Coptidis Rhizoma (CR) for the treatment of ulcerative colitis (UC) is accumulating. However, research revealing the targets and molecular mechanisms of CR against UC is scarce. In this research, a bioinformatics analysis was performed to carry out the physicochemical properties and biological activities of phytochemicals in CR and analyze the binding activities, targets, biological functions and mechanisms of CR against UC. This research shows that the CR’s key phytochemicals, which are named Coptisine, Berberrubine, Berlambine, Berberine, Epiberberine, Obacunone, Worenine, Quercetin, (R)-Canadine, Magnograndiolide, Palmatine and Moupinamide, have ideal physicochemical properties and bioactivity. A total of 1,904 potential phytochemical targets and 17,995 UC-related targets are identified, and we finally acquire 233 intersection targets between key phytochemicals and disease. A protein-protein interaction network of 233 common targets was constructed; and six hub targets were acquired with a degree greater than or equal to median, namely TP53, HSP90AA1, STAT3, ESR1, MYC, and RELA. The enrichment analysis suggested that the core targets may exert an impact on anti-inflammatory, immunoregulatory, anti-oxidant and anti-fibrosis functions mainly through the PI3K/ART signaling pathway, Th17 differentiation signaling pathway, inflammatory bowel disease signaling pathway, etcetera. Also, a molecular docking analysis shows that the key phytochemicals have strong affinity for binding to the core targets. Finally, the interaction network of CR, phytochemicals, targets, GO functions, KEGG pathways and UC is constructed. This study indicates that the key phytochemicals in CR have superior drug likeness and bioactivity, and the molecular mechanism of key phytochemicals against UC may be via the signaling pathway mentioned above. The potential and critical pharmacological mechanisms provide a direction for future research.Evidence of the advantages of  Coptidis Rhizoma were assessed. The results show that the key phytochemicals, which are named Coptisine, Berberrubine, Berlambine, Berberine, Epiberberine, Obacunone, Worenine, Quercetin, (R)-Canadine, Magnograndiolide, Palmatine and Moupinamide, have ideal drug likeness and biological activity.1. The physicochemical properties and bioactivity of key phytochemicals in 2. All biotargets of the key phytochemicals in CR against Ulcerative Colitis are identified, and core targets, namely TP53, HSP90AA1, STAT3, ESR1, MYC, and RELA, are recognized. Molecular docking indicated that the key phytochemicals have strong affinity for binding to the core targets.Coptidis Rhizoma have anti-inflammatory, immunoregulatory, anti-oxidant and anti-fibrosis effects by targeting core targets via the PI3K-ART signaling pathway, Th17 differentiation signaling pathway and inflammatory bowel disease signaling pathway.3. The key phytochemicals in Ulcerative Colitis (UC) is a type of inflammatory bowel disease characterized by mucosal inflammation initiating in the rectum and extending continuously towards the colon with the manifestation of abdominal pain, tenesmus and bloody diarrhea , which sCoptidis Rhizoma (CR) has been used to alleviate the notable symptoms of UC for thousands of years in China, such as bloody diarrhea, tenesmus and abdominal pain  was employed to obtain phytochemicals of CR. Adjusting the pharmacokinetics index so that the oral bioavailability (OB) was greater than or equal to 30%  was used to acquire the molecular descriptors, drug likeness and bioactivity , Swiss Target Prediction , A Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine , Search Tool for Interacting Chemicals  and Super-PRED . All candidate targets were duplicated after standard gene symbol transformation with review and human setting in the Uniprot database , Online Mendelian Inheritance in Man , Comparative Toxicogenomics Database , DisGeNET , NCBI gene , Therapeutic Target Database . All the targets of disease were identified after eliminating duplicates. The R-language package ggvenn (https://rdocumentation.org/packages/ggvenn) was used to discover intersecting targets between the potential protein targets of active ingredients in CR and UC. The intersection was the final target for CR against UC.UC-related targets were gathered from six open-source databases listed as follows: GeneCards  was employed to construct the PPI network with the minimum required interaction score of 0.95 and a species restriction of “Homo sapiens.” The PPI network was then visualized and analyzed using Cytoscape software . The Network Analyzer plugin of Cytoscape was utilized to analyze the topology parameters, including the minimum and maximum degrees of freedom in the network. The hub targets were selected based on degree values, with the upper limit of the filtering range set to the greatest degree-value and the lower limit taken as the median degree of freedom in the topological data  function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the core targets was performed and visualized through R-language packages, including “ClusterProfiler” (version 4.2.2), “org.Hs.eg.DB” (version 3.14.0) and “ggplot2” (version 3.3.6). The species was limited to “ormation , the R-lormation , was emphttps://pubchem.ncbi.nlm.nih.gov, accessed on 28 June 2022). The crystal structure of the hub target was retrieved from the Protein Data Bank database . BIOVIA Discovery Studio 2019 was used to extract ligands and water molecules from the crystal structure complex, as well as for protein preparation and docking grid creation. The first docking site in receptor cavities generated by BIOVIA Discovery Studio 2019 was selected as the binding site, and the Lib Dock method was adopted to perform the molecular docking. To verify the reliability of our docking result, AutoDock Vina was also employed to perform molecular docking between receptor and ligand.Molecular docking analysis was performed to investigate the binding affinity between the key phytochemicals and the core targets. The 3D structure of each of the compounds was obtained from the PubChem database , Swiss Target Prediction (1051), BATMAN-TCM (151), STITCH (38) and Super-PRED (1431), were used to obtain CR-related targets. The CR-related target set was established by combining a union of the screened results, 1,904 targets were obtained after the removal of duplications and transferring gene symbols . UC-relaHomo sapiens.” The PPI network was visualized by Cytoscape as shown in The PPI network was constructed by importing the 233 intersecting targets to the STRING database with the minimum required interaction score of 0.95 and species limited to “R-language packages were used to analyze the GO biological process enrichment and KEGG pathway of the six core targets. A total of 623 GO terms for biological processes were obtained, the top 20 count and the top 20 fold enrichment results were visualized using a histogram and a bubble diagram . ConsideMolecular docking was performed to determine the possible binding of the key phytochemicals in CR with the core targets of UC. In our research, two different molecular docking softwares were used to enhance the reliability of docking results. The lib docking score analyzed by Discovery Studio and the binding energy generated by AutoDock Vina are summarized in With increasing prevalence, UC affects millions of individuals worldwide, increasing the risk of colon cancer . Conside2, %ABS > 63.68% fell within the range of ideal oral bioavailability. The results demonstrate that these active phytochemicals have no restrictions in terms of drug likeness or ADME. Furthermore, a molecular with a bioactivity score greater than 0 has a significant likelihood of exerting great bioactivity , phytochemicals, targets, GO functions, KEGG pathways and disease (UC) interaction network demonstrated that the core targets of UC are modulated by multiple compounds in a manner of multi-targeted modulation, and the core targets exert multiple GO functions and KEGG pathways against UC. Moreover, the physicochemical properties and bioactivity of the active phytochemicals of CR, including drug likeness, absorption, distribution, metabolism and excretion (ADME), were evaluated according to Lipinski’s Rule of Five, TPSA and %ABS . Except activity . BioactiIn the bioinformatics analysis, a total of 233 potential targets of CR against UC were obtained, and six targets with degree values greater than or equal to the median of 15 were selected as the hub targets in the PPI network. The six hub genes are named TP53, HSP90AA1, STAT3, ESR1, MYC, and RELA. TP53 is the master regulator of apoptosis and senescence , which cvia the MAPK/PI3K/Akt signaling pathway (The GO results showed that biological processes were enriched in the regulation of inflammation, immune response, proliferation and apoptosis of cells, silencing and transcription of genes, primarily including responses to interleukin-6 and interleukin-8, T cell activation, apoptosis and proliferation of fibroblasts, and so on. KEGG pathways were further confirmed in present research, mainly including the PI3K/Akt signaling pathway, Th17 cell differentiation, inflammatory bowel disease, and so on. The PI3K/Akt signaling pathway is associated with various biological processes, including cell cycle, apoptosis, metabolism and angiogenesis . Suppres pathway . Th17 ce pathway . Inhibit pathway . The key pathway . The inf pathway . Previou pathway .The key phytochemicals of CR have ideal physicochemical properties and bioactivities and possess potential applications in the treatment of UC. According to previous findings and analyzed results in our research, the key molecular mechanisms of CR against UC are summarized as anti-inflammatory, immunoregulatory, anti-oxidant and anti-fibrosis by target to core genes and pathways, including TP53, HSP90AA1, STAT3, ESR1, MYC, RELA, the PI3K/Akt signaling pathway, Th17 cell differentiation and inflammatory bowel disease signal pathway. The potential and critical pharmacological mechanisms of CR against UC provide a direction for future research.


