus infection and aflatoxin production in maize also point to important roles for some proteins, genes, and metabolites. Genetic and association mapping found genes that produce enzymes that break down chitin to be linked to reduced aflatoxin in maize; these enzymes can defend against fungi, as chitin is found in the cell walls of fungi, yeast, and algae, as well as the exoskeleton of insects [A. flavus resistant maize lines [A. flavus infection and aflatoxin accumulation include a trypsin inhibitor [The first use of the PAST protocol identified metabolic pathways in maize plants responding to inoculation with s spores  and obses spores ,41 and rs spores . Variouss spores ,44 may bs spores . Studies insects . The proze lines ). Other nhibitor , α-amylanhibitor , a pathonhibitor , and lipnhibitor ,52,53.Considering the immense global health burden and economic importance of damage caused by aflatoxin, studying this trait with tools that provide an accurate mapping of related, small-effect genes that in combination provide a resistance phenotype is essential. Knowledge gained from such work will facilitate marker-assisted selection and allow the creation of an ideotype of a resistant plant, which breeders could incorporate into genomic selection or phenotypic selection programs. Linking GWAS with the study of metabolic pathways can provide more information on genetic mechanisms and the biological relevance of important traits ,54. Impop level The 197 pathways associated with increasing and decreasing expression of the traits in the four studies at a A. flavus in maize. They are described below according to their annotated functions, and a synthesis of the pathways fond within each function can be found in Just over half of the associated pathways (~100 of 193) were involved in a limited number of plant functions  class of steroid hormones that regulate plant development and physiology. Brassinosteroids and salicylic acid signaling pathways and genes mediate local and systemic activation of defenses following fungal infection of maize silks  and seedA. flavus.Terpenes and terpenoids are the more commonly studied phytoalexins, which are small secondary metabolites that are induced following attack by multiple pathogenic organisms including insects and fungi ,44. AnotFusarium verticillioides and F. graminearum, as well as other antifungal activity compounds [A. flavus infection flavonoids modulated toxin accumulation [Fifteen pathways associated with the production of phenylpropanoids, including phenolics and anthocyanins, were found in both the increasing and decreasing analyses. Phenylpropanoids make lignin and cell walls, and many phenolic acids are components of the cell wall. Following infection, decreases in soluble phenylpropanoids occur with an increase in cell wall bound pheylpropanoid metabolites . Gembeh ompounds . Flavonoompounds . Increasmulation .Fusarium verticillioides down-regulated pathways for cellulose production while up-regulating genes involved in reinforcement of the cell walls in a previous study [Eighteen pathways involved in the production of other cell wall components were also prevalent in the pathway analyses. Specifically, 9 pathways involved in the production of cellulose, hemicellulose, lignin, glycoprotein and suberin, were identified. Induced cell wall deposition of phenolics increase resistance to fungal penetration , and so us study . The proNine pathways involved in the production of cell membranes, especially choline and sphingolipid biosynthesis were associated with increased and decreased resistance in the pathway analyses. Cell membranes are known to function in plant defense because they carry sets of pattern-recognition receptors (PRRs) that detect cellular components that do not come from the plant itself, often shed by or part of invading pathogens. These PRRs recognize microbe-associated molecular patterns (MAMPs) and activate pattern-triggered immunity (PTI) when they are triggered. However, these PRRs are not the cell membrane itself, and not the subject of the pathways identified in our study. Choline is a cell membrane associated metabolite necessary for plant development and is an antioxidant in many plant tissues . It is kMany compounds are produced specifically to defend against fungi. In our analysis, 18 defense compound pathways were identified. In addition to the phenolics and polyphenols which form part of the cell wall, compounds identified in the current study are created by 7 pathways relating to hydroxycinnamic acids, including chlorogenic acid. In maize, ferulic acid and p-coumaric acid are the most abundant in the hydroxycinnamic complex ,73, and Lipoxygenases are an important molecular family distributed in plants and can be classified into two large subfamilies of 9-lipoxygenase (9-LOX) and 13-lipoxygenase (13-LOX) . IncreasA. flavus), than can weak or degrading plants and tissues. A. flavus is a decomposing fungus, and rarely causes extensive damage to an actively growing plant. There is also evidence that aflatoxin production increases after a plant experiences stress [Several other pathways were identified in analysis of the four datasets with PAST. Six pathways were identified that directly or indirectly synthesize coenzyme A, and 12 others synthesized vitamins . Many of the remaining 92 pathways identified in this analysis and listed in s stress  and thusThese results can guide research to identify phenotyping targets useful for aflatoxin resistance. Changing the levels of production of plant hormones, plant signaling, and probably pathogen recognition and membrane function are somewhat obvious but may be hard to implement as resistance mechanisms because they are very complicated responses and interfere with each