Our previous studies demonstrated that the FOXM1 pathway is upregulated and the PPARA pathway downregulated in breast cancer (BC), and especially in the triple negative breast cancer (TNBC) subtype. Targeting the two pathways may offer potential therapeutic strategies to treat BC, especially TNBC which has the fewest effective therapies available among all BC subtypes. In this study we identified small molecule compounds that could modulate the PPARA and FOXM1 pathways in BC using two methods. In the first method, data were initially curated from the Connectivity Map (CMAP) database, which provides the gene expression profiles of MCF7 cells treated with different compounds as well as paired controls. We then calculated the changes in the FOXM1 and PPARA pathway activities from the compound-induced gene expression profiles under each treatment to identify compounds that produced a decreased activity in the FOXM1 pathway or an increased activity in the PPARA pathway. In the second method, the CMAP database tool was used to identify compounds that could reverse the expression pattern of the two pathways in MCF7 cells. Compounds identified as repressing the FOXM1 pathway or activating the PPARA pathway by the two methods were compared. We identified 19 common compounds that could decrease the FOXM1 pathway activity scores and reverse the FOXM1 pathway expression pattern, and 13 common compounds that could increase the PPARA pathway activity scores and reverse the PPARA pathway expression pattern. It may be of interest to validate these compounds experimentally to further investigate their effects on TNBCs. Breast cancer (BC) is the most common cancer and the second leading cause of death from cancer among Canadian women, accounting for 25% of all new cancer cases and 13% of cancer-related deaths . PatientIn one of our previous studies , the GenBRCA2, CDKN2A, CHEK2 and RB1), the proto-oncogene family MYC, genes encoding cyclins , genes encoding cyclin-dependent kinases , ESR1, NEK2 as well as FOXM1 itself. FOXM1 is one of the most important oncogenic TFs and it is overexpressed in many human cancers [Beclin-1 and LC3 genes in TNBCs [DEPDC1) was over-expressed in human TNBCs relative to their paired neighboring non-cancerous tissues using two public data sets from Gene Expression Omnibus (GEO) [The FOXM1 pathway is a predefined pathway extracted from the C2 category gene sets in the MSigDB . The FOX cancers . It regu cancers . Not sur cancers . Xue and cancers . The up- cancers . In addi cancers  and is i cancers . FOXM1 p cancers . FOXM1 ain TNBCs . Increasin TNBCs . In a rein TNBCs . They fuin TNBCs . Zhang eus (GEO) . Stable us (GEO) . Taken tRB1 and PIK3R1, the proto-oncogenes MYC and JUN, as well as transcription factors CITED2 and PPARA. The stimulation of the PPARA pathway increases the volume and number of peroxisomes which are responsible for, among other things, lipid metabolism and catabolism. Genes in this pathway are regulated by the PPARA transcription factor. Previous studies suggest that PPARA is a tumor suppressor in some cancers, including melanoma [LPL, SORBS1, PPARG, PLIN, FABP4, AQP7, CD36, and ADIPOQ that are involved in the PPARA signaling pathway may also inhibit the pathway and contribute to breast tumor progression [The PPARA pathway is a predefined pathway extracted from the C2 category gene sets in the MSigDB database . This pamelanoma  and gliomelanoma . PPARA amelanoma . Moreovegression . Recentlgression . To studgression . The adigression . Thus, agression .The findings from our previous studies together with the published literature strongly support the roles of the FOXM1 and PPARA pathways in BC and especially TNBC tumorigenesis. Therefore, identifying compounds that can modulate these two pathways may provide novel therapeutic strategies for BC, and, in particular, TNBC. The purpose of this study was to investigate the suppression of the FOXM1 pathway and the stimulation of the PPARA pathway on BC cell lines with various compounds.http://www.broadinstitute.org/cmap), which includes 3095 drug-induced gene expression instances (treatment vs. vehicle control pairs) from MCF7 cells treated with 1294 bioactive small chemical molecules at varying concentrations. The raw CEL files were downloaded. Since the CMAP database is based on three different Affymetrix chip types , the microarray data were then grouped according to the platforms and the Robust Multichip Average (RMA) [ComBat function in the R package sva [The overall approach of this study is depicted in Fig. ge (RMA)  method whttps://gdac.broadinstitute.org), multiplied by 106 to obtain Transcripts Per Million (TPM) [For the TCGA breast tumor data, we used the estimated baseline expression  of genes computed by the RSEM method  providedon (TPM)  and log2The GEO data set GSE48213 contains molecular profiles  from a collection of 84 BC cell lines reported in Daemen et al.’s publication . In thisThree gene signatures were constructed based on the GSEA results from the FOXM1 and PPARA pathways in our previous study  Fig. 1b1b. A typGiven that the FOXM1 pathway is the top upregulated pathway, while the PPARA pathway is the top downregulated pathway in TNBC versus normal breast tissue, the signatures from both pathways were simplified into a single FOXM1/PPARA-pathway signature by selecting genes that are most responsible for up-regulation of the FOXM1 pathway and down-regulation of the PPARA pathway Fig. . AccordiDoDART function from the De-noising Algorithm based on Relevance network Topology (DART) [DoDART function performed the following steps to calculate pathway activity scores using the DART algorithm: 1) constructing the relevance network where nodes are genes in the signature and edges represent the correlation among genes based on the expression profiles. The significance of the correlation between a gene pairs is defined using the default FDR value of 1.0 ×10−6, which is stringent and represents a conservative Bonferroni threshold that assumes that a typical signature consists of on the order of 100 genes thereby necessitating an estimated 10,000 pairwise gene correlations; 2) evaluating the consistency of the gene-gene correlations  in the relevance network with the prior gene-gene correlations contained in the given signature; 3) filtering out edges that are inconsistent with the prior information contained in the given signature from the relevance network; and 4) estimating an activity score of the given signature for each individual sample using its expression profile. A high positive pathway activity score indicates the stimulation of the pathway activity in a given sample while high negative score indicates the repression of the pathway activity.To calculate the activity score of the FOXM1- and PPARA-pathway signatures, the y (DART)  R packagConsidering the difference between cancer tissue and cell lines in terms of transcriptional profiles, we asked whether the activity scores of the FOXM1- and PPARA-pathway signatures across different BC subtypes showed a similar pattern between the BC tissue and cell lines. We computed the activity scores of the two signatures for the BC cell line samples from the GSE48213 gene expression data set and also for BC tissue samples from the TCGA BC gene expression data set. The activity scores of the two pathways in the BC tissue and cell lines were normalized into the range of 0 to 1 for easy comparison. For each signature, we performed the ANOVA test to check the difference of activity score in three BC cell line subtypes  in GSE48213. To compare the means of the signature activity score between any two BC cell line subtypes in GSE48213, we performed pairwise t-tests between every two subtypes followed by Benjamini–Hochberg correction for multiple testing. For each signature, we also performed the ANOVA test to check the difference of the signature activity score in five BC tissue sample groups  in TCGA. We performed pairwise t-tests between BC tissue pairs followed by Benjamini–Hochberg correction for multiple testing to compare the means of the pathway activity scores between BC tissue sample groups in TCGA.DoDART function to the CMAP perturbational expression profiles and calculated the FOXM1- and PPARA-pathway signature activity scores for each CMAP instance   Fig. . In the ASd) as the activity score in the treatment (ASt) minus the activity score in the control (ASc)  (Eq. ).1\documASd is the degree of change in pathway activity score caused by the corresponding treatment, while the sign of ASd is the direction of that change. Therefore, a positive sign indicates the treatment increases the pathway activity while a negative sign decreases the pathway activity. We ordered the instances according to increasing difference score for the FOXM1 pathway but decreasing difference score for the PPARA pathway. Therefore, the top-ranked instances for the FOXM1 pathway are those which decrease the pathway activity while for PPARA pathway are those which increase the pathway activity. These top-ranked instances were further investigated.The magnitude of In comparison to drug treatments affecting pathway activity in the context of the change of pathway activity score between treated and control cell lines in CMAP, we also evaluated drug treatments in the context of modulating (mimicking or reversing) pathway expression patterns using the CMAP online tool Fig. . The thrGene lists for the three signatures are provided in Table Given the trend of up-regulation of the FOXM1 pathway and down-regulation of the PPARA pathway in TNBC, the signatures from both pathways were simplified into a single FOXM1/PPARA pathway signature by identifying genes that are most responsible for FOXM1 pathway up-regulation and PPARA pathway down-regulation. Among the 27 upregulated genes from the FOXM1 pathway, 21 were core members that contribute most to the pathway up-regulation in the GSEA results in our previous study  Supplem.p-value < 2.2 × 10−16)  Fig. . We foun16) Fig. . Among t16) Fig. . We notep-value = 0.003)  Fig. . Similar03) Fig. , the TNB03) Fig. . There ip-value < 2.2 × 10−16)   Fig. . The nor16) Fig. . However15) Fig. , similarASd) between treatment and control groups. Of the 1390 perturbational gene expression instances, 885 instances show a negative difference score for the FOXM1 pathway   between the treatment and the paired control groups. Of the 1390 drug-induced gene expression instances, 739 show a positive difference score for the PPARA pathway   . FurtherUp-regulation of the FOXM1 pathway and down-regulation of the PPARA pathway were found in BCs and in particular TNBCs. Therefore, the current study was aimed to identify compounds effective at repressing the FOXM1 pathway activity as well as those inducing the PPARA pathway activity in BC. The former included 5109870, MG-132, MG-262, celastrol, resveratrol, ciclopirox and cephaeline while the latter included anisomycin, cephaeline, pararosaniline cicloheximide and monensin. In addition, compounds decreasing the FOXM1 pathway activity while increasing the PPARA pathway activity concurrently were identified, including MG-262, MG-132, celastrol, ciclopirox and puromycin.Table S1Table S2Table S3


Kuntai capsules are effective in controlling primary ovarian insufficiency (POI). However, the precise mechanisms underlying the pharmacological effects of Kuntai capsules remain unclear. This study aimed to screen the active components and underlying mechanisms of Kuntai capsules for POI treatment using network pharmacology protocols and molecular docking technology. Potential active constituents in the chemical composition of Kuntai capsules were obtained from the Traditional Chinese Medicine System Pharmacology Database. Targets for POI were obtained from the Online Mendelian Inheritance in Man and Gene Cards database. All target data were integrated to identify the active ingredients of POI treatment. Enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery database. The STRING database and Cytoscape software were used for protein-protein interaction network construction and core target identification. Finally, a molecular docking analysis of the active components and core targets was performed. A total of 157 ingredients related to POI were identified. Enrichment analysis showed that these components might participate in the mitogen-activated protein kinase, tumor necrosis factor, phosphoinositide-3-kinase/AKT serine/threonine kinase 1, and forkhead box O signaling pathways. Further protein-protein interaction network analysis revealed that the core targets were Jun proto-oncogene, AKT serine/threonine kinase 1, tumor protein P53, interleukin 6, and the epidermal growth factor receptor. Molecular docking analysis showed that baicalein was the most active ingredient with the highest affinity for the core targets. This study identified baicalein as the core functional component and elucidated the potential pharmacological effects of Kuntai capsule in the treatment of POI. The etiology of POI includes genetic and iatrogenic factors, infections, and autoimmune disorders (ovarian surgery and chemotherapy).