Traditional Chinese herbal medicine draws more attention to explore an effective therapeutic strategy for Alzheimer's disease (AD). Shenqi Yizhi granule (SQYG), a Chinese herbal recipe, has been applied to ameliorate cognitive impairment in mild-to-moderate AD patients. However, the overall molecular mechanism of SQYG in treating AD has not been clarified.  This study aimed to investigate the molecular mechanism of SQYG on AD using an integration strategy of network pharmacology and molecular docking.  The active compounds of SQYG and common targets between SQYG and AD were screened from databases. The herb-compound network, compound-target network, and protein-protein interaction network were constructed. The enrichment analysis of common targets and molecular docking were performed. β1–42, tanshinone IIA and BACE1, baicalin, and AchE.  816 compounds and 307 common targets between SQYG and AD were screened. KEGG analysis revealed that common targets were mainly enriched in lipid metabolism, metal ion metabolism, IL-17 signaling pathway, GABA receptor signaling, and neuroactive ligand-receptor interaction. Molecular docking analysis showed high binding affinity between ginsenoside Rg1 and A The therapeutic mechanisms of SQYG on AD were associated with regulating lipid metabolism, metal ion metabolism, IL-17 signaling pathway, and GABA receptor signaling. Ginsenoside Rg1, tanshinone IIA, baicalin, astragaloside IV, and folic acid may play an important role in AD treatment. A totales of AD . In addies of AD –7.Panax ginseng C.A.Mey, Astragalus membranaceus (Fisch.) Bunge, Scutellaria baicalensis Georgi, Salvia miltiorrhiza Bunge, and Alisma plantago-aquatica L with a ratio of 2 : 4 : 3 : 3 : 2. The main active compounds of SQYG included ginsenoside Rb1, ginsenoside Rg1, ginsenoside Rd, baicalin, cryptotanshinone, and tanshinone IIA [β deposition, tau hyperphosphorylation, and inflammation [β deposits and reduced Aβ1–42 levels [Although AD has been extensively studied for many years, its therapies are still lacking due to the complex pathology. Traditional Chinese medicine (TCM) has a long history of widely applications in Asia . TCM shonone IIA . These anone IIA , 12. SQYnone IIA . Previouammation . The hip2 levels . The neu2 levels .Network pharmacology, an approach capable of revealing the mechanisms of multiple drugs for diseases, makes the systematic study of herbal formulae achievable by interconnecting disease features, bioactive agents, and drug targets , 17. MolIn the present study, the active compounds of SQYG and common targets between SQYG and AD were screened from databases. The network pharmacology was applied to construct a herb-compound network and compound-target network and analyze the key nodes based on active compounds and targets. Enrichment analysis was performed to classify cellular components, biological processes, molecular functions, and KEGG pathways. Molecular docking was employed to analyze the binding affinity between main compounds and key targets. This study aimed to elucidate the pharmacological mechanisms of SQYG on AD.The active compounds of SQYG and common targets between SQYG and AD were screened from databases. The herb-compound network and compound-target network were constructed by the Cytoscape. The protein-protein interaction (PPI) network of common targets was constructed by the STRING. The Gene Ontology (GO) and KEGG enrichment analysis of common targets were performed by the SangerBox. The molecular docking was analyzed using the Autodock Vina and AutoDock. The schematic diagram of the study is shown in https://tcmspw.com/tcmsp.php), Traditional Chinese Medicine Integrated Database , The Encyclopedia of Traditional Chinese Medicine , and Bioinformatics Analysis Tool for Molecular MechANism of Traditional Chinese Medicine  databases [The active compounds of SQYG were collected from multiple databases, including the Traditional Chinese Medicine Systems Pharmacology database . The therapeutic targets for AD treatment were obtained from the DrugBank database (https://go.drugbank.com/), Therapeutic Target Database , ALZgene database , and DisGeNET database (https://www.disgenet.org/) [We hypothesize that the targets of SQYG intersect with the targets of AD were potential therapeutic targets of SQYG on AD. The protein targets retrieved from TCMSP databases were standardized using the UniProt Knowledgebase database  , 21.The herb-compound network helps to probe deeper into the interactions between herbs and corresponding compounds. The compound-target network was constructed for understanding closer correspondence between compounds and potential targets. The herb-compound network and compound-target network were constructed by the Cytoscape. The “degree” calculated by linked edges can well reflect the significance of nodes. The degree of each node and compounds were analyzed by the CytoHubba plugin in Cytoscape .http://sangerbox.com), which can classify cellular components, biological process, molecular function, and KEGG pathway. The results were used for post-analysis and visualization by the WebGestalt .The enrichment analysis was performed by the SangerBox  are indicated in Figures The main compounds of SQYG are shown in TCM has been used in clinical practice for several thousand years. TCM-based new drug development for treating complex diseases is promising. However, it is difficult to reveal molecular mechanisms of the Chinese herbal prescription due to the diverse ingredients and their complex interaction with the human body . Modern β-sitosterol, suffruticoside A, choline, cetylic acid, stigmasterol, hexadecanoicacid, 3-hydroxycoumarin, baicalin, ginsenoside Rb1, ginsenoside Rg1, ginsenoside Re, danshenol A, salvianolic acid B, danshenol B, astragaloside IV, 3 beta-hydroxytanshinone IIA, and isoastragaloside I. The PPI network results indicated that INS, ALB, IL6, TNF, TP53, IL1B, VEGFA, APOE, CTNNB1, and PPARG were key targets of SQYG on AD. The compounds  were detected by high-performance liquid chromatography (HPLC) in previous research. They were also screened by network pharmacology from TCM database. So, they were selected for further molecular docking with classical AD targets.In this study, network pharmacology, bioinformatics, and molecular docking network were applied to investigate the underlying pharmacological mechanisms of SQYG on AD. The main compounds of SQYG included gamma-sitosterol, GO enrichment analysis was used to explore the distribution of the overall proteins and grasp the correlation between proteins and biological function in a whole –30. In tThere were a lot of metabolic, regulatory, and signal transduction pathways in the organism. KEGG pathway enrichment analysis allowed us to identify important biochemical-metabolic pathways and signal transduction pathways in which the key proteins were involved . In thisβ in PS/APP mice [β by suppressing the TP53 expression [The transcription factor TP53, commonly known as p53, can initiate apoptosis. Hypoxia, DNA damage, oncogene activation, microtubule destruction, and oxidative damage all caused an increase in the p53 expression . OverexpAPP mice . Corresppression .Interleukin-1 beta (IL1B) is ubiquitously distributed in the hippocampus and hypothalamus . IL1Bs wγ (PPARγ) is a member of the PPAR family. The activation of PPARγ controlled the multiple pathways involved in inflammation and lipid metabolism. In addition, the vital role of PPARγ agonists in neuroprotection has been extensively studied in neurodegeneration, such as in Aβ-induced AD. Also, in the light of the present evidence, the neuroprotective effects shown by agonists of PPARγ were related to the capability of decreasing Aβ levels [γ has a wide spectrum of functions in nerve inflammation [The peroxisome proliferator-activated receptor-β levels . Overallammation , energy ammation , and redammation , which mβ-induced learning and memory deficit, hippocampus injury, and neuron apoptosis. The neuroprotective mechanisms were related to preventing the decrease of Aβ-induced mitochondrial membrane potential, cytochrome c release, and caspase-9/-3 activation [β‐stimulated BV2 microglial cells [Baicalin has been shown to have anti-inflammation and neuroprotective effects. Baicalin effectively improved Ativation . Besidesal cells .β protein levels in APP/PS1 mice hippocampus were found to be elevated due to the lack of folate [β42 by decreasing the mRNA and protein expressions of β-secretase and γ-secretase complex catalytic component in the APP/PS1 mice brain [Folic acid deficiency is linked to cognitive deterioration and AD . The folf folate . Moreovece brain .Menispermum dauricum DC. Dauricine alleviated cognitive deficits in 3xTg-AD mice by lowering Aβ plaques and hyperphosphorylated tau and raising hippocampus ATP levels. After dauricine administration, Aco2, Ndufs1, Cox5a, and SDHB involved in the mitochondrial energy metabolism were significantly increased, while the expression level of synapse-related proteins such as Syn1 and Syn2 were upregulated [Dauricine is a bisbenzylisoquinoline alkaloid derivative extracted from the rootstock of egulated . Dauriciegulated . OverallHowever, there were still limitations in the present study. The screened active compounds of SQYG lack analytical chemistry identification. Moreover, the key targets and potential pathways screened by enrichment analysis require experimental validation.β1–42, tanshinone IIA and BACE1, and baicalin and AchE. This study provides insights into pharmacological mechanisms of SQYG in ameliorating the cognitive impairment of AD.In conclusion, the present study screened 816 active compounds and 307 common targets of SQYG on AD and constructed compound-target network and PPI network using network pharmacology analysis. SQYG alleviated cognitive impairment of AD by regulating lipid metabolism, metal ion metabolism, IL-17 signaling pathway, GABA receptor signaling, and neuroactive ligand-receptor interaction. The molecular mechanisms of SQYG improving the cognitive function were associated with high binding affinity between ginsenoside Rg1 and A


Gastric ulcer (GU) is a peptic disease with high morbidity and mortality rates affecting approximately 4% of the population throughout the world. Current therapies for GU are limited by the high relapse incidence and side effects. Therefore, novel effective antiulcer drugs are urgently needed. Ginsenosides have shown good anti-GU effects, and the major intestinal bacterial metabolite of ginsenosides, protopanaxatriol (PPT), is believed to be the active component. In this study, we evaluated the anti-GU effect of PPT in rats in an acetic acid-induced GU model. High (H-PPT) and medium (M-PPT) doses of PPT (20.0 and 10.0 mg/mg/day) significantly reduced the ulcer area and the ET-1, IL-6, EGF, SOD, MDA and TNF-α levels in serum were regulated by PPT in a dose-dependent manner. We also investigated the mechanisms of anti-GU activity of PPT based on metabolomics coupled with network pharmacology strategy. The result was that 16 biomarkers, 3 targets and 3 metabolomic pathways were identified as playing a vital role in the treatment of GU with PPT and were further validated by molecular docking. In this study, we have demonstrated that the integrated analysis of metabolomics and network pharmacology is an effective strategy for deciphering the complicated mechanisms of natural compounds. Gastric ulcer (GU) is a painful lesion of the gastric mucosa in the upper abdomen that can lead to more serious symptoms including internal hemorrhage, stenosis, perforation and pyloric obstruction. As a serious health problem affecting 4% of the population throughout the world , GU is aMetabolomics, defined as the comprehensive qualitative and quantitative analysis of all small molecules (metabolites) present in a biosystem , is a brIn the current study, an acetic acid-induced gastric ulcer model, one of the representative experimental GU models ,25, was p < 0.01). However, compared with the model group, the intervention of all doses of PPT or omeprazole could significantly alleviate the weight loss caused by gastric ulcer (p < 0.05).The body weights of the rats in all groups, i.e., high-dose PPT , moderate-dose PPT , low-dose PPT , sham, model and positive , were measured every day, and the