other. However, specific defense compounds,  may be specifically measured and selected for in future programs to increase resistance to aflatoxin accumulation in maize. Stronger cell walls including increased phenolic compounds will almost certainly lead to reduced fungal infection and reduced aflatoxin levels, but negative nutritional properties of such mechanisms may need to be investigated. In addition to identifying possible useful targets of plant improvement for aflatoxin reduction, it is apparent in this study that PAST analysis finds pathways in common between independent GWAS studies, which may help to illuminate common genetic mechanisms contributing to a trait of interest in different populations of the same species.A. flavus (from [A. flavus and scored for Aspergillus ear rot (AER). The lines were also genotyped by sequencing and presented in Bengosi Bertagna et al. [A. flavus; data were presented in Suwarno et al., (in review).Four GWAS studies were included in the comparative analysis of aflatoxin accumulation resistance in maize. One study of 282 temperate and semi-tropical inbred maize lines was characterized by the CHPRRU twice independently via genotyping by sequencing (GBS), leading to two separate genotypic data sets  and Tabl. The samus (from ) were usa et al. . A fourthttps://maizegdb.org/past; accessed on 27 June 2022). The data used in PAST included the following: SNP-trait association values for significance (p); correlation (R2 or proportion of the phenotypic variation accounted for); effect values along with the calculated LD values for D’ and R2; and p between each marker SNP and its closest neighboring SNPs (50 upstream and 50 downstream). The association and effects files were uploaded without filtering for significance.The output of Mixed Linear Model (MLM) GWAS from the four previously analyzed studies were obtained and run through the Pathway Analysis Study Tool  as per Tp value, and shortest distance to all other SNPs in the linkage block. Choosing the tagSNP with the greatest effects on the trait under analysis reduces dimensionality of the dataset. The gene(s) causing the SNP-trait association was then assumed to be within 1 Kb of the SNP. When a gene was located, the SNP-trait association effect value was transferred to the gene and used for the gene-set enrichment analysis as per Tang et al. [2 of 0.8 to determine linkage between SNPs.SNPs were then assigned to genes by seeking a tagSNP for each block of linked SNPs as outlined in Tang et al. . Brieflyg et al. . SNPs wehttps://maizegdb.org/metabolic_pathways; accessed on 27 June 2022) used by PAST online, were considered a perfect match. In addition, pathways that lead to the same end product were also considered a match, as there are often two or three pathways  that can synthesize the same compound in the same organism. Finally, pathways leading to related compounds or biological functions were also noted and discussed.Pathways associated with both increasing and decreasing expression of the trait (levels of aflatoxin) were identified. Only pathways with at least 5 annotated genes were analyzed to avoid small sample size effects. Significance of association between pathways and the phenotypes was determined by creating 1000 random distributions of gene effects and comparing the actual effects to randomly sampled effects. The outputs , were compared to find pathways in common between them. Pathways with the same name or pathway ID in the CornCyc database (


Mucopolysaccharidoses (MPS) are genetic metabolic diseases characterized by defects in the activity of lysosomal hydrolases. In MPS, secondary cell disturbance affects pathways related to cardiovascular disorders. Hence, the study aimed to identify MPS-related drugs targeting cardiovascular disease and select a list of drugs for repositioning. We obtained a list of differentially expressed genes and pathways. To identify drug perturbation-driven gene expression and drug pathways interactions, we used the CMAP and LINCS databases. For molecular docking, we used the DockThor web server. Our results suggest that pirfenidone and colchicine are promising drugs to treat cardiovascular disease in MPS patients. We also provide a brief description of good practices for the repositioning analysis. Furthermore, the list of drugs and related MPS-enriched genes could be helpful to new treatments and considered for pathophysiological studies. Mucopolysaccharidoses (MPS) are a group of rare genetic metabolic diseases characterized by defects in the activity of lysosomal hydrolases that degrade glycosaminoglycans (GAGs), such as heparan and dermatan sulfate. Patients with MPS present a wide range of symptoms, characterizing a disease with progressive multisystemic involvement. Lysosomes are essential mediators of various cell processes, so multiple pathways are deranged in MPS patients. In addition, immune function may be impaired and directly impact disease pathogenesis  [Cardiovascular involvement is among the main features of MPS disorders, being a significant cause of morbidity and mortality. The range of manifestations includes progressive thickening and compromised function of the heart valves, conduction abnormalities, left ventricular hypertrophy, and diffuse coronary artery stenosis  , https://www.ncbi.nlm.nih.gov/geo/) (accessed on 5 October 2022) with the accession number GSE30657 . We performed the gene expression analysis with the R package limma v.3.44.3  [https://www.ufrgs.br/mpsbase/) (accessed on 5 October 2022), and in Transcriptome datasets were retrieved from the Gene Expression Omnibus database   . More in., 2015) , MPSBasep-value using a hypergeometric distribution. Multiple test correction was also implemented by applying the FDR