–4 A meta-analysis revealed that the worldwide incidence of POI in 2019 was 3.7%. Currently, there are no effective therapies for POI. Hormone replacement therapy (HRT) is the first-line therapy to improve low estrogen levels in patients with POI, which may prevent cardiovascular diseases and bone issues. However, the clinical use of HRT is limited by its side effects and contraindications, especially in patients with neoplastic or endocrine complications.,9 In addition, HRT has little effect on fertility, although many women with POI still have reproductive needs. Therefore, there is an urgent need to develop more effective replacement drugs for POI with fewer side effects.Primary ovarian insufficiency (POI) is an ovarian function disorder that occurs before the age of 40 years and causes permanent and irreversible damage to the ovarian function. The diagnostic criteria for POI are oligomenorrhea or amenorrhea at least 4 months before 40 years of age, with serum follicle-stimulating hormone levels > 25 IU/L (on 2 occasions at least 1 month apart).,11 Of these TCM, the Kuntai capsule, which originated from a modified Huanglian Ajiao decoction published by Shang Han Za Bing Lun, is an efficient intervention for ovarian dysfunction.,13 The Kuntai capsule is a compound herbal formula comprising 6 herbs: Radix Rehmanniae Praeparata (shudihuang), Rhizoma Coptidis (huanglian), Radix Paeonia Alba (baishao), Radix Scutellaria Baicalensis (huangqin), Poria Cocos (fuling), and donkey-hide gelatin (ejiao). A meta-analysis involving 12 randomized controlled trials revealed that combined treatment with Kuntai capsules and HRT is more effective than HRT alone for POI treatment, including improved serum levels of anti-Mullerian hormone, estradiol, follicle-stimulating hormone, and luteinizing hormone, lipid indexes, and ultrasonic results . Similarly, a previous study showed that combined therapy with Kuntai capsules and HRT had a higher therapeutic efficacy rate than HRT monotherapy. Moreover, clinical trials have indicated the benefits of Kuntai capsule administration for POI, especially in fertility treatment, including elevation of serum anti-Müllerian hormone levels, estrogen levels, antral follicle counts, increased spontaneous pregnancy rates, and improved assisted reproductive technology outcomes. Luo et al found that Kuntai capsules can improve ovarian function by maintaining follicles and activating autophagy using a cisplatin-induced POI rat model. However, the exact mechanism of Kuntai capsules in the treatment of POI is still unclear. Network pharmacology with molecular docking is a novel approach for revealing the potential mechanisms underlying the therapeutic effects of a drug through integrating drug-target-disease networks with several bioinformatics methods and binding ability verification.,18 Therefore, it is appropriate to elucidate the molecular mechanisms of Kuntai capsule in treatment of POI using network pharmacology with molecular docking, instead of conventional analytical strategies because the effects of Kuntai capsules on POI are complex and may result from multiple components acting on multiple targets and multiple pathways. To the best of our knowledge, no such studies have been reported to date.Traditional Chinese Medicine (TCM) has been used in combination with HRT to treat POI.To explore the potential mechanisms of Kuntai capsules involved in the treatment of POI, we screened the active components of Kuntai capsules as well as potential treatment targets of POI using a network pharmacology approach, and further verified the binding ability between components and targets by running molecular docking. This study revealed the most active component and therapeutic mechanisms of Kuntai capsules against POI, which may provide a new rationale for basic research and lead to new drug development for treating POI. A flow chart of the study is shown in Figure http://tcmspw.com/index.php) is a public pharmacology platform for TCM analysis that includes chemical, target, and drug-target networks. We collected the predicted targets of the active ingredients of Kuntai capsules using TCMSP. The criteria for screening active ingredients included a drug-likeness evaluation equal to or >0.18 and an oral bioavailability equal to or >30%. These 2 parameters are strongly correlated with the absorption, distribution, metabolism, and excretion features of the ingredients. We integrated the data and removed duplicates after obtaining the entire list of active ingredients.The TCM system pharmacology database and analysis platform (TCMSP), (https://www.genecards.org) is a public database that provides comprehensive information regarding human genes. The OMIM (http://www.omim.org/) is an authoritative resource for all Mendelian disorders and over 16,000 genes associated with them. We obtained POI-related target genes from the Gene Cards and OMIM databases. To identify the candidate targets of Kuntai capsules impacting POI, we combined the predicted targets of the active ingredients of Kuntai capsules and POI-related genes using the Venn diagram online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/). The Cytoscape software platform is an open-source network analysis and visualization tool for biological research. Ingredient-gene networks were obtained and visualized using the Cytoscape software.GeneCards (https://david.ncifcrf.gov/). The GO terms and KEGG pathway term clusters  were selected, and the top 20 terms were ranked according to the gene count.GO annotation describes a specific gene from different dimensions, including biological process, molecular function, and cellular component. KEGG pathway enrichment analysis provides a collection of pathway maps that represent the molecular interactions of several genes. GO term and KEGG pathway enrichment analyses were performed for candidate target genes using the Database for Annotation, Visualization, and Integrated Discovery (https://www.string-db.org/) is an online resource that integrates protein interaction information with confidence scores. The PPI network was established using the STRING database (version 11.5) (https://string-db.org/) and visualized using the Cytoscape software (version 3.8.2) (https://cytoscape.org/index.html). In the PPI network, 0.700 (high-confidence) interaction nodes were required, and the disconnected nodes were hidden. The PPI network was visualized using the Cytoscape software, and further analysis was performed using the Cytoscape plugin. CytoHubba is a plugin that explores important nodes  of a network using several algorithms. Hub proteins were identified based on the PPI network using CytoHubba version 0.1 (https://apps.cytoscape.org/apps/cytohubba) and Maximal Clique Centrality algorithms.STRING (http://www.rcsb.org/) was used to download the 3D structures of hub proteins. The molecular structures of quercetin, kaempferol, and baicalein were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Water molecules were removed from all protein and ingredient molecules, polar hydrogen atoms were added, and the samples were stored in PDBQT format. The AutodockVina 1.2.2 (http://autodock.scripps.edu) was used to analyze the binding affinities between the active ingredients and hub target proteins. The grid box parameters were as follows: box size, 30 × 30 × 30 Å; grid point distance, 0.05 nm.Molecular docking is a method for identifying and visualizing the interactions between receptors and ligands. The Protein Database (PDB) , AKT serine/threonine kinase 1 (AKT1), tumor protein P53 (TP53), interleukin 6 (IL-6), and epidermal growth factor receptor (EGFR) as the key treatment targets for POI.TCM has been used for thousands of years to treat and prevent illnesses.A total of 228 ingredients from each herb (except ejiao) of the Kuntai capsules were screened using TCMSP. To identify the Kuntai capsule ingredients involved in POI treatment, we obtained 5248 POI-related genes from GeneCards and OMIM and integrated them with 228 ingredients from TCMSP. Accordingly, 157 common molecules which may involve in the therapeutic effects of Kuntai capsules in treatment of POI were selected -catechin; 2R)-2-phenanthren-17-yl]-6-methylhept-5-enoic acid; (R)-canadine; 5,7,4’-trihydroxy-6-methoxyflavanone; 5,2’-dihydroxy-6,7,8-trimethoxyflavone; -3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione; 5,7,2,5-tetrahydroxy-8,6-dimethoxyflavone; 5,7,4’-trihydroxy-8-methoxyflavone; acacetin; baicalein; kaempferol; oroxylin a; paeoniflorin; quercetin; and stigmasterol. We constructed a network of drug ingredients-POI targets using the Cytoscape software, as shown in Figure R-2-[. It has been used to treat various diseases owing to its various pharmacological effects, such as antioxidant, anticancer, and anti-inflammatory effects. Baicalein has progesterone receptor antagonist and glucocorticoid receptor agonist activity in vitro, which may have a potent therapeutic effect on female reproductive system disease. Notably, these 3 components share the same pharmacological antioxidant activity. Moreover, it is increasingly believed that oxidative stress may contribute to POI pathogenesis. Patients with POI have increased plasma levels of advanced oxidation protein products, which induce reactive oxygen species generation, resulting in the inhibition of ovarian granulosa cell proliferation, which could lead to the occurrence of POI in vivo. Some antioxidants, such as melatonin, resveratrol, and dehydroepiandrosterone have considerable beneficial effects on the ovarian reserve by reducing oxidative stress.–42 Therefore, these results suggest that quercetin, kaempferol, and baicalein in Kuntai capsules may play important roles in the treatment of POI.The top 3 ingredients, which were the main components with the highest target connection, were identified in the drug ingredients-POI targets network as quercetin, kaempferol, and baicalein. Quercetin is a well-known antioxidant that can prevent primordial follicle loss in mice with iatrogenic POI induced by cyclophosphamide treatment through the phosphoinositide-3-kinase (PI3K)/AKT pathway.P < .05; arranged by gene count). According to the GO enrichment analysis, the candidate target genes were mainly involved in protein binding, DNA binding, ATP binding, positive regulation of transcription from the RNA polymerase II promoter, protein homodimerization activity, and apoptosis. KEGG pathway analysis revealed that the candidate genes were related to the mitogen-activated protein kinase (MAPK), tumor necrosis factor, PI3K/AKT, and forkhead box O (FOXO) signaling pathways.GO and KEGG enrichment analyses of the candidate target genes were performed using database for annotation, visualization, and integrated discovery. The top 20 terms are listed in Table  The MAPK, PI3K-AKT, and FOXO signaling pathways, which were enriched in KEGG enrichment analyses, are classic pathways in the regulation of follicular development and are also involved in POI.,45 A previous study suggested that quercetin prevents primordial follicle loss in mice with cyclophosphamide-induced POI by inhibiting the PI3K-AKT-FOXO3a pathway. The MAPK pathway is a downstream pathway that is related to oxidative stress. MAPK and PI3K-AKT pathways play important roles in follicle activation and POI. Moreover, the tumor necrosis factor pathway plays an essential role in inflammation and can induce POI via apoptosis.GO and KEGG enrichment results indicated biological characteristics of the candidate target genes, from which we can learn the underlying pharmacological mechanism of Kuntai capsules in the treatment of POI. GO enrichment analysis revealed that the targets were mainly enriched for biological processes such as transcription, which suggests that the pharmacological effects of Kuntai capsules on POI may involve in the transcription regulation of POI target genes. Inappropriate follicular development, especially the depletion of primordial follicles, is a prominent event in the progression of POI.The PPI network of 157 intersecting molecules was obtained using STRING software. Visualization of the PPI network is shown in Figure ,49 The binding energies for each docking molecule are presented in Table To further explore the interaction between core active ingredients and hub proteins, we selected the top 3 ingredients  based on the drug ingredients-POI targets network and PPI results to conduct molecular docking with hub proteins: JUN , AKT1 , TP53 , IL-6 , and EGER . The interactions between the 3 ingredients and 5 proteins were analyzed using AutoDock Vina version 1.2.2. Binding energy is a common index used to evaluate the docking between receptors and ligands. Binding energies below − 5 kcal/mol indicate a good connection between the receptor and ligand.In this study, we explored the mechanisms by which Kuntai capsules exert therapeutic pharmacological effects on POI and identified potential therapeutic targets and pathways using network pharmacology. In addition, by molecular docking, we identified baicalein as the core component of the Kuntai capsule involved in the treatment of POI. These findings extend understanding of the mechanisms of Kuntai capsules for treating POI, while also providing scope for additional research for new drug development.www.editage.cn) for the English language editing.We acknowledge the public databases for providing their platforms and contributors for uploading meaningful data. We would like to thank Editage (Funding acquisition: Yan Sun.Supervision: Yan Sun.Writing – original draft: Feng-Xia Liu.