results are shown in 2), the omeprazole group (19.63 ± 2.42 mm2), H-PPT group (18.09 ± 3.54 mm2) and M-PPT group (21.23 ± 5.61 mm2) had significantly reduced ulcer areas. However, L-PPT (94.99 ± 10.27 mm2) showed no significant difference compared with the model group.In order to evaluate the ulcer formations, the macroscopic appearances of stomachs were captured, and the ulcer area was calculated. The macroscopic appearances of stomachs in different groups were captured: Macroscopic images of stomachs are shown in The general microstructures of the gastric mucosa were observed using H&E staining. As shown in The biochemical estimations in serum including ET-1, IL-6, EGF, SOD, MDA and TNF-α levels were carried out according to the instructions provided by the manufacturers. As shown in p < 0.01), while the expressions of ET-1 in H-PPT and M-PPT rats decreased to a similar extent as in the omeprazole group, suggesting that PPT treatment reduced the expression of pro-inflammatory cytokine. The level of EGFR significantly decreased in the model group (p < 0.01)  B. PPT com/z) from different spectral regions were as follows: 0.78–276.1187, 1.51–188.0742, 5.04–582.2692, 10.35–874.4197, 14.31–415.2157, 16.58–520.3442, 17.95–496.3475, 23.12–413.2453 in ESI+ mode, 0.52–197.8073, 3.57–241.1203, 8.77–225.1245, 12.44–453.2865, 16.96–564.3336, 20.55–568.3600, 22.79–303.2343, 24.95–281.2469 in ESI- mode. The RSDs of the peak intensity and RT in system stability, precision, reproducibility and sample stability tests are shown in Sixteen ions were randomly selected from QC sample for the method validation throughout the whole study. The ions with the retention times and mass-to-charge ratios  value > 0.8 were identified as the biomarkers and marked in red in S-plots  chromatograms in positive and negative modes of the three groups of serum samples is shown in  S-plots D,H. The By MetaboAnalyst analysis, the identified biomarkers were mainly involved in 7 potential metabolisms with impact values above 0.10 . AlteratTo elucidate the interactions between PPT and anti-GU activity, and predict the related potential targets and pathways from a comprehensive perspective, network pharmacology was performed. The disease targets of GU and molecular targets of PPT were searched and filtered using various databases, and their intersection was considered the predicted targets of PPT against GU. Then, GO and KEGG analysis were performed, and the PPI network and component-target-pathway networks were established.A total of 119 targets of PPT and 7170 targets related to GU were retrieved from various databases. The PPT-related targets were matched with the candidate targets of GU; specifically, 89 common targets were identified , which wp value. The abscissa represented the enrichment score, while the ordinate represented signal pathways. The signal pathways such as the sphingolipid signaling pathway and the PI3K-Akt signaling pathway corresponded to more target genes, suggesting that these pathways may be important pathways for PPT to exert anti-GU effects. Interestingly, sphingolipid metabolism, arachidonic acid metabolism and linoieic acid metabolism were significantly enriched, which was partly consistent with the results of serum metabolomics analysis. As shown in To identify the hub genes of PPT against GU, a protein–protein interaction (PPI) network was constructed A to showMetabolomics and network pharmacology were merged to construct an integrative network, obtaining a comprehensive view of the mechanism of PPT against GU. The potential biomarkers and metabolic pathways identified in metabolomic study and the potential hub targets and pathways identified in network pharmacology were imported into the MetScape plugin in Cytoscape 3.8.2 software to generate the ‘compound-reaction-enzyme-gene’ networks .To sum up, a total of 16 biomarkers (19(S)-HETE, arachidonic acid, prostaglandin F2a, linoleic acid, 12,13-EpOME, phytosphingosine, sphinganine, PE(22:1(13Z)/16:0), PE/P-18:1(9Z)), PC(O-16:0/2:0), SM(d18:0/22:0), SM(d18:0/20:0), SM ), SM(d17:1/24:1(15Z)), SM(d18:1/14:0), SM(d18:1/24:1(15Z))), 3 key targets , and 3 metabolic pathways  were confirmed, which were considered to play vital roles in the therapeutic effect of PPT on GU.In order to predict the binding modes of key targets with PPT and investigate the localized binding sites and relative binding energies in the active pocket of key genes, molecular docking was carried out by retrieving the crystal structures of key targets, preparing the ligand and performing the GLIDE docking.The PDB IDs of the protein structures we employed are displayed in Gastric ulcer (GU) is an ulcerative lesion in the surface of stomach mucosa, which is caused by various invasive factors especially the gastric acid secretion. Conventional medications to treat GU have shown various side effects and high relapse incidence. Thus, an effective antiulcer drug with few side effects is needed. PPT as an important human microbial metabolite of ginsenosides has shown good anti-oxidation and anti-inflammation activities. In this study, we have evaluated the therapeutic effect of PPT on GU.GU is one of the major gastrointestinal disorders, which often occur due to an imbalance between offensive factors and defensive factors . The offMetabolomics and network pharmacology analysis were carried out to identify the pharmacological effect from the aspects of potential biomarkers, metabolic pathways, targets, respectively. Firstly, metabolomics was used to decipher the dynamic changes of endogenous metabolites for assessing drug effects systematically. In our study, pattern recognition with multivariate statistical analysis suggested that the metabolic profiles of the acetic acid-induced GU rats were markedly different from that of the normal control rats, while the PPT-treated GU treatment groups had similar metabolomic profiles as the normal control group. We identified 26 potential metabolites involving 7 metabolic pathways. Secondly, network pharmacology at the molecular level was carried out to explain the relationships between the latent metabolites and pathways. The PPI network was constructed, the bub genes were screened out, and the component-target-pathway network indicated that PPT was closely related with 45 target genes and 16 pathways. Finally, an integrated analysis of metabolomics and network pharmacology was performed, and the ‘compound-reaction-enzyme-gene’ network was established: 16 biomarkers, 3 targets and 3 metabolic pathways were screened out that were considered to be closely related to the therapeutic effect of PPT on GU.Two of these three pathways were lipid metabolism, including linoleic acid metabolism and sphingolipid metabolism, and one was arachidonic acid metabolism. Alteration in the metabolism of lipid was reported to be disturbed in GU . PhosphaThree targets including CYP2C9, CYP3A4 and PIK3CA were suggested as the potential therapeutic targets. CYPs are members of the cytochrome P450 families, which are mainly expressed in the gastrointestinal tract and human liver. CYPs can convert numerous exogenous compounds, such as most therapeutic agents, to less toxic components ,43. CYP2Cytochrom enzymes including CYP2CA and CYP3A4 are very important superfamily of biotransformation enzymes that are involved in oxidative metabolism of a wide variety of endogenous and exogenous compounds such as drugs. Numerous people in many countries have taken ginseng or its derived products, however, little is known about the interactions between ginseng and prescription drugs. Therefore, it is necessary to evaluate whether ginseng and its active components exhibit the potential to exert influence on metabolic enzymes. Thus, it is meaningful to explore the relationship between PPT and P450 enzymes. Actually, the complex relationship among PPT, biomarkers, targets and metabolic pathways were found based on metabolomics study combined with network pharmacology study. That is, some metabolites had been re-regulated after PPT binding to CYP3A4 and CYP2CA. Just as shown in Taken together, the three key target genes play important roles in the occurrence and development of gastric ulcer. Molecular docking also verified the high affinities of PPT with these key targets, while CYP3A4/PPT has the lowest binding energy.Our findings showed that PPT could exert anti-GU effect similar to that of omeprazol. Though they showed similar effect, the mechanism of them were not the same. Omeprazol is belonging to proton pump inhibitors (PPIs), which could inhibit H+, K+-ATPase (proton pump), reduce the H+ secretion by parietal cells, effectively decrease gastric acid secretion and exert anti-GU effect. As for PPT, our study based on integrated metabolomics and network pharmacology showed that PPT could play a therapeutic effect on GU by regulating three key targets, 16 biomarkers and 3 metabolomic pathways. Both PPT and omeprazol showed good anti-GU effect, so they may be used together in patients. Though the mechanism of PPT and omeprazol were different, it is not sure whether there would be some potentially drug interactions after combination of them. Ginsenosides always showed synergistic effect by increasing efficacy and decreasing toxicity when combined with other drugs. For example, ginsenoside Rg3 can enhance drug efficacy and reduce drug-induced toxicity when used together with first-line chemotherapy drugs in clinical , ginsenoHowever, we are aware that our research may have some limitations. For instance, it would have been better if the experiments had been set up as days 1, 3, 5 and 7 and the data had been collected on each of these days. Future studies could improve the experimental design and collect time-course data.Protopanaxatriol  was provided by the Research Center of Natural Medicine of Jilin University . Omeprazole was chosen as the positive drug and purchased from Sigma . Enzyme-linked immunosorbent assay (ELISA) kits of ET-1 and IL-6 were obtained from ImmunoWay Biotechnology Company . ELISA kits of Epidermal growth factor (EGF) was obtained from JiuBang Biotechnology Company . ELISA kits of TNF-α were purchased from MultiSciences (Lianke) Biotech Co., Ltd., Hangzhou, China, and ELISA kits of SOD and MDA were bought from ZCIBIO Technology Co., Ltd., Shanghai, China. The antibodies of ET-1 and Epidermal growth factor receptor (EGFR) were purchased by Abcam . UPLC grade acetonitrile and methanol were bought from Fisher Chemical Company . UPLC grade formic acid was purchased from Sigma-Aldrich . All other chemicals were used at the analytical grade. Deionized water was produced by a water purification system . Acetic acid was 36~38% and of analytical grade from J&K Technology Co., LTD. Chloral hydrate was provided by Biosharp Co., Ltd. . Paraxanthine was provided by Beijing Laiyao Biological Technology Co., Ltd. . Linoleic acid and arachidonic acid were obtained from Sigma-Aldrich Co., Ltd., . Phytosphingosine and sphinganine were from Beijing Century Aoke Biological Technology Co., Ltd., Beijing, China. 12,13-EpOME was purchased from Shanghai Zhen Zhun Biological Technology Co., Ltd., Shanghai, China. 