algorithm at a significance level of p < 0.05. Gene set annotations were obtained from the Kyoto Encyclopedia of Genes and Genomes database.We performed the pathway analysis using the R package pathfindR  , identifhttps://clue.io/about) (accessed on 5 October 2022), a cloud-based software platform for the analysis of perturbational datasets generated using gene expression (L1000) and proteomic (P100 and GCP) assays. In the CLUE platform, we used the repurposing app Touchstone to analyze datasets of chemical compounds and genetic perturbagenes where there are clinical studies that have reported gene expression signatures in cell lines. The Touchstone data provide a benchmark to assess connectivity among drugs and genes. To examine pathway drug interactions, we used the repurposing app to identify the drugs related to the pathways found in our enrichment results. The repurposing app provides information about 5000 compounds and drugs for drug discovery. We also used the Library of Integrated Network-based Cellular Signatures  (accessed on 5 October 2022) to look for compounds that could modulate the activity of DEG in MPS.To identify drug perturbation-driven gene expression and drug pathways interactions, we used the CMAP database   and CLUEThe protein model was constructed using the SWISS-MODEL homology modeling web server, with the respective amino acid sequence as reference  . The proTo assess the homology model structure’s quality, we have performed a Ramachandran plot with the Ramachandran plot server tool  . Accordihttps://www.dockthor.lncc.br/ (accessed on 5 October 2022). We used the first-rank RMSD, the program HADDOCK, and the Van der Waals energy scores to evaluate the best approach.To understand the molecular basis of the interactions between proteins and ligands, we used the DockThor web server v.2  ,25. The Drug repositioning starts with the identification of therapeutic targets. In the case of rare complex diseases, using high-throughput expression data may provide new targets. Here, a dataset from the MPS VII mouse model aorta (GSE30657) was used for two comparisons: MPS VII dilated aorta vs. WT control and MPS VII dilated aorta vs. MPS VII non-dilated aorta. The gene expression analysis results are summarized in Next, we searched which pathways were involved using the KEGG database   protein for molecular docking due to its biochemical properties ,28 Jans. The obtIn order to validate the docking score results, we replicated the docking workflow with six other IRAK4 inhibitors with reported Ki or IC50 values. The results are shown in MPS patients suffer from different cardiac problems, mainly fibrosis of the conduction system with GAG infiltration  , valve sTLR4) gene, the activation of interleukin-1 receptor-associated kinase 4 (IRAK4), in addition to numerous cathepsin proteases and matrix metalloproteinases were observed in MPS cardiovascular disease  [GAG overaccumulation has been associated with the release of various pro-inflammatory immune mediators and autophagy dysfunction. Furthermore, oxidative stress, abnormal mitochondrial function, disruption in ion homeostasis, and overexpression of lysosomal, and proteasomal-related genes also play a role in MPS disease  . In addi., 2020) . One study suggested that heparan sulfate–TLR4-mediated monocyte/macrophage-induced inflammation contributes to the pathogenesis of cardiovascular disease in MPS I  . FurtherFrom the results obtained by the drug-pathway interactions and the IRAK4 molecular docking, we showed some possible candidate drugs for MPS disorders with cardiovascular involvement, such as nintedanib, vandetanib, and gefitinib. They are potent and selective intracellular inhibitors of PDGFR, FGFR, VEGFR, EGFR, and RET tyrosine kinases. These small molecules have shown consistent antifibrotic, antiangiogenic, and anti-inflammatory activity in animal models  . HoweverOther pathways enriched in the MPS VII aorta involved cell apoptosis, and several caspase genes were DEG. Gradual cardiomyocyte apoptosis occurs in failing hearts, leading to the progressive loss of cardiomyocytes and lethal heart failure  . AngioteDefective lysosomes lead to abnormal autophagy, inflammation, and reduced infection control, as occurs in MPS disorders  . The inhMPS patients suffer from fibrosis of the conduction system with GAG infiltration  , valve lFor a successful targeted cardiovascular delivery of therapeutic agents, several barriers must be overcome, including the anatomical difficulties of access, the mechanical force of the blood flow, and the endothelial and cellular barriers  (Li et aAs for other orphan diseases, the need for more efficient adjuvant therapies for MPS can benefit from the pipeline used in this study, shown in


Abrus precatorius L. is a slender, perennial, deciduous, and woody twining plant used in various regions of Asia to treat a variety of ailments, including diabetes mellitus. Various in vitro studies revealed the therapeutic significance of A. precatorius against diabetes. However, the exact molecular mechanism remains unclarified. In the present study, a network pharmacology technique was employed to uncover the active ingredients, their potential targets, and signaling pathways in A. precatorius for the treatment of T2DM. In the framework of this study, we explored the active ingredient–target–pathway network and figured out that abrectorin, abrusin, abrisapogenol J, sophoradiol, cholanoic acid, precatorine, and cycloartenol decisively contributed to the development of T2DM by affecting AKT1, MAPK3, TNFalpha, and MAPK1 genes. Later, molecular docking was employed to validate the successful activity of the active compounds against potential targets. Lastly, we conclude that four highly active constituents, namely, abrusin, abrisapogenol J, precatorine, and cycloartenol, help in improving the body’s sensitivity to insulin and regulate the expression of AKT1, MAPK3, TNFalpha, and MAPK1, which may act as potential therapeutic targets of T2DM. Integrated network pharmacology and docking analysis revealed that A. precatorius exerted a promising preventive effect on T2DM by acting on diabetes-associated signaling pathways. This provides a basis to understand the mechanism of the anti-diabetes activity of A. precatorius.Type 2 diabetes mellitus (T2DM) is a notable health care load that imposes a serious impact on the quality of life of patients. The small amount of reported data and multiple spectra of pathophysiological mechanisms of T2DM make it a challenging task and serious economic burden in health care management.  