Corididius chinensis (Cc) has been proven to have a certain curative effect on Mn poisoning. Therefore, network pharmacology was performed to explore potential therapeutic targets and pharmacological mechanisms of Cc. We found ingredients by building our own database through literature,  and potential targets of Mn-induced nervous system diseases from the OMIM, GeneCards, and DrugBank database were identified. A protein-protein interaction network was constructed using Cytoscape. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analysis was performed for the treatment of Mn-induced nervous system disease, and molecular docking was carried out to verify the results of network pharmacology analysis. After screening disease-related genes, 12 intersecting genes overlapped between 284 target proteins of the active compound and 195 potential disease targets. The pathways of neurodegeneration_multiple diseases and Alzheimer disease pathway may be the most potential pathway of Cc treating Mn-induced nervous system diseases. CASP9 and PTGS2 in neurodegeneration_multiple diseases, NOS1, NOS2 in Alzheimer disease pathway were identified as core targets. Especially, molecule docking analysis unveil that aspongpyrazine A docking NOS2 is the most potential therapeutic drug and target, which primarily involved in the processes of oxidative stress and inflammation.Neurotoxicity could be induced by long exposure to manganese (Mn). The traditional Chinese medicine,  At the cellular level, Mn directly causes mitochondrial dysfunction by destroying mitochondrial complex I and consuming mitochondrial ATP storage. Moreover, the stress response in mitochondrion is activated, and ROS are stimulated to produce more toxic reactions and apoptosis. Clinically, the symptoms of Mn poisoning include depression, anxiety, cognitive impairment and autonomic dysfunction. Current treatment options for PD are mainly symptomatic because the etiological physiology has not been fully elucidated. Dopamine and other drugs have been shown to be effective in PD. However, more effective and less toxic medicines are expected to be developed to treat Mn-induced nervous system diseases.Fast-growing studies shown that neurodegenerative diseases are closely related to environmental transition metal exposure.Corididius chinensis (Cc), a TCM, has been proven to have a certain curative effect on Mn poisoning.,12 Moreover, the active biomolecules of Cc, which have been shown to have a protective function, were identified by GC–MS and other techniques. However, only proliferation in neural stem cells has been shown. Therefore, the underlying mechanism by which Cc treats Mn-induced nervous system diseases has not been fully verified.Recently, traditional Chinese medicines (TCMs) have been considered for treating PD, with good tolerance.,15 The targets of the active compound of Cc and the disease targets of “Mn-induced neurotoxicity” and “Mn-induced nervous system disease” were collected to construct a component-target network and a PPI network of interaction targets. Afterwards, a gene ontology (GO) analysis was performed using the database for annotation, visualization, and integrated discovery (DAVID), followed by Kyoto encyclopedia of genes and genomes (KEGG) analysis.Network pharmacology, which is based on bioinformatics and systems biology, is becoming increasingly dynamic. Network pharmacology is widely used in the in-depth study of TCM to explain scientific conundrums.We aimed to identify potential treatments for of Mn-induced nervous system diseases using network pharmacology for the first time, and to provide relevant relative references for subsequent research. The workflow is displayed in Figure https://pubchem.ncbi.nlm.nih.gov/) was used to obtain certificate, transformation, sorting and classification information. The core bioactive components in Cc are asponguanosine A, aspongadenine B, asponguanine B, aspongpyrazine A, aspongpyrazine B, asponguanine C, N-acetyldopamine and 1/2-dehydro-N-acetyldopamine.We obtained the main chemical components of Cc from China national knowledge infrastructure, universal databases, PubMed and Web of Science. The PubChem database (http://lilab-ecust.cn/pharmmapper/index.html). The following filter criteria were applied: prediction accuracy  > = 0. 7, predicted probability  > 0. 9.We used ChemDraw 19.0 software to map the structure of the active components and predicted their targets using PharmMapper (http://www.genecards.org/), OMIM database (https://omim.org/), and Disgnet (https://www.disgenet.org/) with the keywords “Mn-induced the neurotoxicity and “Mn-induced nervous system disease.” A Venn diagram was drawn to integrate the common targets of Cc in nerve protection (https://bioinfogp.cnb.csic.es/tools/venny/).Disease targets were then collected in the GeneCards database (https://cn.string-db.org/cgi/input.pl) aims to integrate all publicly available sources of protein–protein interaction information and search for systems with known and predicted interactions between proteins. We collected targets using STRING and acquired the protein-protein interaction network (PPI) network. The data were imported into Cytoscape3. 7.0 software to obtain the PPI. The Network Analyser plug-in was then used to analyze the network topology parameters, and nodes with above-average nodules were screened to further construct a network of “top 10 targets network.” The Enrich database is commonly used for gene enrichment analysis. GO functional  analysis and KEGG pathway enrichment analysis of the coaction targets were conducted using the BINGO plugin and the DAVID database (https://david.ncifcrf.gov/).We constructed the “component-target” network using Cytoscape 3.7.0 software. The search tool for the retrieval of interacting genes proteins (STRING) (https://www.rcsb.org/) database and downloaded the mol2 format files of the 3D structure of the core active component from several other databases. Core ingredients were determined by ChemDraw 19.0. The software program minimizes the energy of the compound, uploads it in Mol format, and selects the standard binding mode from the server articles. The specific docking parameters are as follows: generic evolutionary method = 200, generation = 70, and number of solutions = 2.AutoDOCK is an open-source molecular docking software for systematic evaluation and activity screening, providing postanalysis tools for k-means and layered clustering methods based on the docking site (protein–ligand action) and compound properties (atomic composition), which can be directly introduced into ligand molecules for docking and allows multiple ligands to dock at the same time. The bioactive components and top 10 targets of Cc were selected for the molecular docking analysis. We downloaded the PDB format file of the 3D structure of the targets from the RCSB PDB . A total of 284 potential targets of Cc-related targets and 195 targets of Mn-induced nervous system diseases were obtained. Duplicate data were removed, and 12 intersecting genes were identified as potential key targets of Cc in Mn-induced nervous system diseases.A total of 89 compounds in Cc were collected from several articles , 17 cellular component, and 16 molecular function processes. The top 5 items of BP, cellular component, molecular function were selected visually based on ely Fig. . The resP < .05) were identified using KEGG analysis  in the PPI network with a degree > −6 in the “compound-target-pathway-disease” network. The primary active components  were molecularly docked by AutoDock Vina, as shown in Table We optimized and processed the binding patterns of the target proteins and their components in PyMoL 2.5.4. There are strong interactions between the 2 molecules, including electrostatic interaction, Van der Waals force, hydrogen bond, hydrophobic interaction, etc. This energy is expressed as binding energy. In molecular docking, a lower the affinity standard indicates a stronger binding affinity. The results showed that most affinity values are below −5 kcal/mol, indicating that the core targets have a relatively stable binding energy with the active components. potentially leading to neurodegenerative disease. Neurotoxicity belongs to the category of “tremor syndrome” in TCM. Tremor syndromes originate in the brain. Old age and low immunity are the causes, while renal insufficiency is the root. In TCM, the renal insufficiency is closely related to the decline of reproductive ability. The Compendium of Materia Medica records that Cc is mainly used to adjust body balance and renal insufficiency. Therefore, from the perspective of TCM, Cc is suitable for toxic neurodegenerative diseases patients. Modern pharmacological studies have demonstrated that Cc has a certain curative effect against Mn poisoning. Cc reduced the number of apoptotic cells and the expression of caspase-3, cytochrome c, and malondialdehyde, improving the reproductive system compared with Mn poisoning group, because its antioxidant activity. The blood-testis barrier, including boundary