19(S)-HETE was manufactured from Xi’an Ruixi Biological Technology Co., Ltd., Xi’an, China.In the study, an acetic acid-induced gastric ulcer model was used to investigate the therapeutic effect of PPT for treating GU, and an integrated strategy combining metabolomics and network pharmacology was used to elucidate the molecular mechanism of PPT’s anti-GU effects; then, molecular docking was used to show the high affinities between key targets and PPT. The research flowchart of the study is shown in A Waters Xevo G2-XS QTOF mass spectrometer  equipped with a UPLC system by an electrospray ionization (ESI) interface was applied for analysis. Data recording was carried out on a MassLynx V4.1 workstation .m/z 556.2771 in the positive mode and 554.2615 in the negative mode) was injected at a rate of 10 μL/min as external reference of Lock Spray™. Centroid mode was used to record the MassLynx data.The optimized parameters of Q-TOF/MS were set as follows: drying gas, N2; desolvation temperature, 400 °C; capillary voltage, 2.6 kV (ESI+) and 2.2 kV (ESI−); desolvation gas flow, 800 L/h; mass, 50–1200 Da; cone voltage, 40 V; collision energy of high energy function, 20–40 V; collision energy of low energy function, 6 V. Leucine enkephalin (v/v) and 0.1% methanoic acid in acetonitrile  with a flow rate of 0.4 mL/min in a liner gradient program: 0–2 min, 10% B; 2–26 min, 10–90% B; 26–28 min, 90% B; 28–28.1 min, 90–10% B. The injection volume was 2 μL, the column temperature was maintained at 30 °C and the sample manager temperature was set at 15 °C.The separation was done on an ACQUITY UPLC BEH C18 column  from Waters Corporation  using a mobile phrase of 0.1% methanoic acid in water  provided by Changchun Yisi experimental animal technology Co., Ltd. according to the protocols approved by the Review Committee of Animal Care and Use of Jilin University. The study was approved by the Institutional Animal Care and Use Committee of Jilin University School of Pharmaceutical Sciences . Prior to the experiment, the rats were housed at 20~25 °C under 12 h light/dark cycle with the relative humidity at 40~60%. After a habituation for 7 days, the animals were divided into six groups randomly (8 rats/group): sham group, model group, positive group  and PPT groups of three doses , namely the low-dose PPT group (L-PPT), moderate-dose PPT group (M-PPT) and high-dose PPT group (H-PPT) respectively, which was chosen based on the pre-experiments. All the rats were prohibited from food intaking for 16 h, and then the laparotomy was performed on all rats through a midline epigastric incision under anesthesia . The stomachs of all the rats were exposed, 0.3 mL of acetic acid was injected under mucosa at the junction of the stomach body and pyloric sinus for the rats in model group, omeprazole group and PPT treatment groups of three doses. And 0.3 mL of vehicle  was injected into the stomachs of the rats in the sham group. During the operation the acetic acid was not allowed to escape. The stomach wound was carefully washed with 0.9% saline, and then cautiously put back to the abdominal cavity, embedded in the omentum, and the abdomen was finally sewed. Afterwards, the rats were subjected to the various treatments in a manner of gavage for 7 days. The rats were treated with intragastric administration of different samples once a day, and they freely had access to food and water supplies. The rats of sham and model groups were given 0.9% saline (10 mL/kg), the rats in the positive group were orally administered with omeprazole aqueous solution (0.4 mg/mL), and PPT treatment groups were with PPT aqueous solution . One hour after the last treatment, the rats were killed, and the stomach samples and blood samples were harvested.g rpm for 10 min at 4 °C. After drying under a mild stream of nitrogen, the supernatant was dissolved with 80% methanol and 20% of water, and filtrated with a 0.22 µm syringe filter to prepare the test sample solution. Simultaneously, the quality control (QC) sample was prepared by mixing 20 μL of each sample.Blood of all rats was collected 1 h after gavage from abdominal aorta and clotted at 4 °C for 1 h, then centrifuged at 3000 rpm for 10 min to obtain the serum. 800 µL serum was used to evaluate the ET-1, IL-6 and EGF levels. 500 µL serum was prepared for metabolomics analysis. Three times volume of methanol was added to the serum, then the supernatant was harvested by vortexing for 5 min, placing at 4 °C for 10 min and centrifugation at 10,000× The body weights of the rats in all groups were measured every day.2).In order to evaluate the ulcer formations, after the animals were sacrificed, the stomach was collected and the abdomen was unfolded along the greater curvature. The stomach was opened and cleaned with 0.9% saline, and then to calculate the ulcer area , interleukin-6 (IL-6), epidermal growth factor (EGF), superoxide dismutase (SOD), malondialdehyde (MDA), tumor necrosis factor-α (TNF-α) in serum were evaluated using ELISA kits according to the instructions provided by the manufacturers. The ELISA steps of ET-1 and IL-6 were as follows: reconstituted biotinylated detection antibody and protein standard and diluted the wash buffer as specified. Performed serial dilution of protein standard and prepared samples as desired. Added 100 μL of protein standard, sample or control to each well and incubated for 2 h at room temperature. Aspirated protein standards, samples or controls out and washed plate 4 times. Diluted biotinylated detection antibody as specified, and added 100 μL to each well and incubated for 2 h at room temperature. Aspirated biotinylated detection antibody out and washed plate 4 times. Diluted streptavidin-HRP as specified. Added 100 μL of 1× Streptavidin-HRP to each well and incubated at room temperature for 60 min. Aspirated 1× Streptavidin-HRP out and washed plate 4 times. Added 100 μL of Ready-to-Use Substrate to each well and incubated at room temperature for color development. Add 100 μL of Stop Solution and read OD values at 450 nm. The ELISA steps of EGF were as follows: Prepared all reagents before starting assay procedure. Added 50 μL of Standard or Sample to the appropriate wells. Blank well didn’t add anyting. Added 100 μL of Enzymeconjugate to standard wells and sample wells except the blank well, covered with an adhesive strip and incubated for 60 min at 37 °C. Washed the Microtiter Plate 4 times. Added Substrate A 50 μL and Substrate B 50 μL to each well. Gently mixed and incubated for 15 min at 37 °C, protecting from light. Added 50 μL Stop Solution to each well. Read the OD values at 450 nm using a microtiter plate reader within 15 min.2O2, and incubated overnight with ET-1 antibody and EGFR antibody, respectively. Sections were then stained using avidin-biotin-peroxidase complex method and counterstained with hematoxylin. Image-Pro-Plus software(IPP) 6.0 was utilized to evaluate the intensity of the immunohistochemistry. Firstly, the intensity calibration of the image was done by choosing the brightest pixels. Secondly, the area of interest (AOI) was selected manually and the measurement options were settled. Finally, the area of the AOI was measured followed by the collection of the Integral Optical Density Values . OD value was calculated by IOD/area. Each group was evaluated with 3 different AOI and came out with the average and the standard deviation of OD values.The gastric mucosa were stained immunohistochemically for ET-1 and EGFR protein expression. The paraffin-embedded sections were deparaffinized and incubated in methanol and 3% HThe method was validated in system stability, precision, reproducibility and sample stability. Firstly, the system stability was accessed by QC sample, which was randomly run covering the whole analysis process, and eight exact mass/retention time pairs were selected from different spectral regions in ESI+ and ESI- respectively. Secondly, the precision of the method was evaluated by the QC sample with six consecutive replicate injections. Thirdly, the reproducibility of sample preparation in this method was investigated by performing six parallel replicates of a chosen sample. Fourthly, the sample stability of those analytes was tested by analyzing 1 serum sample at 4 °C for 0, 4, 8, 10, and 12 h. The results of validation were expressed as the relative standard deviation (RSD).p-value below 0.05 and variable importance in the projection (VIP) greater than 1.0 were considered potential biomarkers. Then, the potential biomarkers were also screened with the area under curve (AUC) > 0.8 in the predictive receiver operating characteristic (ROC) curves. Further, the biomarkers were identified by comparing with either the standards or the tandem mass spectrometry (MS/MS) fragmentation patterns based on HMDB and METLIN databases. Finally, the MetaboAnalyst 5.0 was used to confirm potential metabolites and metabolic pathways .The raw file of the original mass spectrometry data was acquired and processed in MarkerLynx XS Version 4.1 software, with the optimized parameters as follows: retention time, 2~28 min; retention time window, 0.20; mass, 50~1200 Da; mass tolerance, 0.10 ppm; mass window, 0.10 ppm; noise elimination, level 6. Then SIMCA-P sofware  was used for multivariate data analysis. Principle component analysis (PCA) was performed to generate an overview for clustering of different groups and to search for potential outliers. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was then carried out to evaluate the maximum separation between two different groups. The corresponding S-plots were used to distinguish spectral variables and provide visual presentation of the OPLS-DA predictive results that contributed to the separation of the samples. The quality was also validated with R2 and Q2 followed by rigorous permutation tests (number: 200) to indicate statistical significance. The differential metabolites with https://www.genecards.org/), DrugBank (http://www.drugbank.ca/), GAD (http://geneticassociationdb.nih.gov/), OMIM (http://www.omim.org/), Disgenet (https://www.disgenet.org/), TTD (http://database.idrb.cqu.edu.cn/TTD/) and other databases by using ‘gastric ulcer’ or ‘stomach ulcer’ as the keyword. Secondly, the molecular targets of PPT were filtered by searching the keywords ‘Protopanaxatriol’ from PharmMapper (http://lilab-ecust.cn/pharmmapper/), SEA database (http://sea.bkslab.org), STITCH (http://stitch.embl.de/), SwissTarget (http://www.swisstargetprediction.ch/), BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/). Thirdly, the intersection of compound targets and disease targets was considered the predicted targets of PPT against GU, which was used to screen core targets. And the Cytoscape 3.8.2 software was used to construct the visual regulation network diagram of “component-targets-disease”. Fourthly, a protein-protein interaction (PPI) network was created by uploading the core target data to the String (http://string-db.org/cgi/input.pl) database and hiding the discrete nodes (highest confidence = 0.9) via Cytoscape 3.8.2 software. Then the hub gene network was constructed with the hub genes which were screened from the key nodes (Degre e ≥ 7) in the PPI network. Fifthly, the Gene Ontology (GO) enrichments and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. Uploaded the core target data to the Gprofiler (https://biit.cs.ut.ee/gprofiler/convert) database for GO analysis and download the data file. The GO enrichment histogram was obtained through GO visualization processing with OmicShare Tools (https://www.omicshare.com/tools/). Then the core target data was uploaded to the DAVID (https://david.ncifcrf.gov/) database for KEGG Pathway analysis. We identified key pathways by combining evidences from the literature. The bubble chart of KEGG enrichment was obtained through visualizing the pathways with Omicshare (https://auth.lifemapsc.com/). Base on the bubble chart, Cytoscape 3.8.2 was used to construct the visual regulation network diagram of ‘component-targets-pathways’.In order to reveal the key targets and related proteins and pathways, and provide visualization of the component-targets-pathways, Cytoscape 3.8.2  was used to construct the network. Firstly, the disease targets of GU were searched in GeneCards  and key targets (identified in network pharmacology) were imported into the MetScape plugin to construct the ‘biomarker-reaction-enzyme-target’ network, then the key biomarkers, targets and metabolic pathways could be recognized.https://www.rcsb.org/). The receptors were prepared using the Protein Preparation Wizard in the GLIDE software. Secondly, the ligand, namely PPT, was drawn with Maestro Elements and subjected to minimization using the OPLS3 force field. Thirdly, the Receptor Grid Generation and GLIDE docking were carried out. Extra precision docking was conducted, and the parameters of partial charge cutoff and scaling factor were set at the default values 0.15 and 0.80, respectively. PyMOL was used to produce the final figures of the docking results.Molecular docking was carried out to predict the binding modes of key targets with PPT using Schrödinger . Firstly, the crystal structures of key targets screened out in network pharmacology were retrieved from the RCSB Protein Data Bank , as well as related metabolites and pathways. The targets were further validated by molecular docking. This research shed light on the pathogenesis of GU and provides a promising anti-ulcer candidate.