Type 2 diabetes mellitus (T2DM) is the most common form of diabetes mellitus and is a rapidly growing global problem . T2DM isAbrus precatorius L. is a herbaceous plant of fabaceae, which flowers in winters. The latest pharmacological studies suggest that A. precatorius possesses antidiabetic, antimicrobial, anticancer, and anti-inflammatory properties due to the presence of natural bioactive compounds [A. precatorius [A. precatorius is a root and distinctive source of numerous promising phytochemicals, making it a valuable and adaptable plant with a wide range of therapeutic qualities.ompounds ,9. Alkalcatorius ,9,10,11.Network pharmacology (NP) is a rising star in the field of drug discovery, as it integrates systematic medicine with information science . It is aA. precatorius. To the best of our knowledge, this is the first study that integrated bioinformatics analysis with NP to explore the mechanism of A. precatorius for T2DM treatment. This study gives a new and novel insight in terms of understanding the molecular mechanism of the anti-diabetic activity of A. precatorius and expedites the process of drug discovery. Moreover, this breakthrough has sparked a new interest in the search for candidate drugs from A. precatorius. In the framework of this study, bioactive compounds of A. precatorius, as well as the putative mechanism underlying its anti-T2DM effect, were analyzed by employing a network pharmacology approach along with molecular-docking analysis. Moreover, laboratory experiments are recommended to explore the substance’s pharmacological potential in the near future.The use of bioactive compounds to reform medicines in the future is exciting, and prospects for curing multiple diseases are encouraging. Recently, Li et al.  employedA. precatorius were selected and considered as key targets.After searching, filtering, and removal of the duplicates, 11 putative components  with F ≥ 30% and DL ≥ 0.18 were selected . F30 meaA. precatorius. Further, 11 active compounds, 397 key targets, and their associated pathways with a maximum number of genes were chosen for the construction of an ‘active compound–targeted genes–connected pathway’ network diagram. Each of these active compounds corresponded to multiple targets. This is a strong indication that many targets may induce a synergistic effect when A. precatorius serves as an anti-type 2 diabetic agent. The degree of these 11 compounds in the compound–targeted genes–connected pathways network was then evaluated , insulin resistance (22), type II diabetes mellitus (16), and pathways in cancer (55). Finally, KEGG pathway analysis revealed that AKT1, MAPK3, TNFalpha, and MAPK1 were significantly enriched genes , MAPK3 (PDB id: 2ZOQ), TNFalpha (PDB id: 2AZ5), and AKT1 (PDB id: 3QKK)) were retrieved from PDB. Structural refinement was completed by using the ucsf chimera tool. Energy minimization was completed at 1000 decent steps, while the non-standard residues were also removed from the receptors of the protein to avoid clashes and incorrect configurations. Molecular docking was performed to screen out the putative targets of constituents with the ability to lower the risk of T2DM. Docking analysis successfully predicted the strong binding affinity between constituents and the binding pockets of four target proteins. Docking scores, along with binding energy, were employed as key criteria for compound screening . Clusterlidomide , Minocyclidomide , Resveralidomide , and Ulilidomide  were ideADMET analysis is a challenging process in drug discovery. This is achieved through SwissADME database and showed that selected compounds have good pharmacokinetic properties. ADMET profiling of all those top selected drug candidates shows that there is no side effect of pharmacokinetic properties of all potential compounds . The assType 2 diabetes mellitus is a multifactorial chronic metabolic condition characterized by relative insulin deficiency, hyperglycemia, and insulin resistance . All aroMedicinal plants are considered a natural pool and an infinite source of medicinal agents due to the presence of naturally occurring compounds ,25. NatuA. precatorius is a medicinal herb abundantly found across Afro-Asian regions of the world. This plant has therapeutic properties used to treat a variety of ailments. Various parts of the plant, such as seeds, roots, and leaves, are utilized for a variety of medical purposes. Flavonoids, glycosides, triterpenoids, abrin, and alkaloids are the main components of A. precatorius [A. precatorius in the treatment and management of diabetes mellitus has been emphasized in various reports [A. precatorius revealed efficacy in treating breast cancer [A. precatorius as well as a new therapeutic concept for further exploration on mechanisms of A. precatorius for T2DM treatment. In our line of work, screening results represented that triterpenoids, flavonoids, steroids, and alkaloids were the