adhesion molecule 1, zonal occlusion-1, claudin1 and occludin were ameliorated by Cc in Mn poisoning. Asponguanine B in Cc have significantly neuroprotective effects, but only the proliferation stimulating properties of neural stem cells. Therefore, we speculate that Cc plays a therapeutic role in Mn-induced nervous system diseases.In this research, the potential targets and material foundation of Cc for treating Mn-induced nervous system diseases were analyzed utilizing network pharmacology. Eighty-nine components were collected from several articles, which have good biological activities. Some of the screened compounds have good biological activities. The structure of the active ingredients was determined in ChemDraw 3D, and the results were input into the PharmMapper database for target screening. The disease targets of “Mn-induced neurotoxicity” and “Mn-induced nervous system disease” were collected from the DisGeNET, OMIM and GeneCards databases. Mn directly causes mitochondrial dysfunction by destroying mitochondrial complex I and consuming stored mitochondrial ATP. Thus, the stress response in mitochondria is activated, and ROS are stimulated to produce more toxic reactions and apoptosis,Our network analysis demonstrated that the most highly correlated targets between Mn-induced nervous system disease-related genes and Cc compound-related genes were CASP9, PTGS2, NOS1 and NOS2, which are primarily involved in oxidative stress and inflammation. The results of PPI network identified CASP9, PTGS2, NOS1, and NOS2. In particular, CASP9 may be the core target.,21 Therefore, the anti-Mn-induced nervous system diseases potential of CASP9 still deserves further attention and research, and CASP9 may be an important target for the treatment of Mn-induced nervous system diseases.In wild-type neurons, cell death occurs through the caspase-9 pathway, which relies on the key molecule CASP9. The existence of these 2 alternative pathways of nutrient deprivation induced death may have an impact on ensuring the correct development of the nervous system. In wild-type neurons, death requires CASP2 dependent pathway, and caspase-9 dependent pathway seems to be inhibited by endogenous inhibitor of apoptosis protein. On the contrary, for CASP2-null neurons, death depends on the CASP9 pathway. PTGS2 polymorphism is associated with reduced risk of Alzheimer disease.,23 Michele et al reported that the polymorphism of the PTGS2 gene is associated with an unrelated incidence of stent restenosis. Nevertheless, the potential of PTGS2 for atherosclerosis still deserves further attention. PTGS2 as an important substance that affects inflammation is likely to become a key target for the treatment of Mn-induced nervous system diseases.PTGS2 is an important inflammatory mediator mostly expressed in the central nervous system; high PTGS2 expression is associated with neuronal damage and cognitive impairment. Multiple single-nucleotide polymorphism have been described in the promoter region of PTGS2 gene, which may regulate its transcription, but only one polymorphism is located at position 765 G/C, and the stimulator-1 binding site inferred by a has been proven to be functional.,26 Another study demonstrated that deleting the NOS2 gene protects against Mn-induced neurological dysfunction in mice. As important substances that affect oxidative stress, NOS1 and NOS2 are an important factors in the occurrence and development of Mn-induced nervous system diseases.Schwann cells are essential in the peripheral nervous system. The dysfunction of Schwann cells can induce various degenerative disease of peripheral nerves. Oxidative stress is considered a pathogenic factor of degenerative neurological diseases. However, in peripheral nerve degenerative disease, there is no effective molecule that can be used to inhibit the neurodegeneration of nerve Degenerative disease. Ethyl pyruvate acid is a candidate regulator of oxidative stress, targeting Schwann cells during peripheral nerve degeneration. Some studies have showen that inhibiting NOS1 can prevent neuronal degeneration. In KEGG pathway enrichment analysis, pathways of neurodegeneration_multiple diseases and Alzheimer disease may be the main pathways for Cc treatment of Mn-induced nervous system diseases. Pathways of neurodegeneration_multiple diseases are the top-ranked signaling pathways, and CASP9, APP, NOS2, NOS1, PTGS2 and SLC6A3 are all intersecting targets enriched in this pathway. Among them, CASP9, PTGS2, NOS1 and NOS2 are key targets identified from the network analysis. The Alzheimer disease pathway is the second-ranked signaling pathway, and 5 key targets, CASP9, APP, NOS2, NOS1 and PTGS2, are enriched in this pathway. They cover the entire range of calcium signaling in this process and play an integral and important role in AGE-RAGE signaling in this pathway. After predicting and analyzing potential targets and pathways, we surmised that the effect of Cc in ameliorating Mn-induced nervous system diseases involves inhibiting the inflammatory response and oxidative stress.In GO functional enrichment analysis, Cc for the treatment of Mn-induced nervous system diseases is mainly the mitochondrion, while the molecular function mainly affects enzyme binding and the biological process mainly affects cellular response to UV, response to xenobiotic stimulus. These findings apply to the results of the current research. It has been reported that Cc increases the total antioxidant capacity, and glutathione peroxidase enzymes in serum of Mn-exposed rats with high dose of Cc treatment, which increased the ability of rats to resist external stimuli. These findings suggest that these ingredients may be critical in the treatment of Mn-induced nervous system diseases, which is worthy of further discussion. The docking results showed that the 2 compounds, asponguanosines A and aspongpyrazine A, could strongly bind to the proteins, and the binding of aspongpyrazine A to NOS2 showed the lowest binding energy, indicating that the combination was the most stable. However, the effect of aspongpyrazine A on NOS2 has not been reported in the literature and needs to be confirmed by relevant research.In molecular docking, Cc has a high probability of binding to 4 key targets CASP9, PTGS2, NOS1, and NOS2. According to previous studies, the active components in Cc, including asponguanosines A, aspongadenine B, asponguanine B, aspongpyrazine A, aspongpyrazine B, and asponguanine C, are not clearly understood and have not been characterized in animal models. Among them, asponguanine B exerts better neuroprotective effects, but only in stimulating the proliferation of neural stem cells.Although this study predicted the potential targets and mechanisms of Cc in the treatment of Mn-induced nervous system diseases, it still had some limitations. We used only network pharmacology and molecular docking methods, which are modern bioinformatics methods, to explore the role of Cc in Mn-induced nervous system diseases. However, the current network information technology needs to be further improved, and the accuracy and timeliness of database data require scientific verification. The influence and mechanisms of these potential active ingredients on Mn-induced nervous system diseases have not been explained and verified; however, it is believed that they exhibit substantial potential for development and research.This study predicts the potential targets and mechanisms of action of Cc in the treatment of Mn-induced nervous system diseases. Cc can treat Mn-induced nervous system diseases through targets such as CASP9, PTGS2, NOS1, and NOS2. Its mechanism of action may be related to pathways of neurodegeneration_multiple diseases and Alzheimer disease pathway, which is valuable for further research. Nevertheless, our research lacks experimental validation. The binding of aspongpyrazine A to NOS2 has a good binding energy of − 8.1 kcal/mol. Future experimental studies are expected to be conducted, confirming our findings and continuing to explore new drugs for treating Mn-induced nervous system diseases on this basis. This study is the first to screen the TCM without traditional Chinese medicine systems pharmacology database and analysis platform databases and it is applicable at any time. Building one own TCM database is beneficial for ensuring the integrity of ingredients and targets, which means that some of the latest bioactive molecules that have not yet been included in the traditional Chinese medicine systems pharmacology database and analysis platform database are included in our database.Conceptualization: Huixian Lou, Jing Ma, Xiaohui Hou.Data curation: Mei Zhang, Huixian Lou, Keyi Xiong, Xiaohui Hou.Formal analysis: Mei Zhang, Jing Ma.Funding acquisition: Xiaohui Hou.Methodology: Mei Zhang, Huixian Lou, Keyi Xiong.Resources: Mei Zhang, Huixian Lou.Software: Mei Zhang, Jing Ma.Validation: Keyi Xiong.Visualization: Mei Zhang, Huixian Lou, Keyi Xiong.Writing – original draft: Mei Zhang.Writing – review & editing: Mei Zhang, Xiaohui Hou.