Artemisia argyi (AA) has been proven to be effective in the adjuvant treatment of rheumatism (RA), but the mechanism of its action in RA is not clear. This study aims to clarify the molecular mechanism of AA as a potential therapy for RA by using network pharmacology. The TCM systems pharmacology (TCMSP) was used to screen the active components of AA, and identification of the potential target genes of active compounds and rheumatism was performed with PharmMapper and GeneCards, respectively. Construction of complex target networks and protein–protein interaction networks was based on the Cytoscape software. The biological functions and pathway analysis of targets and effective targets were analyzed using DAVID. Our study demonstrated that 105 target genes were associated with these active compounds and RA. ALB, AKT1, and MAPK1 were the first three hub genes, and the metabolic and signaling pathways related to these hub genes were remarkably abundant. Results showed that AA might play a role in RA by affecting multiple targets and multiple ways, reflecting that TCM was characterized by multicomponents and multitargets. AA has the potential to be a promising new candidate for the treatment of RA and has value for further research and development. The disease is complex, lingering, and difficult to recover and has a high disability rate, thereby seriously threatening the physical and mental health of human beings. The main drugs used to treat rheumatoid arthritis (RA) usually have significant side effects3. Moxibustion is considered a safe and effective way to treat RA4.Rheumatism is a kind of disease that predominantly affects bone, joint, synovial bursa, tendon, fascia, muscle, nerve and other surrounding soft tissue. The prevalence of rheumatic diseases ranges from 11.6% to 46.4%, depending on the region, study protocol, and age of respondents. The prevalence of symptomatic osteoarthritis ranges from 5.1% to 20.8%, with the lumbar spine, knees, and cervical spine as the most affecte5. In clinical practice, moxibustion therapy is usually used to treat RA and includes traditional (pure moxa stick) or indirect moxibustion methods, which are performed by implanting insulation materials between the acupoints and moxa stick8. Moxibustion can improve the immune function of the body through anti-inflammatory and immune effects, inhibit the secretion of synovial cytokines, and play a therapeutic role in rheumatoid arthritis9. Referring to the theory of TCM, moxibustion can help to warm and activate meridians and promote blood circulation and qi5. The results of randomized clinical trials by meta-analysis showed that the combination of moxibustion and drug therapy has a favorable effect in terms of effectiveness compared with conventional drug therapy alone10. Researchers analyzed the efficacy and safety of moxibustion in the treatment of knee osteoarthritis and showed that moxibustion has significant curative effect compared with Western medicine11. The clinical and experimental studies have confirmed that moxibustion may relieve the symptoms of osteoporosis, which can be explained that regulation of the bone metabolism index and endocrine and protein expression levels will help to reach the steady state of bone metabolism and inhibit osteoclast activity, thereby reducing bone resorption13. All the above studies supported that moxibustion is effective in the treatment of RA.Moxibustion, a traditional Chinese therapy, has been used for thousands of years in China and its neighborArtemisia argyi (AA) are a common Chinese herbal medicine, which can be used as medicine or food, and have dual value of medicine and food. They are widely used to produce moxa stick as the main raw material in China14. In the moxibustion treatment, the antioxidants in AA combustion products adhere to the skin at acupoints and penetrate the body through the heat of moxibustion. Therefore, AA, as the raw material of moxibustion, plays a key role in the treatment of RA. However, the pharmacological mechanism of AA is still unclear, and no relevant research is available, which affects its clinical application and the research and development of related new drugs15. Network pharmacology based on the theory of system biology, whose systematic and multi-dimensional analysis characteristics are consistent with the holistic view of TCM, provides a powerful tool for revealing the complex mechanism of TCM. The research shows that the trend of combining calculation, experiment and clinic is a promising development direction of TCM network pharmacology16. In our study, network pharmacology method was used to predict and analyze the mechanism of AA in the treatment of RA from the molecular level, so as to provide scientific basis for the clinical application of AA and the development of new drugs.The leaves of 17.The principal components of AA were referred to the TCM systems pharmacology (TCMSP) database and analysis platfor18. Brain capillary wall and glial cells formed a barrier between plasma and brain cells, and so do choroid plexus formed between plasma and cerebrospinal fluid, which are called the blood–brain barrier (BBB), preventing certain substances (mainly harmful) from entering brain tissue through blood. The permeability of BBB is an indicator of drug permeability. In our research, 30%, 0.18, and 0.3 were selected as the best cutoff values of OB, DL, and BBB, respectively20. The components of AA with values higher than these reference standards were regarded as functional constituents.Oral bioavailability (OB) is a major index that reflects the degree to which oral drugs reach the targets by passing through intestinal wall barriers. Compounds with low OB may exhibit minimal effect because few active components appear in the blood. The drug-likeness (DL) index is applied in the identification of drugs and nondrugs, and compounds with low DL values may not be drugshttp://59.78.98.102/pharmmapper/) and analyze23. Using “human protein targets are only used to select target sets” as the search term, the rest were set by default. The selected protein target was imported into the UniProt database (https://www.uniprot.org/) in PDB ID format. The prediction indexes of the effective components of AA were achieved by searching and converting.To investigate the relation between each selected active compound and its target genes, the main active ingredients of AA were organized in SDF and MOL2 formats, and then uploaded to the PharmMapper server (https://www.genecards.org/) to obtain the reported genes associated with rheumatic diseases.The keyword “rheumatic diseases” was used to search GeneCards (http://cytoscape.org/), and used to construct the model of the medicine-ingredients-disease-targets (M-I-D-T) network. In this network model, nodes represented molecules or target proteins, whereas edges represented relationships among components, disease, and targets.The target information of effective components in AA and disease target genes was processed with the Cytoscape 3.8 software (24. By using the online STRING tool (version 11.0), the PPI of target genes was picked out while a required interaction score was above 0.4, and the target genes of PPI network having high connectivity degrees were selected as hub genes.The Search Tool for the Retrieval of Interacting Genes (STRING) database is an effective tool for exploring and constructing protein–protein interaction (PPI) networksThe biological functions of these selected genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), so do pathway analysis of target biological functions and effective targets. Data were exported from David and screened for count > 5, p-value < 0.05, and FDR < 0.05. By using Cytoscape, a compound–target–pathway network was set up to understand the relationship among RA, targets, and compounds systematically.The 3D structure of key compounds was made by Chem Office software, then the 3D structure of core protein gene was downloaded from PDB database, the protein was dehydrated and dephosphorized by PyMOL software, the pdb format of compound and core protein gene was converted into pdbqt format and active pocket was found by AutoDock 1.5.6 software, and finally, Vina was run for docking. The binding activity of the two is evaluated according to the binding energy, and the binding energy ≤ − 5.0 kJ/mol is selected as the screening basis to evaluate the reliability of network analysis and prediction.By considering OB ≥ 30%, DL ≥ 0.18, and BBB ≥ 0.3, TCMSP results showed 135 traditional components in AA, among which seven active compounds were retrieved from TCMSP , molecular function (MF), and cellular component (CC) were filtered by considering p-value < 0.05 and FDR < 0.05.As shown in Fig. Thirteen pathways were selected in accordance with p < 0.05 and FDR < 0.05 Fig. B to clarGO was annotated, and the response pathway was analyzed. By importing the effective target proteins, chemical components, and pathways into Cytoscape software, the compound-target-pathway network diagrams were set up Fig. .Figure 5Taking the binding energy ≤ − 5.0 kJ/mol as the standard, the molecular docking verification of key active components and key targets was carried out. The results show that the affinity of the two docking sites is less than that of − 5.0 kJ/mol, which indicates that there is a good binding activity between the key active components and key targets, proving that the prediction of this study is more reliable, as shown in Table RA is a chronic inflammatory and autoimmune disease that is distinguished by the excessive proliferation of synovium and inflammatory cell infiltration, causing the chronic inflammation of joints and secondary erosion of cartilage and bone. The main causes of the disease are immune response, infection, and endocrine disorders. In the present study, we propose a network pharmacology method that combines the strategy of component prediction and target path analysis. This method is used to explore the potential regulatory effects of AA on inflammation, immune system, and angiogenesis, providing a new perspective for the treatment of RA. The potential cooperation mechanism of EEAA is predicted and analyzed from the point of view of biological network, and preliminary experimental validations are performed.In this study, 135 active components of AA and 105 potential targets of AA in the treatment of RA were screened out. PPI network identified ALB, AKT1, MAPK1, MAPK8, MMP9, EGFR and CASP3 as key potential targets. The molecular docking results showed that the active ingredients had good binding activity with potential targets. KEGG results showed the top five pathways  are directly or indirectly involved in the formation of RF. Based on the experimental results and a number of articles on the therapy of RA by AA, the authors concluded that AA plays an important role in the treatment of RA by regulating the proliferation and differentiation of bone cells and osteoclasts and immune-inflammatory response.25. AKT1 and MAPK8, as hub genes, play a major role in osteoclast differentiation. AKT1, a unique signaling intermediate in osteoblasts, can regulate the differentiation of osteoblast and osteoclast at the same time. The targeted inhibition of AKT1 can result in effective bone acquisition, and related AKT1 and AKT2 have significant influence on the cell differentiation pathway26. The MAPK8 signal body is the target to prevent joint destruction27. MAPK is a collection of evolutionarily conserved serine/threonine protein kinases, which are activated by certain extracellular stimuli and may startup the signal transmition from cell membrane to nuclear. The activation of the MAPK pathway can induce the expression of heat shock protein (which has the function of regulating cell proliferation) in osteoblasts, thus promoting the formation of osteoblasts. KEGG pathway analysis demonstrates that osteoclast differentiation and Ras signal pathway are involved in the formation of RA. Therefore, AA may serve a purpose in the treatment of RA by regulating the proliferation and differentiation of osteoclasts and osteoclasts.The destruction of articular cartilage and bone is the main cause of disability. In recent years, a large number of related research demonstrated that osteoclasts could be the key to the pathological process of bone destruction in RA. The main active ingredient beta-sitosterol in AA can regulate the proliferation and differentiation of osteocytes and osteoclasts, so as to achieve the effect of treating RA28. In addition, MAPK8 is also called JNK, and its activation in RA synovium can mediate articular damage in rats with adjuvant-induced arthritis29. KEGG results showed that FOXO signaling pathway is directly associated with the formation of RA. FOXO3a can mediate TNF-α through PI3K/AKT signaling pathway to inhibit the proliferation and invasion of trophoblast cells and promote their apoptosis, thus being expected to result in inflammation in the body30. In vitro experiment, AA could significantly down regulate the level of IL-17, IL-1βand TNF-α in RA rats31. Therefore, the authors suggest that AA achieves its therapeutic effect on rheumatoid arthritis mainly by regulating the immune-inflammatory response. Therefore, MAPKs and Akt signal pathway are promising targets for RA therapy, and the research and development of corresponding inhibitors for both pathways are hot spots in the research and development of RA therapeutic drugs. These results demonstrated the complicated relationship among compounds, targets, and pathways of AA in rheumatism32.RA is a chronic inflammatory and autoimmune disease characterized by excessive synovial proliferation and inflammatory cell infiltration, resulting in chronic inflammation of joints and resultant erosion of cartilage and bone. Therefore, AA plays an important role in the treatment of RA by regulating immune-inflammatory response. The main active ingredient stigmasterol in AA can regulate the expression of inflammatory factors, thus achieving anti-inflammatory effectHowever, this research has the following three limitations: (1) all the prediction results were achieved by analysis based on limited online databases, which are kept updating. (2) The composition of AA is so complex that some compounds not identified are likely not involved in the analysis and discussion. (3) The future study should focus on verification experiments which help to explain the prediction results of this study.TCM usually plays an important role to prevent and treat various diseases. Therefore, in this paper, the network pharmacology method was used to screen RA-related targets and AA signaling pathways. Thus, the genomic space might be connected to the pharmacological space. In conclusion, we predicted the mechanism of action of EEAA in the treatment of RA by analyzing key targets and KEGG pathway. The methods applied to TCM should correspond to the synergistic mechanism.


