JinGuanLan (JGL) formula is a traditional Chinese medicine (TCM) developed by the Department of Pharmacology at the First Hospital of Lanzhou University. The network pharmacology approach was applied to determine the potential active compounds, therapeutic targets, and main pathways of the JGL formula to evaluate its application value in acne vulgaris.  Data on the active compounds and their related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Acne vulgaris-related targets were searched from the Online Mendelian Inheritance in Man (OMIM) database, GeneCards Database, Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), and DisGeNET Database. Targets intersecting between JGL- and acne vulgaris-related targets were chosen as potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets was visualized using Cytoscape software based on the PPI data collected from the STRING database. Three topological features, namely, “Degree,” “MCC,” and “EPC” of each node in the PPI network were calculated using the cytoHubba plugin of Cytoscape to excavate the core targets. R program was used for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the potential therapeutic targets. Finally, the compound–target–pathway network was constructed.  Among the 148 active compounds that were identified, quercetin and kaempferol showed the highest degree of target interaction and thus may play essential roles in the pharmacological effect of the JGL formula for acne treatment. Among the 97 potential therapeutic targets that were screened out, the 6 core targets were TNF, JUN, IL6, STAT3, MAPK1, and MAPK3. A total of 2260 terms of GO enrichment analysis were obtained, including 2090 for biological processes (BP), 37 for cellular components (CC), and 133 for molecular function (MF). A total of 156 enriched KEGG pathways were identified, including TNF, IL-17, Th17 cell differentiation, MAPK, PI3K-Akt, T cell receptor, and Toll-like receptor signalling pathways.  This work showed that the JGL formula might reverse the pathological changes associated with acne vulgaris through its antiinflammatory effect and regulate the excessive lipogenesis in sebaceous glands via different signalling pathways. This new drug has application value and is worthy of further research and development. Propionibacterium acnes (P. acnes), infundibular hyperkeratinisation, sebum production alteration, and inflammation [P. acnes [Acne vulgaris is a chronic inflammatory cutaneous disorder affecting the pilosebaceous unit. Acne ranks eighth in the top ten most frequent diseases worldwide, and acne vulgaris has a prevalence rate of 94% according to the Global Burden of Disease Project , 2. Althammation . This coammation . The infammation . The majP. acnes . Most ChP. acnes . With thTCM is a comprehensive medical system that has been used in China for thousands of years and is also becoming popular in Western countries due to its therapeutic efficiency and few side effects , 11. OneLonicerae Japonicae Flos , Licorice , Isatidis Radix , Fortunes Bossfern Rhizome  and Hedysarum Multijugum Maxim or Astragali Radix . Most existing Chinese medicines for acne treatment contain Lonicerae Japonicae Flos, Licorice, or Astragali Radix as monarch drugs [JinGuanLan (JGL) is a new herbal formula developed by the Pharmacology Department at the First Hospital of Lanzhou University. This formula comprises five medicinal herbs, namely, ch drugs . GC is ach drugs . JYH canch drugs , 14. HQ ch drugs . BLG hasch drugs . ReportsNetwork pharmacology is an emerging approach that integrates network biology with poly-pharmacology . This cohttps://old.tcmsp-e.com/tcmsp.php) to explore all the candidate compounds of the five herbal medicines in the JGL formula. Active compounds were screened out according to the pharmacokinetic activity parameters (ADME) and must meet the following standard screening criteria: oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 [The Latin names of each herb were entered into the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform version 2.3 TCMSP  ≥ 0.18 –22.https://www.uniprot.org/) was used to standardize the target gene names and remove invalid targets; only ‘Reviewed (Swiss-Prot)' and ‘Homo sapiens' target genes in UniProt were selected to ensure prediction accuracy [The corresponding targets of each active compound (JGL-related targets) were acquired from TCMSP. The UniProt database (accuracy .https://www.omim.org/), GeneCards Human Gene database (https://www.genecards.org/), Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/), Therapeutic Target Database (TTD) (http://bidd.nus.edu.sg/BIDD-Databases/TTD/TTD.asp), and DisGeNET Database (https://www.disgenet.org). All the search results were then merged, and the duplicate targets were removed to select all the acne vulgaris-related targets.‘Acne vulgaris' was used as a keyword to search for acne vulgaris-related targets in the following five databases: Online Mendelian Inheritance in Man database (OMIM) (http://bioinfogp.cnb.csic.es/tools/venny/index.html).Targets intersecting between the JGL- and acne vulgaris-related targets were selected as the potential therapeutic targets of JGL in treating acne vulgaris using the Venny 2.1.0 online tool (https://string-db.org/) to obtain the data of the PPI of JGL formula and acne vulgaris targets. In STRING, the organism was set as ‘Homo sapiens', and the ‘highest confidence score of 0.9' was defined as a significant interaction score with hidden disconnected nodes in the network [The potential therapeutic targets were uploaded into the Search Tool for the Retrieval of Interacting Genes database (STRING) version 11.0 b , and maximal clique centrality (MCC), were analysed using the Cytoscape plugin CytoHubba to assess the Hub nodes and subnetworks within the PPI network. Finally, the top 10 nodes for each parameter were chosen as core targets that play an essential part