It has been reported that Mori Folium (MF) and Eucommiae Cortex (EC) exhibit pharmacological effects in the treatment of immunosuppression. However, the mechanism of MF and EC against immunosuppression remains unclear. This study aims to explore the mechanism of action of MF and EC for the treatment of immunosuppression through network pharmacology, molecular docking, molecular dynamics simulations and animal experiments. As a result, 11 critical components, 9 hub targets, and related signaling pathways in the treatment of immunosuppression were obtained based on network pharmacology. The molecular docking suggested that 11 critical components exhibited great binding affinity to 9 hub targets of immunosuppression. The molecular dynamics simulations results showed that (-)-tabernemontanine-AR, beta-sitosterol-AR and Dehydrodieugenol-HSP90AA1 complexes are stably bound. Additionally, in the animal experiments, the treated group results compared to the control group suggest that MF and EC have a significant effect on the treatment of immunosuppression. Therefore, MF and EC treatment for immunosuppression may take effects in a multi-component, multi-target, and multi-pathway manner. The results herein may provide novel insights into the treatment of immunosuppression in humans. The immune system plays an important role in countering infections and tumors; it acts as the body’s defense system by protecting our body cells, tissues, and organs from invading infections from harmful microorganisms and other disease-causing microbes ,2. ImmunMorus alba L., belonging to the Moraceae family, is one of the most valuable plants, and rich in natural products; its pharmaceutical name is Mori Folium (MF) [ium (MF) ,5. Mori ium (MF) , flavonoium (MF) , alkaloiium (MF) , etc. Thium (MF) ,10.Eucommiae Cortex (EC) is derived from the dried bark of Eucommia ulmoides Oilv, wherein active compounds are separated and analyzed using modern chemical methods; a total of 112 compounds have been identified, mainly including lignans, iridoids, phenolics, steroids, terpenoids, and flavonoids [Network pharmacology is a new field combining traditional Chinese medicine (TCM) and network pharmacology, which is based on the theory of system biology and the viewpoint of network pharmacology. Network pharmacology facilitates under-standing of the compatibility law of prescriptions, identification of TCM medicine, prediction of disease-related targets, and the action mechanism of TCM . MoleculThe animals were housed in the animal house of Anhui Agricultural University, with commercial standard diets and water ad libitum. Additionally, among the experimented animals, half are males and the other half are females. In the current study, all animal experiments completely comply with the ARRIVE guidelines and were conducted in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and the associated guidelines, the EU Directive 2010/63/EU for animal experiments, and the National Research Council’s Guide for the Care and Use of Laboratory Animals.In this study, fifty mice were randomly divided into five groups, with each group containing ten mice: control group, model group, MF group, EC group and MFEC group. After 7 d accommodation, on the 8th–10th day, all groups except the control group were intraperitoneally injected with cyclophosphamide at a rate of 80 mg/kg once a day. The control group was injected with the same amount of normal saline. On the 11th–17th day, the MF group, EC group and MFEC group were fed 200 mg/kg MF, EC, MFEC, respectively. The other groups were fed the same amount of normal saline. All the experiments pertaining to animals comply with the commonly accepted “3R”, according to the guidelines of Anhui Agricultural University regarding the protection of animals. The spleens of the mice were collected and weighted (g), and the spleen index of the mice was calculated as follows:Additionally, the IF-2, IF-6 and TNF-α levels in serum were determined according to ELISA kits for mice , and the 100 µL of negative control, standard, or diluted serum sample to be tested were added to the microtiter plate to make duplicate wells. The microtiter plate was covered with film, placed horizontally, and incubated at room temperature for 60 min. After discarding the liquid, the plate was washed four times with washing solution an dried after each operation. Some 100 μL of enzyme-labeled antibody was added to each well, the membrane was added, and they were incubated at room temperature in the dark for 30 min. Similarly, after discarding the liquid, they were washed with washing solution four times, and the plates were dried after each operation. Briefly, 100 μL of TMB substrate solution was added to each well and incubated at room temperature in the dark for 10 min; then, 100 μL of stop solution was added to each well to stop the reaction. The optical density value  of each well was measured at a wavelength of 450 nm using a microplate reader.https://old.tcmsp-e.com/tcmsp.php) (accessed on 16 April 2014). The active compounds were selected according to pharmacokinetic parameters including absorption, distribution, metabolism, and excretion. Basically, the criteria for the related parameters were set follows: oral bioavailability (OB) not less than 30%, and drug-likeness (DL) not less than 0.18.The chemical ingredients of MFEC were obtained from the Traditional Chinese Medicine Database and Analysis Platform (TCMSP) (http://119.3.41.228:8000/tcmid/) (accessed on 16 April 2014), PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 16 April 2014) and the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) (accessed on 16 April 2014). Additionally, all the targets were integrated, and duplicates were removed.The potential targets of the active ingredients were obtained from TCMSP, TCM-ID (https://www.genecards.org/) (accessed on 16 April 2014) was used to extract the immunosuppression-related targets, and the median value of relevance score was used to screen the obtained targets. Only the targets with an inference score no less than 9.33 were included. Besides, the Uniprot database (https://www.uniprot.org/) (accessed on 16 April 2014) was used to convert the obtained disease-related targets and the potential targets of the active ingredients obtained in https://bioinfogp.cnb.csic.es/tools/venny/) (accessed on 16 April 2014) was used to determine the number of intersection genes between MFEC potential targets and disease-related genes, and to plot a Venn diagram.The GeneCards database (https://string-db.org/) (accessed on 16 April 2014) and Cytoscape 3.8.1 software. An intersection ingredient–target network was constructed to understand the associations between the active ingredients and intersection targets of MFEC. Network generation and visualization were performed using Cytoscape software 3.8.1. In order to construct a PPI network, common targets were imported into the String 11.5 database to generate a network, and the topological analysis was conducted using Cytoscape software, wherein the degree centrality, closeness centrality, and the betweenness centrality were obtained to evaluate the central properties of the nodes in the network.The network’s construction and topological analysis were investigated using String 11.5 database (https://metascape.org/) (accessed on 18 December 2021), a web-based portal that covers a comprehensive list of gene annotations, was used to perform a Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The results were analyzed and sorted according to the adjusted p value. Additionally, the target–pathway network was established using Cytoscape software 3.8.1.To further elucidate the pharmacological mechanisms of MFEC in immunosuppression treatment, Metascape database (https://www.rcsb.org/) (accessed on 10 November 2022). The Sybyl-x 2.1.1 software could perform the molecular docking of active compounds with putative targets. All the pieces of software and databases used in this study are described in The molecular docking was performed to predict the binding affinity and interactions between the targets with the active ingredients. Based on the topological parameters of the ingredient–target and PPI networks, ingredients and targets with more than 2-fold the median value of the degree were selected as candidate ligands and receptors for molecular docking. The software ChemBio3D 19.0 was used to calculate the minimizing energy, and the ligand structures were exported in MOL 2 format. ChemBio3D has been widely used for the simulation and analysis of the protein receptor–ligand small molecule complex. It is capable of calculating molecular energy-minimizing configurations, carrying out the structural optimization of model molecules, and simulating analyses to examine the stable structures of molecules ,15. The The above part of molecular docking results -tabernmontanine-AR; beta-carotene-AR; beta-sitosterol-AR) were performed using Gromacs 2020.6 software for molecular dynamics simulations. The simulation system was adjusted to a sodium chloride solution at 37 °C in order to replicate the actual human in vivo environment. Considering the active ingredients of MF and EC as multi-carbon ring skeleton structures, a Charmm36 force field and TIP3P water model were chosen for MDs. During the MDs, the relevant hydrogen bonds are constrained by the Linear Constraint Solver (LINCS) algorithm with the integration step of 2 fs, while the non-bond interaction cut-off value is set to 10 Å, and updated every 10 steps. Additionally, the electrostatic interactions are calculated using the particle-mesh Ewald (PME) with a cut-off value of 1.2 nm. In order to optimize the original conformation of the protein in the solvent, the protein receptor–ligand small molecule complex was pre-equilibrated for 100 ps prior to simulations, followed by canonical ensemble (NVT) equilibration for 100 ps using a modified Berendsen temperature coupling algorithm with a coupling time constant of 0.1 ps, which allowed the complex to be warmed up to 310 K with the solvent system. Subsequently, the solvent and complex were pressure equilibrated, while the pressure was increased to 1 bar using a Berendsen constant pressure, with constant-pressure and constant-temperature (NPT) equilibration of 100 ps. Finally, MDs of the complex were performed for 50 ns.It can be seen from Based on the criteria of the OB ≥ 30% and DL ≥ 0.18, a total of 53 active ingredients  and 4897 edges C. Among GO enrichment analysis was performed using Metascape software version v3.5.20230501 on 204 common targets of immunosuppression and Mori Folium and Eucommiae Cortex in terms of biological process (BP), cellular composition (CC), and molecular function . As seenKEGG pathway analyses can indicate possible signaling pathways of 204 proteins, and there were 362 pathways obtained in total. Some nine proteins were selected according to the PPI network, and the structures of these nine proteins were downloaded from the PDB database. Sybyl-X software version 2.2.1 was used to remove water and excess ligands in these nine proteins, and then they were hydrogenated. The last step in pre-processing the protein was to generate binding pockets. These nine proteins were docked with eleven active components the related results are shown in To further validate the binding stability of drug active ingredients with AR and HSP90AA1 in vivo, analyses of root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg) and hydrogen bond (Hbond) were obtained using Gromacs 2020.6 software. RMSD was used to analyze the binding stability of the AR and HSP90AA1 to the corresponding ligand receptor in the MDs. Rg indicated the tightness of the overall structure of the protein  and charIn recent decades, TCM has attracted worldwide attention due to its exact curative effects, relatively low toxicity, and low cost. Due to the complex components of TCM and the various biological systems in which they are involved, elucidating its mechanism of action has become a challenge ,16. NetwIn the present study, network pharmacology is used to explore the possibility of MFEC in the treatment of immunosuppression. The obtained relevant targets were used to dig out the putative related pathways, and to speculate on the possible mechanisms of MFEC treatment of immunosuppression. Additionally, molecular docking was used to verify the key components and targets of MFEC involved in the treatment of immunosuppression.During the active component data-mining of MFEC, a total of 53 active components were collected. Finally, 11 critical components were selected according to the PPI and topological parameters. The 11 components were icosa-11,14,17-trienoic acid methyl ester, iristectorigenin A, arachidonic acid, tetramethoxyluteolin in molecular function; dehydrodieugenol, (-)-tabernemontanine, and (9R)-6′-methoxycinchonan-9-ol in EC, and their common active ingredients quercetin, kaempferol, beta-sitosterol, and beta-carotene. These 11 compounds mainly belong to flavonoids, alkaloids, phenolics, terpenes, phytosterols, and organic acids, which have pharmacological effects in MFEC. Both flavonoids and phenolic active ingredients in MFEC belong to polyphenolic compounds and contain a large number of aromatic hydroxyl groups, which account for their anti-inflammatory, anti-glycemic pharmacological effects, and prevent oxidative damage and cell death ,19. In aThe pharmacological effects of alkaloids are very extensive, and can be used for anticancer, antimalarial, antiviral, antihypertensive, antispasmodic, antiarrhythmic, analgesic, antibacterial, and antidiabetic purposes, and also as central nervous system stimulants and muscle relaxants, with vasodilatory properties . The betComprehensive analysis of GO enrichment results showed that the active components in MFEC may act on organisms through the nucleus, cytoplasm, membrane, cytosol, and organelle membrane; exert the molecular functions of phosphatidylinositol-3-kinase (PI3K) family protein kinases, BH3 domain binding, nitric oxide and other synthase regulators, vascular endothelial growth factor (VEGF) activator receptors, RNA polymerase 2 transcription coactivator, and so on; participate in the reaction of carbon monoxide and iron ions; positively regulate vitamin D biosynthesis, monooxygenase activity and cell apoptosis in vivo; and negatively regulate myosin light chain phosphatase and other series of biological processes so as to achieve the goal of therapeutic immunosuppression. The KEGG enrichment analysis showed that the main signaling pathways involved in the common targets of MFEC and immunosuppression included the pathway in cancer, advanced glycation end products and their receptors (AGEs-RAGE) signaling pathway in diabetic complications, PI3K-Akt signaling pathway, tumor necrosis factor (TNF) signaling pathway, fluid shear stress and atherosclerosis, kapos sarcoma-associated herpes virus infection, hepatitis B and C, and prostate and pancreatic cancer. AGEs are heterogeneous glycation products of proteins, lipids and nucleotides, and their receptors are called RAGE, which are multiligand transmembrane receptors of the immunoglobulin superfamily . When thThe nine protein targets selected for molecular docking in this study were jointly selected by combining the analysis results of compound–target network and PPI network. AKT1, one of the AKT isoforms, is an oncogene that is ubiquitous in neurons as an important downstream substrate in the PI3K-Akt signaling pathway . AKT1 alhttp://smina.sf.net (accessed on 18 November 2021). Vina, iDock, and Smina utilize an empirical scoring function. Dk_scoring function refers to the linear regression between the calculated and experimental binding energies. The molecular docking results suggests that iristectorigenin A, quercetin, acid methyl ester, arachidonic acid exhibited great binding affinity to nine hub targets of immunosuppression. From the analysis of the results of RMSD, RMSF, RG and Hbond of MDs, it is clear that the (-)-tabernemontanine-AR, beta-sitosterol-AR, and Dehydrodieugenol-HSP90AA1 complexes bind stably. Molecular docking and further MDs’ validation finally identified AR, MAPK14, TNF, and HSP90AA1 as the core targets for immunosuppression.Molecular docking revealed drug–target and protein–target interactions from the molecular perspective, and MDs have been widely used to study the binding stability between proteins and molecules and evaluate the structural features of protein–ligand systems . TherefoThe effects and pharmacological mechanisms of Mori Folium and Eucommiae Cortex (MFEC) extracts against immunosuppression were deciphered by integrating network pharmacology analysis, molecular docking, molecular dynamics simulation, and in vivo validation. The results indicated MFEC exerted pharmacological efficacy via multiple targets and multiple pahways. This study provides a theoretical basis for in-depth exploration of the pharmacological effects of MFEC and its mechanism of action in the treatment of immunosuppression.


Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old metabolite KEGG-based datasets, including one benchmark dataset that is invalid due to the presence of over 1500 duplicate entries. Therefore, we have developed a new benchmark dataset derived from the KEGG following optimal standards of scientific computational reproducibility and including all source code needed to update the benchmark dataset as KEGG changes. We have used this new benchmark dataset with our atom coloring methodology to develop and compare the performance of Random Forest, XGBoost, and multilayer perceptron with autoencoder models generated from our new benchmark dataset. Best overall weighted average performance across 1000 unique folds was an F1 score of 0.8180 and a Matthews correlation coefficient of 0.7933, which was provided by XGBoost binary classification models for 11 KEGG-defined pathway categories. Metabolomics is the systematic study of the biomolecules present in a given living system that can be composed of one or more organisms. Metabolomics is often used to study metabolism, the set of life sustaining biochemical reactions occurring in these living systems. These reactions occur in coordinated and regulated pathways, where the product of the previous reaction is the substrate for the next reaction. One or more groups of interconnected or related metabolic pathways form a metabolic pathway category. These pathway categories can be organized in a hierarchy with broader categories being at the top of the hierarchy and more specific categories being lower in the hierarchy . The bioWhile such data sources link some metabolites to their pathway involvement, many of these links are missing. The advances in Mass Spectroscopy (MS) and Nuclear Magnetic Resonance (NMR) technologies over the past few decades, especially in terms of sensitivity, have led to a dramatic increase in the amount of metabolomics data being collected and uploaded to databases such as Metabolomics Workbench  and MetaSeveral machine learning methods have been developed to predict the pathway involvement of metabolites in the form of mapping metabolites to broad hierarchical pathway categories given the molecular structure of said metabolite. Most notably were the machine learning models trained on the dataset created by Baranwal et al.  composedBeyond the ability to predict the pathway involvement of metabolites, there is also the need to determine which molecular substructures within the metabolites are most important for this prediction. Rather than training black box models, Jin et al.  presentshttps://www.genome.jp/brite/br08901, accessed on 3 July 2023) to determine which of the KEGG compounds were associated with broad metabolic pathways as defined by KEGG. The ‘Metabolism’ section of the br08901 hierarchy contains 12 distinct metabolic pathway category branches, excluding ‘Global and overview maps’ since this is a catchall category. Each of these 12 metabolic pathways then branch out into leaf nodes representing more specific pathway categories.We used the kegg_pull  Python pFor the first filtering step in Next, there were a few pairs of metabolite entries with identical or equivalent molfiles. We handled these duplicate molfiles by merging the pair of compound entries into a single representative entry, keeping only one molfile and creating the union set of their metabolite pathway labels. After de-duplicating the duplicate molfiles (fourth filtering step in 2O), contained very few non-hydrogen atoms and thus very little chemical information. With a histogram, However, some compound entries, like KEGG C00001  and the dependent variable being the average misclassification rate of the compounds within the window (compounds with a non-hydrogen atom count within the range). Using a window size of 5, as illustrated in Zooming into the first 20 points b, we seeThe molfiles (in both the filtered and unfiltered dataset) provided the raw data for constructing the chemical features, with each molfile being transformed to a chemical feature vector with associated metabolic pathway labels. To construct chemically-informative features from the molfiles, we used the atom coloring technique introduced by Jin et al. ,18,19, sAcross all molfiles, there were 23 0-bond-inclusive features with an increasing number of features as feature sets that were concatenated to those with higher bond inclusivity . In the The md_harmonize package  allows fAdding more information to the atom colors enabled us to generate a higher amount of coloring combinations, which further distinguished one metabolite from another, reducing the number of duplicate feature vectors. It is especially important to reduce the number of duplicate feature vectors that map to different labels, since such entries confuse the machine learning model. After the atom colors were generated and the feature vectors of 0-bond-inclusive up to 3-bond-inclusive were concatenated together , featureWhile adding additional details by configuring the atom coloring method  reduced The list served as a template, which we then manually filled in with instructions to handle each case. Upon inspecting each compound using the KEGG browser, we determined the action to take for each case and recorded that action in the file such that it could be read into the next script in the pipeline and computationally modify the dataset. Some entries were merged into one, meaning one entry was kept and any duplicates removed with the pathway labels unioned into a single set. Other compounds were simply removed and some compounds were retained. Either way, the dataset decreased in size as manually detected invalid entries were handled. The removal of entries resulted in more duplicate columns, necessitating removing duplicate columns once more. The actions taken in the manual dataset cleaning along with the justification for those actions and the amount of data removed for each action are described in The models trained on the final dataset included RF (popular tree-based method), XGBoost , and a Multi-Layer Perceptron (MLP) (deep learning method). For the MLP, it was not practical to train it on feature vectors of size 14,656. Pragmatically, deep learning methods require optimized training using a graphics processing unit (GPU), which has GPU memory limitations. Therefore, we trained an autoencoder, a separate deep learning model, to compress the feature vectors. We will refer to these compressed features as the encoded dataset, which contained 10% of the original number of features .We tuned the hyperparameters of the autoencoder using the Bayesian optimization method  and we uWhile some of the compounds had R groups, others had repeating units, i.e., the molfile provided the molecular structure of the initial unit but not that of the repeats. Like the R groups, underlying chemical information of the compounds containing repeating units was obfuscated, making their complete molecular structure unknown. We will refer to such compounds as ambiguous compounds. Hypothesizing that the ambiguous metabolites would have different performance than the non-ambiguous, we created 2 subsets from the full final dataset , i.e., tWe trained a separate binary classifier for each of the 12 pathway labels and on both datasets, i.e., full and non-ambiguous . While tWe performed a cross-validation (CV) analysis, using a stratified train/test split to maintain the ratio of training entries to test entries in each fold such that the ratio closely matched that of the entire dataset. Use of stratified CV folds has been demonstrated to reduce the variance in model performance across folds, increasing robustness . For eacModel performance metrics were collected for every combination of model, pathway category, dataset, and test set . The met® Xeon® Gold 6130 CPU@2.10 GHz’ CPUs. No more than 24 h of compute time was allocated for the XGBoost runs and no more than 72 h for the MLP runs. Both the XGBoost runs and MLP runs had 1 core allocated and used a GPU of up to 12 GB of GPU memory, the name of the GPU card being ‘Tesla P100 PCIe 12 GB’. For the hyperparameter tuning and training of the autoencoder, we used similar HPC machines with up to 187 gigabytes of RAM and ‘Intel® Xeon® Gold 6130 CPU@2.10 GHz’ CPUs, but with ‘Tesla V100 SXM2 32 GB’ cards. Only 1 CPU core and no more than 72 h of compute time were allocated for the autoencoder runs.For the RF hyperparameter tuning and training, we used a set of desktop computers with 64 gigabytes (GB) of random access memory (RAM) and central processing units (CPUs) ranging from 3.4 gigahertz (GHz) to 3.6 GHz, each with 6 hyperthreaded (HT) cores. The CPU chips included ‘Intel(R) Core(TM)i7-2600 CPU@3.40 GHz’, ‘Intel(R) Core(TM) i7-5930K CPU@3.50 GHz’, ‘Intel(R) Core(TM) i7-4930K CPU@3.40 GHz’, ‘Intel(R) Core(TM) i7-6850K CPU@3.60 GHz’. For tuning the hyperparameters of and training the XGBoost and MLP, we used high-performance computing (HPC) machines with up to 187 GB of RAM and ‘IntelAll code for this project was written in the Python programming language . The modAfter measuring the misclassification rates for the metabolites in the final dataset b in the To emphasize this trend, we see that the average misclassification rates in the sliding window are much lower after filtering metabolites with non-hydrogen atom counts less than 7 . The aveWe can reduce the number of comparisons from 1152 to 96 by taking the overall performance across the pathway categories. One could accomplish this by taking the average and standard deviation of the scores for all 12 pathway categories and each of their 1000 CV folds . However, each pathway category takes up a different proportion of each dataset. Since the formulas for the performance metrics involve division, all but accuracy have the possibility of dividing by zero. A division by zero is undefined and therefore invalid, and any CV folds resulting in an invalid result were not included in the aggregation . To emphasize the final dataset’s improved performance compared to the unfiltered dataset, Much of the variance observed in While there are 600 different scores across the 12 categories , 477 areIn this work, we present a new KEGG-based dataset for the machine learning task of predicting the pathway involvement of metabolites. It significantly improves on the KEGG-SMILES dataset used in previous publications on metabolic pathway prediction, which contained duplicate entries and lacked the code and description of its creation. The lack of code precludes updating the dataset as more metabolites are discovered, the duplicate entries invalidate the prior analyses, and the lack of description of its creation makes the dataset suspect in general. Huckvale et al. outlined the optimal requirements of a benchmark dataset for this machine learning task, specifying that it must be reproducible, valid, accessible, and complete . We use We present strong evidence for the correlation of the number of non-hydrogen atoms in a metabolite and the ability for said metabolite to be