Buddlejae Flos has a long history of utilization by humans to treat ophthalmic diseases. Although in vitro study revealed that it can be used for treating cataract, the bioactive components and the mechanism of efficacy remained unclear. This study aims to discover the bioactive components and mode of efficacy of Buddlejae Flos in cataract treatment. R language was utilized to execute Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis. Molecular docking was done through Schrödinger Maestro software utilization. Luteolin's (LUT) effect on cataract induced by sodium selenite in rat pups was evaluated.  Several databases were screened for bioactive components and corresponding targets, as well as cataract-related targets. Using the String database, common targets were determined and utilized to construct protein-protein interactions (PPI). The drug-component-target-disease network map was drawn using Cytoscape software.  Six bioactive components with 38 common targets were identified as being associated with cataract. TP53, AKT1, EGFR, CASP3, TNF, ESR1, INS, IL6, HIF1A, and VEGFA were identified as core targets in PPI analysis, and the binding energy of LUT with AKT was the lowest. LUT has been demonstrated to significantly lower MDA levels, raise glutathione (GSH) levels, and boost the activity of antioxidant enzymes like GST, SOD, GPx, and CAT. After LUT treatment, TNF-a, IL-2, and IL-6 levels were significantly lowered. Bcl-2 mRNA expression levels and p-PI3K and p-AKT protein expression were significantly elevated. In contrast, caspase-3 and Bax mRNA expression levels were significantly decreased.  This study demonstrates that LUT is a possible bioactive component that may be utilized for cataract treatment. Its mode of action includes oxidative stress suppression, reducing inflammation, and inhibiting apoptosis via regulating the PI3K/AKT single pathway. According to previously published American Academy of Ophthalmology (AAO) clinical guideline, cataract is an ophthalmic disease characterized by visual impairment due to degradation of lens optical properties, which is highly age-related . A meta-Previous research has revealed that cataract has been categorized according to their etiology as age-related, congenital, and juvenile, or secondary to disease, trauma, drug, ultraviolet (UV), and associated factors such as smoking and drinking. Several studies have also disclosed that cataract may be associated with high blood pressure, obesity, autoimmune diseases, chronic kidney disease, and diabetes , 11. In Buddlejae Flos has a long history of utilization in TCM for treating ophthalmic diseases because of its effect on clearing heat and purging fire, nourishing the liver, and brightening eyesight (TCM terminology). The previous study has demonstrated that flavonoids or their glycosides in 70% methanol extracts of Buddlejae Flos exhibited significant aldose reductase inhibitory activity in vitro, suggesting that its possible components may be a potential cataract treatment drug [Buddlejae Flos in cataract treatment have not been elucidated, limiting its clinical application.It is worth mentioning that about 44 medicinal plants/natural items have been utilized for treating cataracts in various traditional and folk medicine systems, including ayurveda, traditional Chinese medicine (TCM), and Korean traditional medicine . Buddlejent drug . HoweverBuddlejae Flos. GO enrichment, KEGG enrichment, and PPI were utilized to further analyze the common targets of Buddlejae Flos and cataracts. The corresponding targets and bioactive components were then screened using molecular docking, and the results were finally validated by the sodium selenite-induced cataract model in rats.In this study, network pharmacology was first utilized for screening bioactive components and corresponding targets of https://tcmspw.com/tcmspsearch.php) database was employed for screening the bioactive components of Buddlejae Flos [The Traditional Chinese Medicine Systems Pharmacology . To merge search results and delete duplicate records, the UniProt database was utilized (https://www.uniprot.org/).The Swiss Target Prediction database was utilized to collect corresponding targets of bioactive components in http://www.genecards.org/), Online Mendelian Inheritance in Man database , and DisGeNET database (https://www.disgenet.org/) were utilized to search for potential targets linked to cataract using the search term “cataract” (UMLS CUI : C0029531). Additionally, the known targets of drugs for treating cataract recorded in the DrugBank database (https://www.drugbank.ca/), including PTGS1 and PTGS2, were collected for further analysis.GeneCards database (Buddlejae Flos bioactive components and cataract-related targets (https://bioinfogp.cnb.csic.es/tools/venny/). Cytoscape software (version 3.7.2) was utilized to visualize the drug-component-target-disease network. The String database (https://string-db.org/cgi/input.pl) was utilized for establishing PPI network for common targets with a minimum requisite interaction score of >0.4 setting. PPI network's parameters, including betweenness centrality (BC), node degree (degree), and closeness centrality (CC), were computed and presented in a histogram and 3D scatter diagram.The Venn diagram developed by the online tool Venny 2.1 was utilized to visualize the common targets between R package was utilized to accomplish GO and KEGG enrichment analyses while P-value < 0.05 or QValue < 0.05 was considered to be significantly enriched. R packages “Enrichlot” and “ggplot2” were then utilized to visualize enrichment findings, which were showcased in a bar chart and a bubble chart. Finally, the core targets in the graphics of critical signaling pathways were highlighted in red using “Pathview” R package. R software 3.6.2 (x64) was utilized to accomplish all preceding stages [The “clusterProfile” g stages .https://pubchem.ncbi.nlm.nih.gov/). After minimizing energy and optimizing by Schrodinger software, the standard pdpqt files were saved as a ligand. The PDB database was utilized to download the protein structures of core common targets (http://www.rcsb.org/). After removing water and other unrelated molecules, the protein structures were processed using Schrodinger software with a protein preparation wizard module via hydrogenation, calculation of charge, and combination of nonpolar hydrogen, and finally saved as a receptor. Subsequently, the grid box coordinates were set, and the active site was determined in accordance with each protein's natural ligand. The box size was set as 40 × 40 × 40 grid points, with a distance of 0.1 nm between the tiny grid points. Finally, using default software parameters, the core bioactive compounds were docked with the common core targets by flexible docking. The complex compound with protein was visualized using Pymol software .Molecular docking of core bioactive compounds  and core common targets  were performed using Schrödinger Maestro software suite  , 27. Briα (TNF-α), interleukin-2 (IL-2), and interleukin-6 (IL-6) . TIANGEN Biotech provided the Fast Quant RT Kit . YiShan Biotech provided a RNA-Quick Purification Kit . Zen bioscience  provided the GAPDH antibody and HRP-Goat anti-Mouse, while Abcam provided anti-PI3K, anti-AKT, anti-p-PI3K, and anti-p-AKT . All other chemicals were of analytical grade.From Nanjing Dasf Biotechnology Co. Ltd, LUT was purchased (purity ≥98%) . Sodium selenite (purity ≥99%) was purchased from Sigma-Aldrich (Shanghai) Trading Co., Ltd . The Nanjing Jiancheng Bioengineering Institute provided the following kits: superoxide dismutase (SOD), malondialdehyde (MDA), reduced glutathione (GSH), glutathione S-transferase (GST), glutathione peroxidase (Gpx), catalase (CAT), tumor necrosis factor-Grade SPF Sprague-Dawley rat pups  were maintained in a clean grade facility at the Jiangxi Medical College's Experimental Animal Center . All animals were kept on 12-hour light/12-hour darkness cycles and had unrestricted access to tap water and standard rat food . Jiangxi Medical College's Animal Protection Research Ethics Committee authorized the study (2020090401).n = 10). Rats in the model group and three treatment groups with different doses of LUT were injected subcutaneously with sodium selenite solution  in the cervical region at 10, 12, and 14 days of age while rats administered a subcutaneous injection of normal saline simultaneously. From 10 days of age, for three weeks, the rats in control and model groups received normal saline by oral gavage. LUT received by oral gavage to rats in low-dose (L-LUT), medium-dose (M-LUT), and high-dose (H-LUT) groups at dosages of 50, 100, and 200 mg·kg−1, respectively.After adaptive feeding for three days, 10-day-old SD rats were allocated to five groups randomly  .Half an hour after the last oral gavage, the rats were anesthetized with isoflurane, and the aqueous humor from both eyes was collected and pooled using a microinjector. Subsequently, all rats were sacrificed by prompt dislocation of the neck vertebra under deep anesthesia with isoflurane. Lens and retinas from both eyes were isolated, washed three times in ice-cold phosphate buffer saline (PBS). The aqueous humor and lens samples were kept at −80°C until they were analyzed, while the retina samples were kept in 10% neutralized formalin for subsequent paraffin embedding and histological examination.Lens samples were immersed in PBS and homogenized for 60 s at 60 Hz with a tissue homogenizer . The supernatant was collected by homogenate centrifugation for 10 min at 3500 r/min at 4°C. The BCA protein concentration determination kit was utilized for measuring total protein concentration. MDA and GSH levels, as well as the activity of GST, SOD, CAT, and GPx, were quantified using commercial kits in accordance with manufacturer's instructions.