in the PPI network .P value cut-off < 0.05 and q-value cut-off < 0.05 as screening criteria. GO enrichment analysis included the following terms: biological processes (BP), cellular components (CC), and molecular function (MF). The results were presented in visual bubbles, bar charts, and data tables.The names of the potential therapeutic targets were inputted into the R program (version 3.6.3) to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the A total of 802 compounds in the JGL formula were obtained from the TCMSP database. Among them, 181 were identified as potential active compounds using the screening criteria: OB ≥ 30% and DL ≥ 0.18. Out of the 181 potentially active compounds, 19 without corresponding targets were removed because they were not expected to interact with human targets. Finally, 162 active compounds were retrieved. The number of active compounds and associated targets in each herb of the JGL formula are listed in A total of 3123 potential targets were discovered for all compounds using TCMSP databases: 337, 1769, 106, 462, and 449 targets for BLG, GC, GZ, HQ, and JYH, respectively. UniProt was used to standardize the target gene names and yielded 3048 targets. Finally, 264 JGL-related targets were obtained after the redundant terms were eliminated. Basic information for all active compounds and related targets is shown in Supplementary file 1, Tables A total of 148 active compounds were screened, and 264 JGL-related targets were obtained. A visual ‘herb-compound-target' network was then constructed using Cytoscape 3.8.1 . The netA total of 732 targets related to acne vulgaris were identified using the MultiSource Database Integration Method. Among them, 524, 7, 95, 19, and 87 targets were from GeneCards, OMIM, CTD, TTD, and DisGeNET databases, respectively. After the redundant terms were eliminated, 628 potential targets related to acne vulgaris (acne vulgaris-related targets) were obtained. Detailed information for the potential target genes of acne vulgaris is listed in Supplementary file 2, Afterward, 97 intersecting targets between 264 JGL-related targets and 628 acne vulgaris-related targets were identified as potential therapeutic targets, and a Venn diagram was drawn using the Venny 2.1.0 online tool .The 97 potential therapeutic targets were imported into the STRING database for PPI data analysis. The PPI network of potential therapeutic targets was visualized via the Cytoscape software using the PPI data collected from the STRING database . The netP value cut-off < 0.05, the top 10 GO terms of each term were selected as significantly enriched terms  on keratinocytes and the sebocytes of the pilosebaceous unit, thus activating signalling cascades and inducing the release of inflammatory mediators such as IL1ß, TNF-α, IL8 (CXCL8), and IL6 [D and A [α is one of the most critical proinflammatory cytokines and plays an essential role in the immune response during inflammation. TNF stimulates intracellular signalling cascades to activate mitogen-activated protein kinases (MAPKs), which have great activity and participation in the production of proinflammatory mediators. TNF could also activate matrix metalloprotease (MMPs) for tissue remodelling [P. acnes). Activated MAPK pathways are involved in signalling cascades that activate several transcription factors, such as nuclear factor-kappa B. These three MAPK pathways are the potential targets of many antiinflammatory drugs because they regulate inflammatory mediators at the transcriptional and translational levels [ and IL6 , 36. The and IL6 . Th17 ce[D and A . TNF- α odelling , 39. MAPl levels .α in P. acnes-stimulated cells and the phosphorylation of JNK, ERK, and p38 MAPK signalling pathways and the production of TLR-2 in P.acnes-stimulated human keratinocytes and THP-1 cells. In vitro experiments proved that quercetin suppressed MMP-9 in two cell lines exposed to P. acnes [κB and MAPK pathways [Network and enrichment analyses revealed that many compounds and targets have a role in the inflammatory and immune responses of acne vulgaris. Kaempferol, quercetin, formononetin, and luteolin could be the main compounds responsible for the antiinflammatory effect of the JGL formula. Quercetin significantly suppresses the secretion of proinflammatory cytokines IL8, IL1ß, IL6, and TNF-P. acnes . KaempfeP. acnes  and prosP. acnes . In vitrpathways . The corα significantly induces lipogenesis in the sebaceous gland via JNK and PI3K/Akt pathways as reported in a previous in vitro study on inflammatory acne that TNF-α increases lipid droplet accumulation in the sebaceous gland cells [In sebocytes, FoxO1 regulates SREBP-1, a major transcription factor of androgen receptor (AR) regulation. In acne pathogenesis, the activation of the PI3K-Akt signalling pathway inhibits FoxOs and therefore increases lipogenesis , 44. Liknd cells .The anti-acne effect of licorice flavonoids, such as kaempferol, quercetin, naringin, formononetin, and luteolin, might be mediated by the inhibition of the PI3K-Akt pathway, leading to an increase in FoxO1 expression level in the skin to suppress the mTORC1 biological activity, which in turn finally restrains SREBP-1 expression and lipid synthesis . LuteoliIn the JGL formula, 148 active compounds, 97 potential therapeutic targets, and 6 core targets were identified by network pharmacology. Among the active compounds, quercetin and kaempferol showed the highest degree of target interaction and might play a critical role in the pharmacological effect of the JGL on acne vulgaris. The therapeutic effect of the JGL formula was closely related to inhibiting inflammation and regulating excessive lipogenesis in sebaceous glands through specific pathways, namely, Toll-like receptor, TNF, Th17 cell differentiation, IL17, MAPK, and PI3K-AKT signalling pathways. These pathways are among the most important pathogenetic mechanisms of acne vulgaris.