main bioactive compound of A. precatorius, which played a decisive role in the development of T2DM by affecting AKT1, MAPK3, TNFalpha, and MAPK1 genes. Furthermore, molecular docking also confirmed our findings that stable binding forces exist between core compounds and key targets. By constructing a model of ‘herb-active compounds–targets–pathways’, we discovered that abrectorin, abrusin, abrisapogenol J, sophoradiol, cholanoic acid, precatorine, and cycloartenol had a strong association in the network, indicating that it has anti-diabetic properties. Moreover, molecular docking also strengthened our findings by successfully validating the interaction that exists between highly active constituents and their putative targets. Finally, the associated ADMET properties of potential compounds for different models, such as P-glycoprotein substrates, BBB penetration, and gastrointestinal absorption, showed positive results that strongly support the compounds’ suitability as drug candidates.catorius ,28,29. T reports ,31. Of nt cancer  and diabt cancer . This stA. precatorius were mainly involved in insulin receptor substrate binding, protein autophosphorylation, insulin receptor substrate binding, and the regulation of insulin secretion. KEGG pathway studies revealed that targets were concentrated in diabetes-related pathways. In addition to being enriched in diabetes-related pathways, anti-diabetic targets were also involved in other pathways that are intimately tied with T2DM, such as neuroactive ligand–receptor interaction, the AMPK signaling pathway, the PI3K-Akt signaling pathway, and pathways in cancer.According to GO functional analysis, the anti-diabetic targets of In the current analysis, we discovered many target genes participating in various metabolic pathways. By targeting the genes that cause disturbance in metabolic pathways, the pathophysiology of disease can be halted. A few of studies strengthened our findings, such as one that showed that most people suffering from T2DM are insulin resistant just because of glucose toxicity . It is nA. precatorius to validate their medicinal usages.According to the ‘compounds–targets network’, we screened seven compounds and four genes for a docking experiment. Furthermore, docking results verified our findings and showed that abrisapogenol J, abrusin, cycloartenol, and precatorine bind stably with the active pockets of target genes, which spotlight that these compounds can be considered for the treatment of T2DM by inhibiting AKT1, MAPK3, TNFalpha, and MAPK1 genes. In the light of network pharmacology, the current study elaborates on the active compounds, their potential targets, and associated pathways to treat T2DM, hence providing a theoretical basis for additional experimental research. Bearing in mind the limitations of network pharmacology, the basic pharmacological mechanisms for the treatment of T2DM is only achieved by mining data. Network pharmacology presently relies on different databases for bioactive mining. Databases, though curated, may show discrepancies due to numerous sources of information and experimental data. A way to navigate around this problem is to make use of modern, high-throughput chemical identification techniques such as ultra-performance liquid chromatography–electrospray mass spectroscopy . DespiteA. precatorius was retrieved from literature as well as database of biologically active phytochemicals  [A. precatorius. ‘Abrus precatorius’ was used as a keyword in databases, while a literature search was carried out on Pubmed and Google Scholar. All ingredients of A. precatorius were virtually screened by applying bioavailability (F) and drug-likeness (DL) parameters, which are crucial in the characteristics of absorption, distribution, metabolism, and excretion (ADME) characteristics of drugs. Ingredients were only retained if DL ≥ 0.18 and F ≥ 30% to satisfy ADME criteria. In this regard, F30% and DL of all active compounds were calculated using SwissADME and ADMETlab. Meanwhile, chemical information  of screened compounds was also collected from PubChem and ChemSpider.Information on active constituents of (IMPPAT) , traditi(IMPPAT) , and KNA(IMPPAT) ) were usA. precatorius were predicted using Swiss Target Prediction [Putative targets of the selected compounds of ediction  and STITediction . ScreeneA. precatorius compounds and T2DM-related targets were intersected, and Venn plot was drawn to extract the common targets for subsequent analysis.Prediction of disease-related genes is the next preliminary step to explore the molecular mechanism of medicinal herbs to treat multiple diseases and disorders. Two databases, GeneCard  and Onlip-value  ≤  0.05 was selected, and top 10 GO enrichment and top 10 KEGG pathways with higher counts were chosen for further analysis.Database for annotation, visualization, and integrated discovery (DAVID) was hired to perform functional annotation and enrichment analysis . The keyA. precatorius in T2DM. The network was constructed and visualized by Cytoscape 3.8.0, which is a freely available graphical user interface for importing, visually exploring, and analyzing biomolecular interaction networks. Nodes were used to represent the active constituents and target genes in the network, while interactions between active constituents and their target genes were indicated by edges. Network analyzer tool was used to calculate degree, a topological property that reveals the importance of compound/target gene/pathways in network diagram. Further, target genes with the highest degree of connectivity were considered as ‘key targets’.Network analysis was performed to understand the mechanism of Protein–protein interactions (PPI) are highly significant by virtue of having high