α, IL-2, and IL-6 levels in aqueous humor were quantified through enzyme-linked immunosorbent assay (ELISA) kit usage as directed by manufacturer's instructions.TNF-μm segments, and stained with H&E for 48 h of fixation in 10% neutralized formalin. Under an optical microscope, the specimens' histological diagnostic and microscopic characteristics were observed and photographed .The histopathology of the retina was evaluated using hematoxylin and eosin staining (H&E). The retina was embedded in paraffin, cut into 4 μg total RNA, cDNA was synthesized and utilized for RT-PCR. The following RT-PCR was performed: Step 1 : 95°C for 10 min, Step 2 : 95°C for 10 s, and Step 3 : 72°C for 30 s for 39 cycles. As an internal control, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was utilized. The LUT effect on relative mRNA gene expression was computed utilizing 2−ΔΔcq method. The primer sequences utilized in this study are summarized in RNA-Quick Purification Kit was utilized for the total mRNA of lens extraction. Subsequently, from 2 J software was utilized to quantify protein bands.Total protein was isolated from the lens and utilized for evaluating p-AKT, PI3K, AKT, and p-PI3K protein expression levels. The BCA protein assay kit was utilized to determine the total protein concentration. Thereafter, SDS-PAGE with tris-glycine SDS was utilized as the running buffer and the proteins were separated before being transferred to a PVDF membrane at 80 mA for 1 h utilizing tris-glycine SDS transfer buffer mixed with 20% methanol. In turn, the membrane was blocked with 5% skimmed milk and incubated with antibodies overnight  on a 4°C cradle. Subsequently, following rinsing of the membrane with TBST, secondary antibodies were incubated with the membrane for 1 h at 37°C (1 : 5000). Following that, the membranes were stained with a western bright dye to allow faster and reversible identification of protein bands. Image P < 0.05 was deemed statistically significant.The mean standard deviation (SD) of all data was computed and analyzed utilizing SPSS software (version 19.0). One-way analysis of variance (ANOVA) was performed for comparing different groups, and TCMSP database was utilized for 46 bioactive components examinations. However, only four of them met the screening criteria, including butyrospermyl acetate, linarin, luteolin, and acacetin. Additionally, another three bioactive components were identified according to the published articles listed above, including hesperetin, apigenin, and acteoside. Butyrospermyl acetate was excluded from further analysis due to unclear chemical abstracts service (CAS) number and the absence of any disease and target association information in the TCMSP database. Detailed information on the six bioactive components involved in the subsequent analysis is listed in TCMSP and Swiss Target Prediction databases were utilized for collecting 174 and 423 corresponding targets of bioactive components, respectively. After standardization and removing duplicates using the UniProt database, 226 targets were recognized as bioactive components related targets for subsequent analysis. Cataract-related targets were obtained from multiple databases, such as 49 targets from OMIM database, 579 targets from DisGeNET database, 6719 targets from GeneCards database, and 2 targets from DrugBank database. After cleaning the data by de-duplication, 903 targets were recognized as cataract-related targets. A Venn diagram  shows thPPI network, with 38 nodes and 283 edges, was established based on the common targets of bioactive component-related targets and cataract-related targets . The BC,The 38 common targets of GO enrichment analysis are illustrated in σ-π conjugated interaction between the benzene ring of LUT and Val-164 (a hydrophobic amino acid of AKT1) were observed, indicating strong hydrophobic interaction between LUT and the active pocket of proteins, contributing to conformation stability. Overall, all this evidence shows that LUT has the unique advantages in the effective interaction with protein target and is likely to be a potential bioactive component of Buddlejae Flos in cataract treatment by interfering with AKT-related signaling pathways.The docking results of core bioactive compounds  and core common protein targets  are shown in As displayed in Figures As illustrated in Figures α, IL-2, and IL-6 concentrations in aqueous humor of the model group were significantly elevated, indicating that the inflammation level was significantly elevated following sodium selenite treatment. After 3-week LUT treatment, TNF-α, IL-2, and IL-6 concentrations in all dose groups were significantly decreased comparing to the model group. The results revealed that three doses of LUT could significantly mitigate the level of inflammation (Figures In comparison to the control group, TNF- Figures .Histological examination of the retina using H&E staining is presented in Caspase-3 and Bax mRNA expression levels were significantly elevated in the model group, whereas Bcl-2 expression levels were significantly lowered in comparison to the control group. As illustrated in Figures Western blotting analysis was utilized for studying the protein expression levels of PI3K, AKT, p-PI3K, and p-AKT in the lens. As depicted in Buddlejae Flos in cataract treatment using network pharmacology and molecular docking. The most inspiring aspect of the present study is that LUT has been identified as a potential bioactive component in Buddlejae Flos and may play a therapeutic role in cataract by intervening PI3K, AKT, caspase-3, Bax, Bcl-2, TNF-α, IL-2, and IL-6. This conclusion was ultimately confirmed using a sodium selenite-induced cataract model in rats. The Buddlejae Flos mechanism in treating cataract includes oxidative stress suppression, inhibiting apoptosis, and mitigating inflammation by regulating the PI3K/AKT single pathway.In this study, the initial step was to discover the bioactive components and targets of Astragalus membranaceus in treating the diabetic retinopathy, and the PI3K-Akt signaling pathway role has also been highlighted [Buddlejae Flos and cataract was investigated for the first time in the current study by the molecular docking, and the network pharmacology was conducted to reveal binding conformations of bioactive components and targets. Flavonoids exhibit unique advantages in molecular docking due to their abundant active functional groups, which are more likely to form stronger hydrogen bonds with the binding pocket amino acid residues of the protein [Recently, network pharmacology has been increasingly utilized in the study of TCM to greatly accelerate the discovery of drugs and clarify the mechanism of action –31. Howehlighted . The ass protein , 35. The protein –38. In tPrevious studies have revealed that PI3K transmits essential signals that regulate an assortment of physiological processes in virtually every type of tissue studied to date, including inflammation, cancer, immune deficiency, tissue overgrowth, and cellular metabolism , 40. AKTBuddlejae Flos which is capable of treating cataract. Its mode of action includes oxidative stress suppression, alleviating inflammation, and preventing apoptosis via regulating the PI3K/AKT single pathway.In conclusion, our study demonstrates that LUT is a potential bioactive component of


Baihe Zhimu decoction (BZD) has significant antidepressant properties and is widely used to treat mental diseases. However, the multitarget mechanism of BZD in postpartum depression (PPD) remains to be elucidated. Therefore, the aim of this study was to explore the molecular mechanisms of BDZ in treating PPD using network pharmacology and molecular docking. Active components and their target proteins were screened from the traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The PPD-related targets were obtained from the OMIM, CTD, and GeneCards databases. After overlap, the targets of BZD against PPD were collected. Protein–protein interaction (PPI) network and core target analyses were conducted using the STRING network platform and Cytoscape software. Moreover, molecular docking methods were used to confirm the high affinity between BZD and targets. Finally, the DAVID online tool was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping targets. The TCMSP database showed that BZD contained 23 active ingredients in PPD. KEGG analysis showed that overlapping genes were mainly enriched in HIF-1, dopaminergic synapses, estrogen, and serotonergic synaptic signalling pathways. Combining the PPI network and KEGG enrichment analysis, we found that ESR1, MAOA, NR3C1, VEGFA, and mTOR were the key targets of PPD. In addition, molecular docking confirmed the high affinity between BZD and the PPD target. Verified by a network pharmacology approach based on data mining and molecular docking methods, the multi-target drug BZD may serve as a promising therapeutic candidate for PPD, but further in vivo/in vitro experiments are needed. The rate of PPD among women may be as high as 15% and result in a high death rate from self-harm. The rising numbers could make matters worse.–5 Thus, PPD is a significant public health problem.Postpartum depression (PPD) can be defined as non-psychotic depression occurring within a year of childbirth, characterized by low mood, unusual thoughts, feelings of guilt, unexplained anxiety, worthlessness, and other depressive symptoms.,7 Hormonal, genetic, and psychological effects can all lead to PPD, resulting in a series of physical, mental, and behavioral changes. There is no consensus on drugs for the treatment of PPD, although there are reports of the use of antidepressants and antipsychotics in severe cases. Big data fails to confirm whether antidepressant treatment produces adverse effects on breastfed infants.,11However, the aetiology of PPD remains unclear. Various factors are associated with PPD, such as low socioeconomic status, prenatal depression, cultural factors, medication use, excessive stress, and anxiety. In ancient China, TCM was used to treat many mental illnesses, such as restlessness, lily disease, and depression. Reports show that many TCMs, such as Baihe Zhimu decoction (BZD), Chaihu decoction, and Zhizichi decoction Xiaoyao san, are used to treat PPD.Traditional Chinese medicine (TCM) is an oriental traditional medicine that is characterized by a holistic concept. It was the main medical method used in ancient China, with thousands of years of accumulated practical experience. Some studies have shown that BZD can increase 5-hydroxytryptamine, norepinephrine, and dopamine levels and improve the behavioral indicators of depression.–17 Clinical data indicate that Morita therapy combined with BZD can improve the symptoms, quality of life, and ability of daily living for first-episode depression, which may be related to the decrease in serum BDNF and an increase in DOPAC levels. However, the potential mechanisms underlying the treatment of PPD are not fully understood.BZD is a classic traditional Chinese medicine prescription, which was first recorded in Treatise on Febrile and Miscellaneous Diseases (200–210 ad). Consisting of two herbs, Lilii Bulbus (Baihe BH) and Anemarrhenae Rhizoma (Zhimu ZM), BZD has become a classic Chinese medicine formulation for treating depression, insomnia, anxiety and other mental and neurological diseases.–21Network pharmacology is an emerging method that analyses the components, targets, diseases, and related pathways of traditional Chinese medicine. It combines pharmacology, molecular biology, electronic technology, and bioinformatics and has great advantages for studying the complex components, targets, and pathways of traditional Chinese medicine prescriptions.Therefore, this research aims to apply network pharmacology to identify the active ingredients and targets of BZD and to discover the common targets and possible signaling pathways of BZD in the treatment of PPD. The corresponding workflow is shown in Figure https://tcmspw.com/tcmsp.php) and then screened by integrating oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18). Next, each target of the active ingredient was obtained from this website. The aggregated target was input into UniProt (https://www.uniprot.org/) to obtain information, such as gene symbols and gene IDs.,25The components of the two herbs were found in the pharmacology of Traditional Chinese Medicine Systems Pharmacology , Comparative Toxicogenomics Database , and GeneCards (https://www.genecards.org/).,29 We searched for “postpartum depression” in the GeneCards, CTD, and OMIM databases. Finally, all targets were unified as gene names on UniProt.PPD targets were obtained from the Online Mendelian Inheritance in Man  to obtain their intersection with a Venn diagram.The targets of BZD and PPD-related target gene lists were input into Venny 2.1.0 . According to the corresponding calculation method, the species was set to “Homo sapiens,” and the confidence score was set to >0.4.A protein–protein interaction (PPI) network for disease and drug mapping targets was constructed using the STRING