classified reliably. This trend is expected, since low information content is always an issue in classification. Considering the clear drop in misclassification rate until reaching a non-hydrogen atom count of seven , we recoWe defined ambiguous metabolites as those containing R groups or repeat sequences specified in their molfile such that underlying chemical information is obfuscated. We expected and demonstrated that such metabolites would be more difficult to predict correctly for most pathway categories. However, for some pathway categories, i.e., ‘Glycan biosynthesis and metabolism’ and ‘Lipid metabolism’, ambiguous metabolites surprisingly outperformed the non-ambiguous entries .By generating features from the atom colors, we make features out of the molecular substructures of the metabolites. Measuring the importance of these features enables biochemists to determine which substructures are associated with the pathway involvement of the corresponding metabolites. Some substructures are associated with a metabolite being present in a pathway category while other substructures indicate that a metabolite is absent from said category . The abiNext, when comparing the performance of the three machine learning models, XGBoost unsurprisingly performed better overall than the Random Forest model while the MLP deep learning method did not improve on the tree-based methods. This is incongruent with deep learning-based methods exceeding the performance of tree-based methods in past publications . However, those models were more sophisticated than a simple MLP. It could be that such deep learning methods could surpass the performance of the XGBoost trained on our atom color features. Though, the atom color features do provide information on the molecular substructure of the metabolites similar to the graph-based models, albeit in a linearized fashion, and it includes information not just on atom configuration but also stereochemistry and bond order. It is still an open question whether models capable of processing more complex data structures can improve upon the performance of XGBoost trained on a tabular dataset. And to our knowledge, such models have yet to incorporate additional information beyond simple backbone molecular structure, such as atom stereochemistry, bond stereochemistry, and bond order.We recommend using separate classifiers per pathway category. Depending on the pathway category that a classifier is being trained to predict, different hyperparameter values will result from the hyperparameter tuning. We also see that the importance of the features used is highly dependent on the pathway category being predicted  while thWhile the weighted average MCC of XGBoost trained on our final dataset  was 0.7677 with a weighted standard deviation of 0.1540 , these wIn all of these cross-validation analyses and performance evaluations, KEGG is treated as a gold standard, making the assumption that KEGG’s description of pathway involvement is complete and without error. This assumption is necessary for training and evaluation and should be somewhat reasonable for central metabolism; however, this assumption is clearly not true, since KEGG is growing. Thus, there are implications with using a “gold standard” that is evolving. Besides improved machine learning methods and models, metabolic pathway prediction may also improve with more positive entries, i.e., metabolites that are involved in particular pathway classes (positive) as compared to not being involved in said pathway class (negative). A higher amount of positive entries was simulated by duplicating already extant positive entries in the SMILES dataset as shown by Huckvale et al. , which oSince the overall performance as well as the variance in performance are greatly dependent on the pathway category being predicted, certain use-cases may need to exclude certain pathway categories. As illustrated with the violin plots of MCC scores per pathway category in We present a new KEGG-based benchmark dataset for the machine learning task of metabolic pathway prediction, which is valid, comprehensive, completely transparent, fully reproducible, readily accessible via our Figshare, and maintainable as KEGG changes. In our hands, the XGBoost machine learning method outperformed both Random Forest and MLP with autoencoder methods for the classification of metabolites to 12 KEGG-defined pathway categories. While the scores attained with the XGBoost model trained on our dataset are seemingly less impressive than those obtained with other methods developed on the KEGG-SMILES dataset, we maintain that the previous publications are invalidated with the duplicate entries in the KEGG-SMILES dataset. Therefore, the results of KEGG metabolic pathway prediction performance presented here are trustworthy. Furthermore, the atom color features employed in our methods provide chemical insight into which molecular substructures are informative for pathway category prediction. Finally, we recommend that individual pathway category prediction performance be evaluated for each potential use case and application.


Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease that leads to progressive dyspnea and dry cough, with extracellular matrix deposition as the main pathological feature. Yifei Tongluo granules (YTG) are a traditional Chinese medicine formula that could nourish Qi-Yin, clear phlegm, and invigorate blood circulation. In this research, network pharmacology and molecular docking were used to elucidate the potential mechanism of YTG for treating IPF. A total of 278 biologically active compounds were included in YTG, and 16 compounds were selected for pharmacological analysis and molecular docking through “drugs-compounds-intersecting targets of YTG and IPF” network construction. Protein-protein interaction network was constructed using 330 YTG-IPF intersecting targets. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. A total of 10 core targets were screened by protein-protein interaction, and molecular docking was used to further validate the binding ability of the compounds to the core targets. The network pharmacology and molecular docking results showed that Danshenol A, isorhamnetin, Ginsenoside-Rh4, quercetin, and kaempferol might be the main active compounds in the treatment of IPF by YTG, whereas MAPK1, MAPK3, EGFR, and SRC are the core targets while PI3K/AKT pathway and MAPK pathway are the main signaling pathways through which YTG regulates relevant biological processes to intervene in IPF. This study shows that YTG can treat IPF by inhibiting the epithelial-mesenchymal transit process, fibroblast proliferation, fibroblast-to-myofibroblast conversion, myofibroblast anti-apoptosis, collagen expression, and other mechanisms.YTG can be widely used as an adjuvant therapy for IPF in clinical practice, and this study provides the basis for subsequent experimental studies. Its irreversible lung damage results in a high clinical mortality rate, with a median survival of only 2.5 to 3.5 years after diagnosis. In terms of treatment, pirfenidone and nintedanib are approved by the US Food and Drug Administration as drugs with multiple pharmacological mechanisms for the treatment of IPF but with issues of tolerability. Lung transplantation, a treatment that patients less frequently undertake due to the high number of comorbidities in the elderly, postoperative care, and financial issues, is considered the only cure for IPF. As the global aging problem increases, IPF is expected to become a global health issue. Based on the disease characteristics of IPF, finding new therapeutic targets and effective treatment options is essential to slow the progression of or even prevent IPF.Idiopathic pulmonary fibrosis (IPF) is the most common form of chronic idiopathic interstitial pneumonia, with a high incidence and a more insidious onset in elderly individuals. The main clinical manifestations of IPF are progressive dyspnea and dry cough, which seriously affect the quality of life of patients.With thousands of years of development and experience, traditional Chinese medicine (TCM) has a complete and rigorous theoretical basis for understanding the etiology of IPF and has also achieved good clinical treatment of this disease. TCM prescriptions have become a routinely used adjunctive therapy in clinical practice. According to the TCM, due to external evil or the patient’s own Qi deficiency, the lungs become dysfunctional, and phlegm and blood stasis bind in the lung. Hence, the TCM pathogenesis of IPF is lung atrophy without enough nourishment. Yifei Tongluo granules (YTG) consists of 9 Chinese medicines, namely, 15 g ginseng, 12 g Ophiopogon Japonicus, 9 g Schisandra chinensis Fructus, 30 g Astragali Radix, 9 g Ginkgo Semen, 3 g leech, 12 g Fritillaria thunbergii, 15 g Mori Cortex, and 15 g Salvia miltiorrhiza. When given this prescription, Qi-Yin can be nourished and phlegm and stasis can be resolved. Although the use of YTG is clinically effective in alleviating symptoms, such as chest tightness, wheezing, and dry cough in patients, its active compounds, possible targets of action, and signaling pathways regulated by it need to be explored further. In this study, we used the network pharmacology approach to collect practical bioactive components and relevant disease targets of YTG for the treatment of IPF. The core proteins were screened by protein-protein interaction (PPI) network analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to illustrate the biological functions of the intersecting genes and the cellular pathways involved. The final molecular docking technique verifies the affinity between the screened receptors and ligands. We illustrated the targets and signaling pathways through which YTG inhibits IPF using a network pharmacology approach and molecular docking.The composition of a TCM prescription is complex, and its efficacy results from the combined action of multiple compounds, targets, and pathways. Various compounds may modulate the biological network. Network pharmacology has become an effective research method for investigating the active compounds and the mechanism of pharmacodynamic action of compounds by constructing and resolving the biological network enrichment relationship of drug-target-disease.https://old.tcmsp-e.com/tcmsp.php). The screening criteria were presented as follows: oral bioavailability ≥ 30% and drug similarity ≥ 0.18. The SwissADME website (http://www.swissadme.ch/index.php) was used to screen compounds with biological activity contained in ophiopogon japonicus and leech for gastrointestinal absorption. “High” and drug-likeness (drug-like properties) in Lipinski, Ghose, Veber, Egan, and Muegge, 3 or more of the 5 algorithms were selected as “YES.” Next, the standard smile nodes of each compound were obtained using the PubChem (https://pubchem.ncbi.nlm.nih.gov/). Lastly, the standard smile nodes of the identified compounds were uploaded to the SwissTarget (http://www.swisstargetprediction.ch/) database to predict their