Arabidopsis, and rice. Almost all of the nodes were assembled into one major network for each of the four species, which provided opportunities for robust crosstalk and topological analysis, and knowledge graph construction.The crosstalk between signaling and metabolic pathways has been known to play key roles in human diseases and plant biological processes. The integration of signaling and metabolic pathways can provide an essential reference framework for crosstalk analysis. However, current databases use distinct structures to present signaling and metabolic pathways, which leads to the chaos in the integrated networks. Moreover, for the metabolic pathways, the metabolic enzymes and the reactions are disconnected by the current widely accepted layout of edges and nodes, which hinders the topological analysis of the integrated networks. Here, we propose a novel “meta-pathway” structure, which uses the uniformed structure to display the signaling and metabolic pathways, and resolves the difficulty in linking the metabolic enzymes to the reactions topologically. We compiled a comprehensive collection of global integrative networks (GINs) by merging the meta-pathways of 7077 species. We demonstrated the assembly of the signaling and metabolic pathways using the GINs of four species—human, mouse, The online version contains supplementary material available at 10.1007/s42994-022-00078-1. Proteins are a special group of compounds in living cells, featured by their distinctive functions compared to the other compounds. The proteins involved in the biosynthesis or degradation of other compounds form the metabolic pathways, while a large number of protein enzymes sense and transfer the intra-/extra-cellular signals by signaling cascades, forming the signaling pathways. The crosstalk of signaling pathways and metabolic pathways attracts growing attentions for researches in both human disease and plants. A recent review summarized the crosstalk between signaling pathways and metabolic reprogramming in colorectal cancer facilitated by protein kinases such as AKT and c-MYC , an edge usually connects an upstream protein (functioning as an enzyme in the reaction) and a downstream protein (representing the substrate and product of the reaction). On the other hand, an edge in a graph of metabolic pathway usually connects a substrate and a product, with the enzyme(s) sitting on the edge of the arrow. Although there are number of databases that contains both of the two types of pathways [such as KEGG Kanehisa , WikiPatThe differences in the displaying style of the two types of pathways cause a series of inconveniences. First of all, they confuse the topological analysis since the edges in the two types of pathways have different meanings. Topological analysis can provide in-depth biological insight, and has been widely applied in the analysis of biological networks, i.e., the STRING database which is a popular protein–protein interaction (PPI) network database  will open the opportunities for topological analysis of the crosstalk between signaling and metabolic networks, enabling the interpretation of multi-omics data and mining of the systemic changes at a global scope.To integrate the signaling and metabolic pathways conceptually, we decomposed the common data structure of the two types of pathways into the structure of chemical reactions Fig. A, B, whiThere are four major types of the edges, including type1, connecting the protein subunits to the protein complex, type2, connecting the enzyme to the intermediate, type3, connecting the substrate to the intermediate, and type4, connecting the intermediate to the product. Although we retained these information in the data, the final graphs constructed from the data do not discriminate the four types of the edges, since all of them represent the process of transformation of the starting nodes to the ending nodes.Based on the design of the structure of individual signaling/metabolic reaction, we were able to build the meta-pathways integrating the heterozygous relations between molecules from signaling and metabolic pathways Fig. C. For a Homo sapiens), mouse (Mus musculus), rice (Oryza sativa), and Arabidopsis  for further analysis. Most of the pathways of each species can be assembled into a major network, respectively  numerically, with the largest sub-network designated to be subnet 1 . However, the homology between the mammals and plants was very low, with only four subnets matched between human and Arabidopsis  but no incoming connections. We found that these group of nodes were mostly the protein enzymes catalyzing the metabolic reactions, indicating that these protein enzymes might not receive regulation signals from MAPK signaling at the PTM level. However, the possibility that the expressions of these genes were regulated by the transcription factors involved in MAPK signaling could not be excluded, but was out of the scope of this study.To have an overview of all the connecting paths between the two pathways, we arranged the nodes (except intermediate nodes) in bipartite layout for the four species of human, mouse, To conclude, we designed a universal data structure for both signaling and metabolic pathways, and converted the KEGG pathway collections of 7077 species into meta-pathways, then constructed a global integrative network merging the signaling and metabolic pathways for each of them. The GINs of the four-representative species showed that the proteins and compound interactions were highly integrated, with less than 11% of the nodes left in pieces of sub-network. Detailed analysis of the subnet pieces revealed distinct features of the GINs between mammals and plants. Crosstalk analysis of TCA cycling and MAPK signaling pathways showed two closest paths connecting the two pathways in human. Our work integrates the signaling and metabolic pathways at data structure level, enables the topological analysis of the integrated pathway networks, and provides references for crosstalk analysis between signaling and metabolic pathways.The definitions of nodes and edges in signaling pathways and metabolic processes are completely different in current databases, which leads to the chaos when integrating the signaling and metabolic pathways. As a result, the signaling and metabolic pathways were graphically integrated retaining the original definitions of nodes and edges, which was comprehensible for human but hindered topological analysis on the integrated networks. Here, we present a novel structure of “meta-pathway” which is universally suitable for storing the complex relations of signaling pathways, metabolic processes, subunits of protein complexes, and the activation/inhibition of compounds/proteins. These types of biological processes can be easily integrated after converting the pathway collections to meta-pathways. We processed 7077 species pathway collections from KEGG, which provided a ground resource of global integrative networks for further analysis.One of the most distinct differences observed from the four species’ GINs is the proportion of the compounds and compounds related intermediates. The high ratio of the compounds and compounds related intermediates in plants suggests that plants may rely on complex metabolite processes compared to signaling pathways to survive in the normal and stressed environments, while mammal cells have more complicated signaling system. However, other explanations also exist, one of which is the different resources, efforts and focus put into the researches of mammals and plants.The GINs of plants are more fragmented than mammals. This might due to the lack of the knowledge in plant pathways compared to mammals. The respective similarities of the fragments in mammals and plants may be due to the fact that mammals and plants have different pathway models in KEGG, and these fragments reflect the scattered knowledge of these models. We believe that with the combination of other pathway databases and the increase of the pathway knowledges, the gaps between the current subnet will be eventually filled and pathways will be assembled into a complete global network.The GINs integrate signaling and metabolic pathways, however, there are many other types of biological processes in cells to facilitate the coordinated change of cellular state. One important and well-established regulation network is the TF meditated gene regulatory network (GRN), which is associated with all coding gene’s expression. Moreover, TFs are also the “destinations” of the signaling pathways, since the signaling cascades converge on TFs to modulate the gene expression pattern in response to the extra-/intra- cellular signals. Integration of the GRN into the GIN will greatly extend the scope of the network, however, several difficulties must be addressed: the TFs’ effect on gene expressions is variable depending on the tissue, cell type and developmental stages, exemplified by the use of SCENIC to quantify the activity of each regulon in each cell using single-cell RNA-seq data  + D is stored as a group of three members, the protein A, protein B or protein C, and protein D. To avoid chaos, we split this type of protein complex information into complex ABD and ACD. The application of this strategy was carried out using customized tree structure written in perl. Of note, homodimers were ignored in our strategy and stored as one single enzyme unit.For the signaling cascades, we extracted the relations of protein–protein relations “PPrel” and protein–compound relations “PCrel” from the kgml pathway files. Similar to the processing of reactions, we scanned and assembled the subunits into protein complexes, then stored the four types of relations in SIF format.We merged the meta-pathways of each of the 7077 species and removed the redundant relations which were resulted from the overlapping relations in different pathways. The resulting 7077 GIN files were in SIF format, which is compatible for many network displaying and analysis tools, i.e., cytoscape , using the keggGet function from the KEGGREST package and customized R script. The similarities were leveraged by the Jaccard score, defined by the ratio of the number of shared elements divided by the number of elements in the union of two sets:where A and B are two sub-networks composed of ko numbers and compound ids. The annotation of the sub-networks was done by using the search function of KEGG mapper tools, since common pathway enrichment tools do not consider the compounds. The naming of the specific sub-networks was done manually, since all of the sub-networks were only small pieces of the pathways.https://www.kegg.jp/pathway/map04724+C00064), the compound labeled “cAMP” activate PKA but actually links to C00064 (L-glutamine), which will result in an additional path linking TCA cycling and MAPK pathways. Since we didn’t find any direct evidence supporting the activation of PKA by L-glutamine, we removed this protein-compound relation in our GINs.The SIF files of the GINs of the four species were loaded into R, and were treated as edge lists to create igraph objects using “graph_from_edgelist” function from R package “igraph”. Since we observed several ambiguous and misleading information, such as the activation of oxytocin receptor (OXTR) by succinate ) extracted from the kgml file of cAMP signaling pathways, we manually curated the GINs to remove these relations. Furthermore, we also observed that in the graph of glutamatergic synapse Below is the link to the electronic supplementary material.