database (http://david.abcc.ncifcrf.gov/),32 was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.The Database for Annotation, Visualization, and Integrated Discovery  was used to obtain the 2D chemical structures of the small-molecule ligands. ChemOffice software was used to construct the 3D chemical structures of small molecular ligands, and the 3D chemical structures of protein receptors were acquired from PDB (http://www.rcsb.org/). After the molecular ligands and water molecules of the protein receptors were removed using PyMol 2.4.0 software (https://pymol.org.), the format of the protein receptors and small-molecule ligands was transformed into pdbqt format. AutoDock Vina was used for molecular docking. Based on the binding energy value, the lowest binding energy value was selected as the docking affinity. Finally, PyMol software was used to visualize the 3D structures of the molecular ligand and protein receptor bonding.Based on the results of the GO and KEGG pathway analyses, we selected the key protein receptor and ligand associated with the protein receptor. PubChem , and Zhimu had 81 (49.1%) components. The values of OB and DL (OB ≥ 30% and DL ≥ 0.18) were used to screen potential active compounds from Baihe and Zhimu, and 22 active compounds met the screening standards. The properties of the compounds are listed in Table We searched for PPD targets in several publicly available databases: CTD, OMIM, and GeneCards. After deleting duplicate values, we finally obtained 663 disease targets.The drugs, components, and targets of BZD were drawn into a drug-component-target network diagram using the Cytoscape software to show the relationship between them more intuitively Fig. . In thisFigure Figure P < .01) were response to drugs (GO:0042493), oxidation-reduction process (GO:0055114), response to lipopolysaccharide (GO:0032496), response to estradiol (GO:0032355), and response to hypoxia (GO:0001666)  were caveola (GO:0005901), plasma membrane (GO:0005886), extracellular space (GO:0005615), peroxisome (GO:0005777), and perinuclear region of the cytoplasm (GO:0048471)  were enzyme binding (GO:0019899), protein homodimerization activity (GO:0042803), steroid binding (GO:0005496), steroid hormone receptor activity (GO:0003707), and heme binding (GO:0020037) , molecular function (MF), and cell component (CC) obtained by DAVID 6.8 for 41 selected target genes are shown in Figure ess GO:005114, resGO:004249, oxidatiAccording to the five related signaling pathways from the results of KEGG pathway enrichment analysis, we identified five key genes: ESR1, MAOA, NR3C1, VEGFA, and MTOR. These key genes were mainly bound to diosgenin, isopimaric acid, stigmasterol, and beta-sitosterol, which are the main components of BZDs Table . The molPPD is a common and serious mental health problem that causes personal suffering and interferes with parenting. However, the effects of antidepressant medications remain controversial. In the meantime, numerous people worry about the potential adverse effects of antidepressant medications. TCM has been used to treat mental illness in China for thousands of years. Therefore, the application of Chinese medicine can help develop new ideas and methods for the treatment of PPD.Owing to the multi-target treatment effects of TCM, it can serve as a significant repository to develop drugs for the treatment of PPD. This study utilized network pharmacology and molecular docking simulations to reveal the molecular mechanisms of BZD in PPD treatment. BZD plays a potential role in treating PPD by regulating multiple target genes and pathways.ESR1, MAOA, NR3C1, VEGFA, and mTOR. Moreover, GO annotation and KEGG pathway enrichment analyses confirmed that these target genes were associated with HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways, which are closely involved in the treatment of PPD. Molecular docking showed that the five core targets had a certain affinity for the main compounds of BZD.For this study, we selected 22 main compounds of BZD, among which icariin, timosaponin B-III (TB-III), and others are shown to have antidepressant effects. The potential target genes are  Icariin has potential preventive and therapeutic effects in various neurological diseases such as cerebral ischemia, depression, Parkinson’s disease (PD), and multiple sclerosis (MS). The mechanism by which icariin improves depression may be related to the promotion of cell proliferation, peripheral nerve regeneration, improvement of the function of damaged nerve regulation, decrease in glucocorticoid receptors (GRs) and 5-hydroxytryptamine 1A (5-HTR1A) receptors in the hippocampus and prefrontal cortex, regulation of the central neuroendocrine system, or restoration of the negative feedback regulation of the hypothalamic-pituitary-adrenal (HPA) axis.–42 Furthermore, icariin may ameliorate prenatal restraint stress-induced depression-like behavior.TB-III is a steroidal saponin isolated from the rhizome of Baihe, which exhibits antidepressant activity through the regulation of inflammatory cytokines, BDNF signaling, and synaptic plasticity. However, this study was only conducted in a mouse model of PPD, and there have been no human reports.ESR1, MAOA, NR3C1, VEGFA, and mTOR. Simultaneously, the PPI network results showed that these targets had close interactions. ESR1 plays an important role in mediating hormonal differences during pregnancy and postpartum. One clinical experiment suggested a role for ESR1 in the etiology of PPD, possibly through modulation of serotonin signaling. Previous work has demonstrated that exposure to and withdrawal from normal levels of gonadal steroids results in depressive symptomatology in women previously diagnosed with PPD.,46 The MAOA gene, located on the short arm of the X-chromosome (Xp11.4-p11.3), has been the focus of research in the field of mental disorders in recent years. Two studies showed that MAOA was positive at 6 weeks postpartum with PPD.,48 Women with PPD appear to have an abnormal HPA axis response to stress, which may involve genetic variants, as reported previously. The NR3C1 gene, which is located on the long arm of chromosome 5 (5q31), encodes the GR. Epigenetic studies have shown that decreased NR3C1 gene expression activity caused by methylation can impair the negative feedback regulation of the HPA axis, alter an individual’s response to SLE, and induce depression. However, the mechanisms of interaction between these targets are not clear. Other targets have not been directly documented to improve PPD, but the literature supports an improvement in depression, which follows the same pathway as PPD. For example, VEGFA can affect the complex processes of learning and memory and plays a role in regulating neurite growth and maturation during brain development. The role of VEGFA in neurogenesis may be mediated by its interactions with downstream effector genes. In the present study, our data showed that VEGF mRNA and protein expression in hippocampal tissues and serum were downregulated in depression model rats, suggesting that downregulation of VEGFA plays a key role in depression in rats. mTOR is a serine/threonine kinase that controls related signalling pathways to regulate many integrated physiological functions of the nervous system. Many studies have shown that it is tempting to hypothesize that the activation of mTOR function followed by enhanced mTOR-dependent protein synthesis may underlie the action of antidepressants, such as ketamine.–56The network pharmacology results confirmed that the potential target genes of PPD regulated by BZD were mainly  The estrogen signaling pathway plays an important role in PPD. Studies have shown that changes in gonadal steroid levels, including estrogen levels, may contribute to the depressive symptoms of PPD. Other data suggest that PPD symptoms are affected by estrogen levels.,60 Serotonin regulates several basic biological functions relevant to depression, including sleep and appetite. Women with PPD have lower plasma serotonin levels than non-depressed controls, which are modulated by estrogen. Thus, fluctuating estradiol levels during pregnancy and the postpartum period may cause depressive symptoms in vulnerable women by destabilizing the serotonin system. Dopamine is recognized and proven to be an important factor, and diminished dopaminergic function may also play a role in PPD. Studies have shown that dopamine activity and estradiol levels are positively correlated; therefore, they can synergistically affect PPD symptoms.GO annotation and pathway enrichment analyses were conducted to identify the potential biological functions of PPD targets. GO enrichment analysis revealed that the major biological processes included response to drugs, oxidation-reduction processes, response to lipopolysaccharide, response to estradiol, and response to hypoxia. The results of the KEGG enrichment analysis showed that HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways were the leading signaling pathways for the treatment of PPD by BZD. These data suggest that the HIF-1 pathway might play an important role in antidepressant effects, and that altered mRNA expression of HIF-1 and its target genes in peripheral blood cells is associated mainly in a state-dependent manner with mood disorders, especially major depressive disorder (MDD).Molecular docking verified that ESR1, MAOA, NR3C1, VEGFA, and mTOR have high affinities for the main active ingredients of BZD, diosgenin, isopimaric acid, stigmasterol, and beta-sitosterol, providing data support for BZD as a potential drug for the treatment of PD; however, further experimental studies are needed to confirm its effectiveness.In this study, we used network pharmacology combined with molecular docking to elucidate the mechanism by which BZD regulates PPD through multiple targets and channels. The main target proteins of BZD in PPD treatment have been shown, constructing a target protein network. BZD mainly affected HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways while regulating the key target proteins of ESR1, MAOA, NR3C1, VEGFA, and mTOR.However, this article also has some limitations. The composition of traditional Chinese medicine is complex, the data of this article is obtained through some databases, and the information of the databases needs to be further improved. The validity of this article lacks experimental support, and its mechanism needs further experimental verification.Qiong Zhao, Wengu Pan, and Guomin Si performed the main analyses and drafted the manuscript. Hongshuo Shi and Fanghua Qi designed the study. Yuan Liu helped with the introduction and discussion. Tiantian Yang and Hao Si assisted in the preparation of the manuscript. All authors wrote, read, and approved the manuscript.Conceptualization: Guomin Si.Data curation: Hongshuo Shi, Fanghua Qi and Hao Si.Formal analysis: Qiong Zhao, Wengu Pan, Yuan Liu, Tiantian Yang.Funding acquisition: Qiong Zhao, Wengu Pan.Investigation: Qiong Zhao, Wengu Pan, Guomin Si, Fanghua Qi.Methodology: Qiong Zhao, Guomin Si, Tiantian Yang.Project administration: Guomin Si.Resources: Qiong Zhao, Wengu Pan, Hongshuo Shi, Fanghua Qi, Yuan Liu, Tiantian Yang, and Hao SiSoftware: Qiong Zhao, Hao Si.Supervision: Guomin Si.Validation: Guomin Si, Wengu Pan, Hongshuo Shi.Visualization: Wengu Pan, Hongshuo Shi.Writing – original draft: Qiong Zhao, Wengu Pan, Guomin Si, Fanghua Qi.Writing – review & editing: Qiong Zhao, Guomin Si.