versatility, adaptability, and specificity ,46,47. SFurthermore, key targets were validated through molecular docking approach. The RCSB Protein Data Bank was used to obtain the X-ray crystal structures of candidate targets . PDB is SWISS ADME online server was used to check the physiochemical properties of compounds, including absorption, distribution, metabolism, excretion, and toxicity . All thoA. precatorius for the treatment of T2DM. Further, our findings propose that AKT1, MAPK3, TNFalpha, and MAPK1 genes are promising and viable therapeutic targets to reduce the incidence of T2DM, thereby exerting potential therapeutic effects in T2DM. However, this study also has certain limitations, as pharmacological and clinical research still need to further validate our findings. This approach introduces a groundwork for further research on the protective mechanisms of A. precatorius for T2DM and applications of network pharmacology in drug discovery.This research establishes the latest scientific foundation for determining the efficacy of multi-component, multi-target compound formulas, together with exploring more therapeutic targets for T2DM. In this study, we incorporated a network pharmacology approach with molecular docking in order to uncover the molecular mechanisms of


Nonalcoholic fatty liver disease (NAFLD) is considered the most prevalent chronic liver disease, but the understanding of the mechanism of NAFLD is still limited. The aim of our study was to explore hub lncRNAs and mRNAs and pathological processes in high-fat diet (HFD)-induced and lycopene-intervened liver steatosis. We analyzed the gene profiles in the GSE146627 dataset from the Gene Expression Omnibus (GEO) database to identify differentially expressed lncRNAs and mRNAs, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized for functional enrichment analysis. We found that the turquoise, blue, brown, yellow, green, and black modules were significantly correlated with NAFLD. Functional enrichment analysis revealed that some hub lncRNAs  might be involved in the regulation of the inflammatory and metabolic pathways  in NAFLD. The establishment of an NAFLD mouse model confirmed that lycopene supply attenuated hepatic steatosis in HFD-induced NAFLD. Our analysis revealed that the inflammatory and metabolic pathways may be crucially involved in the pathogenesis of NAFLD, and hub lncRNAs provide novel biomarkers, therapeutic ideas, and targets for NAFLD. Moreover, lycopene has the potential to be a phytochemical for the prevention of HFD-induced liver steatosis. Nonalcoholic fatty liver disease (NAFLD) is considered the most prevalent chronic liver disease and is one of the leading causes of death worldwide, with the number of cases increasing ,2. HepatLycopene is a carotenoid widely found in red, pink, and orange fruits and vegetables, such as tomatoes, papaya, and grapes . LycopenIncreasing numbers of studies are revealing that long noncoding RNAs (lncRNAs) play important biological roles in various types of diseases ,13, inclWeighted gene coexpression network analysis (WGCNA) is a new algorithm for analyzing modules from gene expression profiling data . WGCNA dhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146627 (accessed on 20 April 2022)). GSE146627 contained nine liver samples: three fed a normal diet, three fed a HFD, and three fed HFD mixed with lycopene. The fastq raw data were subjected to mapping with grcm38 reference genomes by using the HISAT2 and StringTie packages [p < 0.05, fold change > 2, and false discovery rate < 0.05. The workflow of overall study design is shown in The expression profiling of GSE146627, which is based on the GPL21273 platform (Illumina HiSeq X Ten (Mus musculus), Vazyme Biotech Co., Ltd, Nanjing, Jiangsu, China), was downloaded from the GEO database . Thus, the significant functions and signaling pathways of the mRNAs were identified.The biological process, molecular function, and cellular component information of the mRNA modules were analyzed based on the Gene Ontology (GO) database. Based on the gene signaling pathway annotation information of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we performed the signaling pathway annotation of the mRNA in the modules. Fisher’s exact test and the multiple comparison test were performed to examine the significance level ) [The PPI network analysis was performed with an online tool ) . In the The procedure for the animal experiments was approved by the Ethics Committee on the Use of Animals of Southeast University (protocol code 20220225025). We randomly assigned 30 C57BL/6 male mice (5 weeks old) to the control, HFD, or HFD + lycopene group. The HFD group was fed a HFD ; the HFD + lycopene group was fed a HFD mixed with lycopene (100 mg/kg·d); The control group was fed a normal diet . All mice were housed in a 12 h light/dark environment and had free access to food and water without fasting. They were sacrificed by cervical dislocation after 10 weeks of feeding, and liver tissues were immediately isolated. Each liver tissue was divided into two parts: one part was frozen in liquid nitrogen and stored at −80 °C; the other part was fixed with 4% paraformaldehyde.The livers fixed with 4% paraformaldehyde were dehydrated, embedded in paraffin, sliced, stained with hematoxylin–eosin (HE), and observed under a microscope and quantified with multispectroscopy.http://www.r-project.org/ (accessed on 1 February 2023)) and SPSS Version 21.0 . The results are presented as mean ± SD. Student’s t-test and one-way analysis of variance (ANOVA) were used to determine differences. p < 0.05 was considered significant.Statistical analysis was performed with R software Version 4.1.1 , while the black module was most positively correlated