In total, data for 432 TCM active ingredients with clear pharmacological effects were obtained. Five critical RA-related genes were identified; CCL5 and CXCL10 were selected for molecular docking. Target prediction and network-based proximity analysis showed that dioscin could modulate 22 known RA clinical targets. Dioscin, asiaticoside, and ginsenoside Re could effectively inhibit in vitro cell proliferation and secretion of TNF-α and IL-1β in RA rat synovial cells. Using bioinformatics and computer-aided drug design, the potential small anti-RA molecules and their mechanisms of action were comprehensively identified. Dioscin could significantly inhibit proliferation and induce apoptosis in RA rat synovial cells by reducing TNF-α and IL-1β secretion and inhibiting abnormal CCL5, CXCL10, CXCR2, and IL2 expression.This study is aimed at screening potential therapeutic ingredients in traditional Chinese medicine (TCM) and identifying the key rheumatoid arthritis (RA) targets using computational simulations. Data for TCM-active ingredients with clear pharmacological effects were collected. Absorption, distribution, metabolism, excretion, and toxicity were evaluated. Potential RA targets were identified using the Gene Expression Omnibus (GEO) database, protein–protein interaction network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and potential TCM ingredients using AutoDock Vina. To examine the mechanisms underlying small molecules, target prediction, Gene Ontology, KEGG, and network modeling analyses were conducted; the effects were verified in rat synovial cells using cell proliferation assay. The activities of tumor necrosis factor TNF- Rheumatoid arthritis (RA) is a chronic, systemic, inflammatory, and progressive autoimmune disease that affects synovial joints and other organ systems . To dateCurrently, the available treatment of RA includes nonsteroidal anti-inflammatory drugs, glucocorticoid drugs, and biological macromolecular therapy; however, they are costly and often exert toxicity and cause side effects , 7. TherTraditional Chinese medicine (TCM) has a long history of RA treatment based on rich experiences in clinical applications. RA, a common type of arthritis, belongs to “Bi syndrome” category in Chinese medicine . Currentin vitro cellular experiments, in combination with existing literature analysis, to explore important targets for the treatment of RA and further screen out the active ingredients of TCM with potential therapeutic effects. In vitro experiments were performed to confirm these findings, with the aim of providing powerful methods and technical support for the treatment of RA with TCM. The workflow of this study is shown in Although several TCM compounds have been reported to exert good curative effects and minor side effects, the discovery of additional potential anti-RA ingredients from a large number of common small TCM molecules with clear pharmacological effects has important practical significance. In this view, we integrated bioinformatics, computer-aided drug design, and https://pubchem.ncbi.nlm.nih.gov/) was used to confirm the chemoinformatics data for each ingredient, including molecular name CAS, PubChem CID, molecular formula, canonical SMILES, and SDF files. A more authoritative and reliable database of active ingredients in commonly used Chinese medicines has been constructed.The active ingredients of common TCMs that have been proven to have clear pharmacological effects were collected from published literature in Chinese , 11. Thehttp://www.swissadme.ch/) [P, solubility, blood–brain-barrier (BBB) permeant, Pgp substrate, GI absorption, bioavailability score, synthetic accessibility, metabolic stability, Ames test, and hERG.The ACD/Labs software and SwissADME online system (dme.ch/)  were usehttps://www.ncbi.nlm.nih.gov/geo/), “rheumatoid arthritis” was queried to download the gene expression profile chip data related to RA. Perl language (version 5.32.0) was used to perform preprocessing of the original data, such as standardization, correction, and gene name annotation. The R language-based limma package [p.value < 0.05.In the GEO database ( package  (versionhttps://string-db.org/) to obtain the PPIs of the DEGs. The parameters were set as follows: organism, Homo sapiens; combined score threshold, 0.7. In addition, Gephi software  was used to visualize the PPI network, and cytoHubba in Cytoscape  [All selected genes were imported into the STRING database  , in whicThe GO database constructed by the GO Consortium in 2000 contains information for the biological process (BP), molecular function (MF), and cellular component (CC) of genes . A biolop value < 0.05 and q value < 0.05.In this study, clusterProfiler (version 3.14.3)  was usedThe key protein targets in the PPI network were identified, and the targets in the reported RA-related pathways were obtained from the KEGG pathway enrichment analysis as described in Sections http://www.rcsb.org/). PyMOL  was used to evaluate key target proteins to remove water molecules, impurities due to the presence of ions, and perform other changes. AutoDockTools  was used to hydrogenate and charge the processed protein-ligand; the processed PDB files were converted and saved in the PDBQT file formatThe PDB file for the potential key target proteins was downloaded from the PDB database (http://openbabel.org/) was used to convert the 2D SDF file into a 3D structure file. The original 3D structure was directly converted into a PDB file, and all small molecules were processed with minimum energy; finally, the PDB file was converted into a PDBQT fileThe information of small molecules was downloaded from PubChem in two SDF format files, 2D and 3D. OpenBabel  was used to perform a semiflexible molecular docking and calculate the affinity values  of all small molecules with potential key targets. Generally, the lower the affinity value, the greater the possibility that the small molecule binds to the receptor; therefore, according to the affinity value, the small molecules were sorted from small to large, and small molecules of TCM with potential anti-RA activity