potential targets. Meanwhile, we searched the OMIM (https://www.omim.org/) database, Gene Cards (https://www.genecards.org/) database, and Therapeutic Target Database (http://db.idrblab.net/ttd/) database with “idiopathic pulmonary fibrosis” for IPF-related disease targets. Also, we integrated and deduplicated those targets obtained from the 3 databases and used the Uniprot (https://www.uniprot.org/) database to standardize the names of the obtained disease targets.The YTG compounds were obtained from the TCM Systems Pharmacology (TCMSP http://bioinfogp.cnb.csic.es/tools/venny/) was used to screen common potential therapeutic targets between YTG and IPF-related targets, and the intersected genes were inputted into the STRING database (https://string-db.org/) to obtain the PPI. The PPI network was visualized by Cytoscape 3.9.1 software.Venny 2.1.0 (https://david.ncifcrf.gov/tools.jsp), an annotated database of biological information, and the results were visualized using bioinformatics (http://www.bioinformatics.com.cn/).Intersecting targets of YTG and IPF were analyzed for GO and KEGG enrichment using the DAVID2021 platform (https://www.rcsb.org/). The water molecules of the core target proteins were removed using PyMOL software. Then, we used Autodock4 software to affix hydrogen atoms to target proteins, and charge calculation and nonpolar hydrogens were merged into their corresponding carbons. The processed receptors were saved in PDBQT format. Compounds were also hydrogenated. Nonpolar hydrogens were merged to their corresponding carbon rotations and set to auto-twist using the AutoDock4 software and then set to a ligand and exported to PDBQT format. The processed ligands and receptors were docked using AutoDock Vina software. If the binding energy is <0 kJ · mol−1, then the compound can spontaneously bind to the target protein. We used Discovery Studio software to visualize the docking results.We obtained 3D structures of compounds (SDF file format) at the PubChem website and converted the SDF into mol2 format using OpenBabel software. The high-resolution 3D structures of the core target protein were downloaded from the PDB database , and the target proteins with a score > 0 were selected according to their probability. After removing the duplicate proteins, 1152 YTG targets were obtained. We searched the OMIM, Gene Cards, and Therapeutic Target Database databases with “Idiopathic pulmonary fibrosis” for IPF-related disease targets, integrated and deduplicated the targets obtained from the 3 databases, and used the UniProt (https://www.uniprot.org/) database to standardize the names of the acquired disease targets. A total of 1752 IPF-related targets were obtained. Venny 2.1.0 (http://www.liuxiaoyuyuan.cn/) was used to screen targets between YTG and IPF-related targets. A total of 330 intersected targets were obtained. The network of “drugs-compounds-intersecting targets of YTG and IPF” was analyzed using Cytoscape 3.9.1 software  database, wherein the species was limited to “Homo sapiens,” the minimum required interaction score was set to 0.9, and the independent target protein nodes were hidden. A PPI network with 330 nodes, 6782 edges, an average node degree of 41.1, and a PPI enrichment P value: <1.0e−6 was obtained  GO function enrichment entries, including 669 biological process (BP), 141 molecular functions (MF), and 89 CC terms in total. GO terms were sorted by -log10 , and the top 20 terms in BP, CC, and MF were selected and plotted in the column chart . The top 20 potential signaling pathways with the highest gene counts are presented as bubble plots in Figure P value, and the bluer the bubble color means higher significance of the signaling pathway. The 330 intersection genes were mainly involved in KEGG pathways, including tumor pathway, PIK3-AKT signaling pathway, MAPK signaling pathway, proteoglycans in cancer, lipids and atherosclerosis, and Kaposi sarcoma-associated herpesvirus infection. The GO and KEGG analysis results indicated that YTG might regulate the PI3K-AKT signaling pathway and MAPK signaling pathway to interfere with apoptosis, cell proliferation, and inflammatory response to treat IPF.A total of 148 YTG-IPF-related pathways were screened by KEGG enrichment analysis  process by reversing the expression of E-cadherin and α-SMA. In addition, kaempferol may act as an antifibrotic agent by regulating autophagy and inhibiting the expression of inflammatory factors. EGFR levels were abnormally elevated in IPF fibroblast lesions and fibroblasts isolated from IPF lungs, and nintedanib blocked EGFR activation in a concentration-dependent manner to exert an antifibrotic effect on lung fibrosis. SRC, a nonreceptor tyrosine kinase, could be activated by binding to pro-fibrotic cytokines  and leads to IPF by inducing inflammatory responses, fibroblast differentiation, and EMT. EGFR and SRC play critical roles in regulating cell BPs, such as IPF-related epithelial cell apoptosis, fibroblast proliferation and differentiation, and epithelial cell migration. As discussed earlier, EGFR binding to receptor tyrosine kinases activates the MEK-ERK signaling cascade, and MAPK1 (ERK2) and MAPK3 (ERK1) are essential compounds of the Ras-Raf-MEK-ERK signaling cascade. Animal experiments show that TGF-β1 leads to fibroblast-myofibroblast differentiation in mice by increasing the expression of MEK1/2 and ERK1/2 phosphorylation. The mouse MEK-specific blocker ARRY selectively inhibits TGF-a-induced phosphorylation of ERK1/2 in fibroblasts, inhibits fibroblast proliferation, and improves lung compliance to achieve antipulmonary fibrosis effects.Through PPI network construction, we predicted that the core biological targets of YTG for IPF treatment were STAT3, SRC, MAPK3, MAPK1, RELA, PTPN11, JUN, MAPK14, STAT1, and EGFR. Among them, EGFR, MAPK3, MAPK1, and SRC showed the best binding energy with our previous YTG-screened compounds. EGFR is an epidermal growth factor receptor that binds to the receptor as a ligand. It activates the PI3K-AKT signaling pathway and MEK/ERK signaling pathway to transmit extracellular signals into the cell.,31 Epidermal growth factor receptors, such as EGFR, regulate tumor cell proliferation, apoptosis, angiogenesis, and lymphangiogenesis in small cell carcinoma of the lung mainly through the PI3K-AKT pathway and the Ras/Raf/Mek pathway. At the same time, these 2 pathways also play an essential role in regulating the proliferation and apoptosis of IPF fibroblasts and EMT.,32 The study confirms that the Ras-ERK pathway inhibits the TGF-β1-induced EMT process in mouse lung epithelial type-II cells. In human lung epithelial type-II cells, TGF-β1 activates the EGFR-RAS-ERK signaling pathway through the transcriptional regulator zeb1, which leads to fibroblast activation. Satish K. Madala et al demonstrate that the MEK inhibitor ARRY improved lung fibrosis in TGF-a mice by inhibiting lung mesenchymal cell activation without affecting the PI3K-AKT signaling pathway. Studies have shown that the upregulation of the PI3K-AKT signaling pathway leads to mTOR activation and exacerbates bleomycin-induced pulmonary fibrosis when lung epithelial cell autophagy levels fail to inhibit apoptosis. In addition, AKT inhibitors effectively inhibit fibroblast differentiation in lung tissue and reduce collagen I and III levels. This finding suggests that YTG may achieve antifibrotic effects through combined signaling pathway inhibition.To illustrate the functional and signaling pathways of the 330 protein targets associated with YTG for IPF, we performed GO and KEGG analyses. The results showed that the target genes were mainly enriched in the positive regulation of ERK1 and ERK2 cascade, inflammatory response, response to hypoxia, positive regulation of MAP kinase activity, positive regulation of cell proliferation and cell migration, and other biological pathways. KEGG analysis showed that 330 target genes were enriched in 148 signaling pathways, indicating that YTG acts as a treatment for IPF by regulating multiple signaling pathways. Among them, the PI3K-AKT pathway and MAPK pathway showed high enrichment. In recent years, IPF can be viewed as a proliferative lung disease with similar genetic alterations, pathogenic mechanisms, and pathological features between IPF and lung cancer, particularly nonsmall cell carcinoma, and even in treatment strategies.We used molecular docking to predict the binding activity of the bioactive components of YTG to the target proteins. The results showed that the binding energies of our 10 preselected target proteins to 16 bioactive components were all <−5 kcal/mol. Among them, MAPK3-DA, MAPK1-DA, and MAPK3-Sugiol showed the lowest binding energy , indicating that these 3 receptor-ligand pairs were the most stable in binding. Based on this finding, we suggest that DA is the most prominent compound in YTG, and MPAK3 MAPK1 is the most critical target of YTG for treating IPF.In this study, we used a network pharmacology and molecular docking approach to predict the potential mechanism of YTG for treating IPF. The results showed that DA, isorhamnetin, quercetin, and kaempferol were the main active compounds in YTG. Meanwhile, MAPK3, MAPK1, EGFR, and SRC are the potential therapeutic targets of YTG for treating IPF. YTG treats IPF by regulating the PI3K-AKT signaling pathway, MAPK signaling pathway, and other pathways that inhibit the EMT process, fibroblast proliferation, fibroblast-to-myofibroblast cell conversion, apoptosis resistance of myofibroblasts, collagen expression, and other mechanisms. Our study provides a reference for further research into the molecular mechanisms of YTG in the treatment of IPF.However, this study has some limitations. First, the TCMSP was used to identify the compounds in YTG. Still, probably not all chemical compounds with biological activity were included, and compounds with unproven physical activity were excluded. Thus, the accuracy and timeliness of the database need to be further verified. Second, we identified the essential active compounds of YTG through a network pharmacological approach. Still, these compounds only do not account for the complete mechanism and pathway of action of YTG in treating IPF and only serve as supporting evidence for the efficacy of YTG. Finally, the effects of target proteins identified through the pharmacology network approach in the ponderous and sophisticated human system have not been fully explored and explained. Based on the experimental validation, we believe that YTG has excellent research potential in antipulmonary fibrosis, and the targets and pathways of action of the vital active compounds of YTG are worthy of further study and exploration.Conceptualization: Xinhua Jia.Data curation: Yuan Hou, Guoyu Wang, Shuo Han, Xinhua Jia.Investigation: Guoyu Wang.Methodology: Guoyu Wang.Software: Shuo Han, Xinhua Jia.Supervision: Huaman Liu.Validation: Yuan Hou.Visualization: Yuan Hou, Shuo Han, Huaman Liu, Xinhua Jia.Writing – original draft: Yuan Hou.Writing – review & editing: Huaman Liu.