with HFD . Based on the findings in The relationship between the coexpression modules and dietary interventions is shown in We performed enrichment analysis of the GO terms and KEGG pathways to determine whether these modules consist of functionally similar genes as well as to understand the functional significance of the network modules. The mRNAs in the turquoise, blue, brown, yellow, green, and black modules were subjected to GO and KEGG pathway enrichment analyses. The results showed that the regulation of transcription by RNA polymerase II, lipid metabolic process, apoptotic process, fatty acid metabolic process, steroid biosynthetic process, phosphorylation, RNA splicing, and positive regulation of I-kappa B kinase/NF-kappa B signaling were enriched in the top 20 GO terms, as shown in lncRNA–mRNA coexpression networks were built to detect the functional mechanisms of lncRNAs in the key modules . There wTo identify the potential mechanisms through which the lncRNA-mediated regulation of signaling pathways occurs, we interacted the significantly different pathways and the lncRNA–mRNA coexpression network to obtain the lncRNA–mRNA pathway network . In the In the blue module, Flywch1 (a lncRNA) was linked to three mRNAs  and enriched in inflammatory bowel disease; transcriptional misregulation in cancer; parathyroid hormone synthesis, secretion, and action; oxytocin signaling pathway; cGMP–PKG signaling pathway; MAPK signaling pathway; Apelin signaling pathway; fluid shear stress and atherosclerosis; AGE–RAGE signaling pathway in diabetic complications; HIF-1 signaling pathway; p53 signaling pathway; and cellular senescence. Rnf169 (a lncRNA) was also linked to two mRNAs (Foxp3 and Mef2c) and abundant in inflammatory bowel disease; transcriptional misregulation in cancer; parathyroid hormone synthesis, secretion, and action; oxytocin signaling pathway; cGMP–PKG signaling pathway; MAPK signaling pathway; Apelin signaling pathway; fluid shear stress; and atherosclerosis.To investigate the interactive relationships among the hub genes in the modules, we submitted the hub genes to the STRING database to construct PPI networks . The PPIAfter 10 weeks of normal diet, HFD, and HFD + lycopene feeding, liver tissues were detected with HE staining. The result showed that compared with the normal-diet group a, the heIn recent years, a number of genes regulating NAFLD have been identified based on genomic studies ,32,33. MCompared with the differentially expressed genes, WGCNA used the expression information of thousands of genes to identify gene modules and perform association analysis with phenotypes . The hubNfkbia), a key gene in the NF-κB signaling pathway, was expressed in elevated levels in HepG2 cells during steatosis and inflammation [Increasing numbers of studies are suggesting that lncRNAs play an important role in the development and progression of various types of diseases ,43. In tammation . Silenciammation ,46. Zuniammation  proved tammation  reportedammation . MLL4 waammation . Moreoveammation . CalcocoIn the blue module, Flywch1 was the core lncRNA connected to three hub genes . Foxp3 has been reported to be involved in the development of NAFLD through the regulation of lipid metabolism and the inflammatory microenvironment ,53. AddiIn our present study, we found that feeding lycopene attenuated hepatic steatosis in HFD-induced NAFLD in mice. Lycopene was shown to inhibit the MAPK and NF-κB pathways to reduce superoxide synthesis . GourantBased on WGCNA, we identified NAFLD-related candidate hub lncRNAs and mRNAs, and we constructed lncRNA–mRNA coexpression networks and lncRNA–mRNA pathway coexpression networks. Our analysis revealed that the hub lncRNAs may regulate the inflammatory and metabolic pathways that are crucially involved in the pathogenesis of NAFLD. Moreover, lycopene has the potential to be a phytochemical for the prevention of HFD-induced liver steatosis. Therefore, in the future, researchers should investigate the regulatory role between lycopene and key lncRNAs on NAFLD.


This study aimed to determine the active components of Zhinao capsule (ZNC) and the targets in treating Alzheimer's disease (AD) so as to investigate and explore the mechanism of ZNC for AD.  The active components and targets of ZNC were determined from the traditional Chinese medicine systems pharmacology database (TCMSP). The target genes of AD were searched for in GeneCards. Cytoscape was used to construct an herb-component-target-disease network. A protein-protein interaction (PPI) network was constructed by STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the OmicShare. UCSF Chimera and SwissDock were used for molecular docking verification. Finally, four key target genes were validated by Western blotting. α) and quercetin, vascular endothelial growth factor A (VEGFA) and baicalein, and mitogen-activated protein kinase 1 (MAPK1) and quercetin showed good-to-better docking. Moreover, ZNC not only downregulated CASP3 and TNF-α protein expression but also upregulated the protein expression of VEGFA and MAPK1.  