were screened outTo determine the docking center parameters, we referred to the binding site (region) of the protein receptor and the original ligand. The box size was defined as 30 × 30 × 30, and AutoDock Vina  [http://sea.bkslab.org/) [http://www.swisstargetprediction.ch/) [http://stitch.embl.de/), to predict their potential targets. In particular, the screening threshold precision for HitPick was set at 50%, the threshold MaxTc for SEA was set at 0.7, and the threshold probability for SwissTargetPrediction was set at 0.15. Molecular docking verification, GO function annotation, and KEGG pathway enrichment analyses were performed on the predicted potential targets.Corresponding canonical SMILES of the obtained small molecules were imported into online prediction systems, such as HitPick (hitpick) , SEA (htab.org/) , SwissTaion.ch/) , and STIA is the target set of small molecules of TCM, ‖A‖ is the number of the targets of A, B is the clinically known set of RA treatment targets, ‖B‖ is the number of the targets of B, d is the shortest path distance between two nodes in the human PPI background network, 〈dAA〉 represents the average distance between the target points of the component action, 〈dBB〉 represents the average distance between the key genes, and 〈dAB〉 represents the average distance between the small Chinese medicine molecular target set and the clinically known RA treatment target set on the background network.Next, we determined whether the small molecules of TCM screened have direct or indirect regulatory effects on the clinically known RA treatment targets. Given that some of these molecules may not directly act on certain RA targets but regulate the disease via the intervention of its neighboring targets, we further analyzed the small molecules of TCM using the calculation method of network proximity, based on the human PPI background network , using tSAB < 0, the small molecule target A of TCM and the clinically known RA treatment target set B are close in network topology, indicating that the small molecule of TCM can interfere with the target set A and regulate the clinically known RA treatment target set B. When SAB ≥ 0, A and B are separated in the network topology, indicating that the small molecule of TCM has no significant regulatory intervention on B. Therefore, we can calculate the value of SAB to further judge whether the small molecules of TCM can interfere with the known clinical disease target set by regulating some other target proteins to further improve the treatment of the disease.Generally, when http://db.idrblab.net/ttd/) and DrugBank (https://www.drugbank.ca/). Thereafter, the intersection of the RA targets collected using the two databases was used to construct the final known RA clinical treatment target set.For identifying the clinically known RA treatment targets, we queried across two databases: Therapeutic Target Database  and igraph (version 1.2.5) to complete the aforementioned programming, calculations, and analyses.Materials: rat synovial cells were induced using type II collagen and isolated in our laboratory.Reagents: PBS and RPMI 1640 medium were purchased from Gibco ; fetal bovine serum (FBS) was purchased from Biological Industries ; antibiotic-antimycotic, bovine serum albumin, and trypsin-EDTA (0.05%) were purchased from Life Technologies ; rat TNF-α and interleukin IL-1β enzyme-linked immunosorbent assay (ELISA) kits were purchased from LinkTech .Instruments: EPOCH type multifunctional microplate reader , 3111 CO2 incubator , CX23 microscope , and HFsafe-1200LC biological safety cabinet  were used for the in vitro studies.μl of fresh culture medium and 10 μl of Cell Counting Kit-8 (CCK-8) reagent were added to the wells. After incubation at 37°C for 4 h, the absorbance at 450 nm was measured with a microplate reader to calculate the IC50 values using Graph Pad (Version: 8.0) software.Synovial cells were cultured in RPMI-1640 medium supplemented with 10% FBS. After counting, the cells were seeded in 96-well culture plates at a density of 2000 cells/well. The cells were incubated with different concentrations of the small molecules of TCM. After 72 h, the culture medium was discarded; 90 5 cells/well. A total of 1800 μl of RPMI-1640 serum-containing culture medium per well was added to the culture, with an additional 200 μl drug solution. The final concentration of each sample was calculated as the IC50 concentration of the cells. The drug-free group was used as the arthritis model cell control group and cultured at 37°C in a 5% CO2 incubator for 72 h. The culture medium was collected, centrifuged at 400 × g for 5 min, and the supernatant was collected. ELISA was used for the detection of TNF-α and IL-1β, according to the manufacturer's instructions.The collagen II-induced arthritis (CIA) rat synovial cells were maintained and cultured in RPMI-1640 medium containing 10% FBS, 100 U/ml penicillin, and 100 U/ml streptomycin and were seeded in a 6-well culture plate at a density of 4 × 10The sample proteins collected as described in After collection of published literature data and confirmation from the PubChem database, chemoinformatic data of 432 effective small molecules of TCM and their corresponding 3D structure SDF files were obtained  evaluation showed that 330 small molecules exhibited optimal lipophilicity . The average value for the synthetic accessibility of small molecules was 4.69, suggesting that most compounds were easy to synthesize in the laboratory. The prediction of cardiac inhibitory toxicity (hERG) suggested that 19 small molecules had cardiotoxic properties , leukocyte migration (GO:0050900), immune response activation of the cell surface receptor signaling pathway (GO:0002429), and antigen receptor-mediated signaling (GO:0050851). Thirteen CC functions mainly include the outer side of the plasma membrane (GO:0009897), extracellular matrix containing collagen (GO:0062023), and membrane rafts (GO:0045121). In addition, 19 MFs involved receptor ligand activity (GO:0048018), cytokine activity (GO:0005125), G-protein-coupled receptor binding (GO:0001664), and endopeptidase activity (GO:0004175), among several others.KEGG pathway enrichment analysis revealed that RA-related DEGs mainly involved 31 related pathways. Among them, Th17 cell differentiation (hsa04659), rheumatoid arthritis (hsa05323), NF-kappa B signaling pathway (hsa04064), TNF signaling pathway (hsa04668), Th1 and Th2 cell differentiation (hsa04658), and Toll-like receptor signaling pathway (hsa04620) have been reported to be closely related to RA. A total of 30 targets enriched in these pathways were screened out, namely, BCL2A1, BIRC3, BLNK, CCL5, CD247, CD3D, CXCL10, CXCL11, CXCL6, CXCL9, GADD45B, HLA-DMB, HLA-DOB, HLA-DPB1, IL15, IL1R1, IL21R, IL2RG, ITGB2, JUN, JUNB, LCK, MMP9, PLCG2, PRKCB, SOCS3, SPP1, TLR7, TLR8, and TNFSF11, as shown in We further examined the intersection shown in AutoDock Vina was used to perform bulk molecular docking on the two protein targets CCL5 and CXCL10, and the affinity values of 432 small molecules of TCM to act on the targets were obtained, except for the very few unsuccessful dockings. The distribution of the molecular docking scores of all small molecules of TCM on CCL5 and CXCL10 is shown in In this study, we only listed the top 10 small molecules of TCM with 15 molecular docking scores, as shown in Among the small molecules, two saponins, polyphyllin II and dioscin, had affinities of less than -9.0. Based on molecular docking conformation analysis of these two small molecules, we hypothesized that a combination of dioscin with CCL5 and CXCL10 was most stable. Therefore, we selected dioscin for subsequent in-depth analysis, and the binding conformations of CCL5 and CXCL10 are shown in Figures To comprehensively analyze the potential anti-RA mechanism of dioscin, we used HitPick, SEA, SwissTargetPrediction, and STITCH to predict and screen the targets of dioscin. As a result, we found that dioscin had two potential targets, CXCR2 and IL2. In addition to the results of molecular docking screening presented in Next, we verified the molecular docking of dioscin for CXCR2 (PDB ID: 6LFO) and IL2 (PDB ID: 4NEM). The results showed that the docking score of dioscin and CXCR2 was -9.1, which was identical to the docking score of dioscin and IL2 (-9.1). The molecular binding conformation also indicated that dioscin could form stable interactions with these two protein targets, as shown in Figures Furthermore, GO function annotation and KEGG pathway enrichment analysis revealed that the four targets CXCR2, CCL5, CXCL10, and IL2 were mainly involved in GO functions, such as cellular calcium homeostasis (GO:0006874), calcium homeostasis (GO:0055074), cellular divalent inorganic cation homeostasis (GO:0072503), kinase regulator activity (GO:0019207), cytokine activity (GO:0005125), G-protein-coupled receptor binding (GO:0001664), and external side of the plasma membrane (GO:0009897). The KEGG pathway was mainly enriched in 10 pathways. The interaction pathways between viral proteins and cytokines and cytokine receptors (hsa04061), the interaction pathway between cytokines and cytokine receptors (hsa04060), chemokine signaling pathway (hsa04062), cytoplasmic DNA sensing pathway (hsa04623), epithelial cell signaling pathway in Helicobacter pylori infection (hsa05120), Chagas disease pathway (hsa05142), Toll-like receptor signaling pathway (hsa04620), TNF signaling pathway (hsa04668), influenza A (hsa05164), and human cytomegalovirus infection pathway (hsa05163) are shown in A total of 140 and 193 known RA targets were collected from the TTD and DrugBank databases, respectively. After the two sets were intersected, 23 common RA treatment targets were obtained: ABCG2, AKR1B1, ALOX5, CASP1, DHFR, DHODH, HRH4, IL2, JAK1, JAK2, JAK3, MMP9, NR3C1, PDE4A, PDE4B, PDE4D, PLA2G1B, PPARA, PTGS1, PTGS2, TLR7, TLR9, and TNF. Among them, IL2 is also a potential direct target of dioscin.SAB = −1.16 < 0), where 〈dAB〉 = 2.85, 〈dAA〉 = 4.62, 〈dBB〉 = 3.40. Therefore, we speculated that dioscin could indirectly regulate and intervene in other 22 known RA targets by directly acting on the four targets, i.e., CCL5, CXCL10, CXCR2, and IL2, in treating RA. Based on further analysis of the human PPI network, we found that CCL5, CXCL10, CXCR2, and IL2 interacted with 54 proteins in the human PPI network to regulate other therapeutic targets of RA, as shown in Using the algorithm, we obtained the proximity values of dioscin to the 23 clinically known RA treatment targets on the human PPI network (https://tcmspw.com/) and obtained 42 small molecules of TCM with anti-RA effects and corresponding therapeutic targets , asiaticoside (300 μg/ml), neferine (400 μg/ml), ginsenoside Re (300 μg/ml), polyphyllin II (800 μg/ml), Bupleurum saikosaponin A (750 μg/ml), and alpha-crocin I (1000 μg/ml), for in vitro experiments. Tripterygium glycosides (150 μg/ml) were used as a control for small molecules of TCM. Therefore, a total of eight small molecules of TCM were included in this experiment.Based on the results of molecular docking, we selected seven small molecules, namely, dioscin  were selected for follow-up research. Target prediction and κB ligands [Reportedly, chemokines play an important role in inflammatory cell infiltration in the joints of patients with RA , and che ligands . Studies ligands , and an  ligands . Similar ligands . Therape ligands . Studies ligands .Dioscoreanigra, D. zingiberensis, and D. fuzhouensis. Dioscin exerts many therapeutic effects on various diseases [Dioscin is found in medicinal plants such as diseases , 33, sucdiseases . In an adiseases . Moreovediseases . Other sdiseases  and exerdiseases . In addidiseases . In our In this study, we used an algorithm based on network proximity to innovatively detect the regulation and intervention ability of dioscin on the known clinically therapeutic targets of RA. The results clearly showed that dioscin, by acting directly on four targets, indirectly intervenes and affects 54 related proteins and eventually regulates the main therapeutic targets of RA. We found that, in the network of dioscin-regulated RA targets, UBC (degree = 18), ARRB1 (degree = 14), APC (degree = 8), VCAN (degree = 