In total, 55 active components, 287 targets of active components, 1197 disease genes, and 134 common genes were screened, which were significantly enriched in 3975 terms of biological processes (BP), 284 terms of cellular components (CC), 433 terms of molecular functions (MF), and 245 signaling pathways. Caspase-3 (CASP3) and beta-sitosterol, tumor necrosis factor-alpha (TNF-α to affect T cell receptor (TCR), TNF, and MAPK signaling pathway, thereby achieving the treatment of AD. This study provides a scientific basis for further exploring the potential mechanism of ZNC in the treatment of AD and a reference for its clinical application. The active components of ZNC, such as beta-sitosterol, quercetin, and baicalein may act on multiple targets like CASP3, VEGFA, MAPK1, and TNF- Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, which is clinically characterized by a progressive decline in cognitive function . The hisCurrently, one effective treatment for AD is drug therapy, which can slightly alleviate symptoms and prevent or slow AD progression. Traditional Chinese medicine (TCM) formulas have long been used to prevent and treat a variety of diseases, including AD –6. UnlikCodonopsis pilosula), Huangqi , Huangjing , Rou Congrong , Yujin , Shi Changpu , Chuanxiong , and Dilong  and has good efficacy in treating AD [Zhinao capsule (ZNC), a Chinese patent medicine, is mainly composed of Dangshen  [https://www.uniprot.org/) [The traditional Chinese medicine systems pharmacology database (TCMSP) (msp.php) , which imsp.php) , an oralot.org/)  for furtot.org/) .https://www.genecards.org/) [https://www.genecards.org/) using “Alzheimer's disease” as the keyword.GeneCards (ds.org/)  is an inhttp://bioinformatics.psb.ugent.be/webtools/Venn/) to obtain intersecting targets. The common targets of ZNC and AD were imported into the STRING (https://string-db.org/) [We input the active components and AD-related targets of ZNC into the Venn tool Venny 2.1.0 (db.org/)  platformhttps://www.omicshare.com/) [P value <0.05 indicated significant enrichment.The targets were entered into the online software OmicShare (re.com/)  to identhttp://swissdock.ch/) [https://www.rcsb.org/) [http://www.pymol.org/) [SwissDock (ock.ch/)  was usedsb.org/) , and watol.org/) . The strSPF-grade ICR mice were intraperitoneally injected with D-galactose and sodium nitrite once per day for 6 weeks to establish an AD model . From thβ-actin, CASP3, TNF-α, VEGFA, and MAPK1, 1 : 1000) at 4°C overnight. The membranes were washed with TBST and incubated with the corresponding secondary antibodies for 2 h at room temperature. The proteins were visualized with the ECL reagent, detected by an automatic exposure instrument, and quantitatively analyzed by ImageJ software.The hippocampal tissue was lysed in RIPA buffer and centrifuged at 12000 rpm for 10 min to collect the supernatant. The protein samples were boiled for 5–10 min, separated on SDS-polyacrylamide gels, and transferred to a PVDF membrane. The membranes were blocked with 5% skim milk powder for 2 h and then incubated with primary antibodies  are shown in Finally, four key targets with a lower binding energy were selected for further analysis. The molecular docking results of these targets  deposition [β-induced memory deficits and neuronal atrophy via inhibition of phosphodiesterase 2 (PDE2) and PDE4 [First, 55 active components of ZNC with an OB ≥ 30% and DL ≥ 0.18 were identified, including beta-sitosterol, baicalein, and quercetin. One study showed that the synergy-based combination of beta-sitosterol successfully enhanced memory and learning by abating free radical and acetylcholine levels in memory loss rats . Anotherposition . Baicaleposition . Evidencposition . An expeand PDE4 . Quercetand PDE4 . Quercetand PDE4 . Evidencand PDE4 . Therefoβ [α signaling pathway predominantly mediates inflammatory and proapoptotic signaling pathways [β formation [The PPI network contained 134 targets, which represent the most likely core targets of ZNC in the treatment of AD. KEGG pathway enrichment analysis showed that the T cell receptor (TCR), TNF, and the MAPK signaling pathway may play roles in ZNC-mediated treatment of AD. Many previous studies have shown that TCR mediates antigen-specific T cell responses, and TCRs are protein complexes formed by six different polypeptides in the TCR signaling pathway , 44. In β . Neuronaβ . Accumulpathways . In addiormation . MAPKs aormation . Furtherormation . The aboα, were validated by Western blotting. A recent study revealed that CASP3 is the most important enzyme in apoptosis, and when activated, Aβ can induce neuronal apoptosis [α, which is involved in inflammation throughout the body [α expression is a risk factor for AD, and exacerbates Aβ and tau pathologies [α to treat AD.Molecular docking between 10 targets with a high degree of PPI and their respective components was evaluated. Then, four key targets with low to high binding affinity according to the measured ΔG kcal/mol, including CASP3, VEGFA, MAPK1, and TNF-poptosis . In addipoptosis , 53. A ppoptosis . Researcpoptosis . Furtherpoptosis . Anotherthe body . Furtherhologies . Therefoβ-induced neuronal injury through the MAPK signaling pathway. In addition, the expression of CASP3 and TNF-α was significantly downregulated after ZNC administration. This finding suggests that the active components of ZNC may inhibit apoptosis through the TCR and TNF-α signaling pathways.The results of the validation experiment confirmed that the expression of VEGFA and MAPK1 was significantly upregulated after ZNC administration. That finding suggests that the active components of ZNC could prevent Aα to affect the TCR, TNF, and the MAPK signaling pathway. With the help of network pharmacology and in vivo verification experiment, it is useful for us to further understand the influence of ZNC on AD and its potential molecular mechanism. Nevertheless, there are still some limitations in the study. First, the role of key active components should be further validated. Second, data sources mostly rely on specific databases, and frequent updates of the databases are necessary. In future research, our groups will carry out more comprehensive experimental research on ZNC and fully analyze its mechanism of action.In conclusion, the active components of ZNC, such as beta-sitosterol, quercetin, and baicalein may act on multiple targets like CASP3, VEGFA, MAPK1, and TNF-