7), GRB2 (degree = 6), IL2RB (degree = 6), FGR (degree = 6), and GNA15 (degree = 5) had higher degree values. The discovery of these target proteins in the network provides new insights for further related research and could serve as an important reference for the development of other RA drugs.in vitro experiments confirmed that dioscin exerted significant regulatory effects on CCL5, CXCL10, CXCR2, and IL2. The series of integrated analysis methods performed in this study are expected to provide powerful technical support for the treatment of RA and development of novel molecules of TCM.In summary, this study comprehensively used bioinformatics, molecular docking-based virtual screening, network modeling, and other computational methods; collected 432 commonly used TCM active ingredients; and analyzed the overall ADMET characteristics of these small molecules. The GEO and PPI network analyses helped to identify several key target genes associated with RA, and molecular docking technology was used to screen the small molecules of TCM for the treatment of RA. Furthermore, we analyzed the potential treatment mechanism of RA through network proximity-based prediction, and


Entamoeba histolytica (E. histolytica) is an anaerobic parasite that causes Amoebiasis in the intestine or extraintestinal, with immunology, genetics, and environmental variables all playing a part in the disease’s development, but its molecular mechanism is unknown. One of the primary obstacles in understanding the etiology of Amoebiasis will be identifying the genetics profiling that controls the Amoebiasis network. By examining the gene expression profile of Amoebiasis and comparing it with healthy controls, we could identify differentially expressed genes (DEGs). DEGs were used to build the Amoebiasis protein interaction network and calculated its network topological properties. We discovered nine key hub genes (KHGs): JUN, PTGS2, FCGR3A, MNDA, CYBB, EGR1, CCL2, TLR8, and LRRK2 genes. The genes JUN and EGR1 were transcriptional factors (TFs) and up-regulated, others down-regulated. hsa-miR-155-5p, hsa-miR-101-3p, hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-16-5p are also among the essential miRNAs that have been demonstrated to be targeted by KHGs. These KHGs were primarily enriched in the IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway. miRNAs were grouped in various pathways, focusing on the TGF-β signaling pathway, human immunodeficiency virus 1 infection, insulin signaling pathway, signaling pathways regulating pluripotency of stem cells, etc. Amoebiasis KHGs  and their associated miRNAs are the primary targets for therapeutic methods and possible biomarkers. Furthermore, we identified drugs for genes JUN, PTGS2, FCGR3A, CCL2, and LRRK2. KHGs, on the other hand, required experimental validation to prove their efficacy. Entamoeba histolytica (E. histolytica) is an anaerobic parasite that causes Amoebiasis in the intestine or extraintestinal TCA-3′ [The JUN gene was up-regulated when C]TCA-3′ . JUN forC]TCA-3′ . When HIC]TCA-3′ . JUN is C]TCA-3′ .PTGS2 gene represents prostaglandin-endoperoxide synthase 2. The primary enzyme in prostaglandin formation, prostaglandin-endoperoxide synthase (PTGS), commonly known as cyclooxygenase, is a dioxygenase and peroxidase. PTGS is divided into two isozymes: constitutive PTGS1 and inducible PTGS2 , which rMyeloid cell nuclear differentiation antigen (MNDA) gene acts as a transcriptional activator/repressor in the myeloid lineage and plays a role in the granulocyte/monocyte cell-specific response to interferon. It stimulates the DNA binding of the transcriptional repressor protein YY1. MNDA is a protein-coding gene that is found in myeloid cells. Nodal marginal zone lymphoma and Parkinson’s disease 4, autosomal dominant, is two diseases linked to MNDA. NF-κB signaling and the innate immune system are two of its linked pathways.FCGR3A is the receptor for immunoglobulin γ's invariable Fc segment (IgG). Antibody-dependent cellular cytotoxicity is triggered by the binding of clustered antigen–IgG complexes exhibited on cell surfaces (ADCC). In the absence of an antigenic trigger, it does not bind free monomeric IgG, preventing incorrect effector cell activation . This prThe CYBB gene directs the production of a protein called cytochrome b-245, β chain. This protein is a component of a group of proteins that make up the NADPH oxidase enzyme complex, which is vital for the immune system. The α (CYBA) and β (CYBB) chains make up cytochrome b (-245). It’s thought to be a vital component of the phagocyte’s microbicidal oxidase system. One of five biochemical deficiencies linked to chronic granulomatous illness has been identified: CYBB deficiency (CGD) . The actThe CCL2 gene (C-C motif chemokine ligand 2) codes for a protein. Neural tube defects and human immunodeficiency virus type 1 are two diseases linked to CCL2. Folate metabolism and the TGF-β pathway are connected processes ,64. ProtTLR8 gene produces a Toll-like receptor (TLR) protein, which is involved in pathogen identification and innate immune activation. TLRs exhibit structural and functional commonalities from drosophila to humans. They identify pathogen-associated molecular patterns on infectious pathogens and mediate the generation of cytokines needed for successful immunity development. The expression patterns of the various TLRs are distinct. This gene is found on chromosome X near another family member, TLR7, and is primarily expressed in lung and peripheral blood leukocytes. TLR8 (Toll-like receptor-8) is a gene that codes for a protein. TLR8 is linked to immunodeficiency 98 and other diseases. Anemia, autoimmune hemolytic, autoinflammation, X-linked TRAF6-mediated NF-kB, and MAP kinase up-regulation in response to TLR7/8 or 9 activations is related mechanisms. Controls the host’s immunological response against diseases by recognizing RNA degradation products unique to bacteria, which are processed by RNASET2 . When itThe LRRK2 gene encodes a protein featuring an ankyrin repeat region, a leucine-rich repeat (LRR) domain, a kinase domain, a DFG-like motif, RAS, and GTPase, an MLK-like domain, and a WD40 domain. It belongs to the LRR kinase family. The protein is primarily found in the cytoplasm, although it can also be found in the mitochondrial outer membrane. Parkinson’s disease has been linked to mutations in this gene-8. ncRNAs engaged in Wnt signaling in hepatocellular carcinoma and the MAPK signaling pathway are two of the associated pathways . ProteinEGR1 codes for a zinc-finger protein that belongs to the EGR family of C2H2-type zinc-finger proteins. It’s a transcriptional regulator that’s found in the nucleus. Differentiation and mitogenesis are dependent on the products of the target genes it activates . StudiesE. histolytica-infected KHGs. hsa-miR-155-5p, hsa-miR-101-3p, hsa-miR-124-3p, hsa-miR-26b-5, and hsa-miR-16-5p are among the important miRNAs shown to be targeted by  KHGs. As a result, differential expression and network analyses were used to examine the gene expression profile after E. histolytica infection. miRNAs hsa-miR-155-5p, hsa -miR-101-3p, hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-16-5p present us a narrow window of novel therapeutic opportunity to target genes in E. histolytica infection. KEGG pathway enrichment depicted that miRNAs hsa-miR-155-5p, hsa-miR-101-3p, and hsa-miR-26b-5p are clustered in long-term potentiation, renal cell carcinoma, prolactin signaling pathway, pancreatic secretion, leukocyte transendothelial migration, insulin signaling pathway TGF-β signaling pathway, human immunodeficiency virus 1 infection, Signaling pathways regulating pluripotency of stem cells, etc.  was used.LIMMA package was used to analyze data and quantify DEGs.Cytoscape 3.8.2 was used for network data visualization.Cytoscape plugin CytoNCA was used for centrality measurement.Cytoscape plugin CytoHubba was used for MCC, DMNC, MNC, and EPC properties.http://biit.cs.ut.ee/gprofiler/convert) was used for probe ID conversion.Server (https://www.grnpedia.org/trrust/) was used for elucidating the TF–target interaction.TRRUST (v2.0) server (https://david.ncifcrf.gov/) used for GO-enrichment.DAVID 6.8 Web server (https://string-db.org/) was used to construct a PPI network.STRING v11 database (http://userver.bio.uniroma1.it/apps/mienturnet/) server was used to isolate the targets of KHG–miRNA.MIENTURNET(https://www.dgidb.org/) web-based database of drug–gene interactions was used for drug identification.DGIdb (Click here for additional data file.


To explore the possible mechanisms of Ephedra herb (EH) in the treatment of nephrotic syndrome (NS) by using network pharmacology and molecular docking in this study.  Active ingredients and related targets of EH were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the gene names corresponding to the proteins were found through the UniProt database. Then, target genes related to NS were screened out from GeneCards, PharmGKB, and OMIM databases. Next, the intersection targets were obtained successfully through Venn diagram, which were also seen as key target genes of EH and NS. Cytoscape 3.9.0 software was used to construct the effective “active ingredient-target” network diagram, and “drug-ingredient-target-disease (D-I-T-D)” network diagram. After that, the STRING database was used to construct a protein-protein interaction (PPI) network. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment involved in the targets were performed by the DAVID database and ClueGO plugin in Cytoscape. Finally, AutoDockTools software was used for molecular docking to verify the binding strength between main active ingredients and key target proteins.  A total of 22 main active ingredients such as quercetin, kaempferol, luteolin, and naringenin were obtained, which could act on 105 targets related to NS. Through PPI network, 53 core targets such as AKT1, TNF, IL6, VEGFA, and IL1B were found, which might play a crucial role in the treatment of NS. Meanwhile, these targets were significantly involved in PI3K-Akt signaling pathway, TNF signaling pathway, AGE-RAGE signaling pathway, hepatitis B, and pathways in cancer through GO and KEGG enrichment analysis. The docking results indicated that active ingredients such as kaempferol, luteolin, quercetin, and naringenin all had good binding to the target protein AKT1 or TNF. Among them, luteolin and naringenin binding with AKT1 showed the best binding energy .  This study indicated that the potential mechanism of EH in treating NS may be related to PI3K-Akt signaling pathway, TNF signaling pathway, and AGE-RAGE signaling pathway, which provided better approaches for exploring the mechanism in treating NS and new ideas for further in vivo and in vitro experimental verifications. Nephrotic syndrome (NS) is characterized by massive proteinuria (greater than 3.5 g per day), hypoalbuminemia (less than 30 g/L), hyperlipidemia, systemic edema, and various complications . As one Ephedra sinica Stapf, Ephedra intermedia Schrenk & C.A.Mey., and Ephedra equisetina Bunge [Ephedra herb (EH), also named as mahuang in Chinese, belongs to the stems of na Bunge . EH is ona Bunge . EH has na Bunge –13. Recena Bunge –16. Morena Bunge , 18. HowAs a new technology to analyze the potential mechanism of compounds or single herbs, network pharmacology has been widely used in the field of TCM. It is known that network pharmacology is an emerging discipline that designs drugs based on systems biology theory and biological system network analysis . So, netIn this study, network pharmacology and molecular docking methods were both used to screen the possible targets of EH intervention in NS and establish the network of EH active ingredients-targets-pathways-diseases, which may provide a basis for relevant experimental researches .http://tcmspw.com/tcmspsearch.php) database is an open and accessible database resource [Traditional Chinese Medicine Systems Pharmacology  with Homo sapiens as the selected species. The Cytoscape software (version 3.9.0) was used to construct a network between active ingredients of EH and their corresponding target points. In the network, each node represented a target gene or molecule, and the connecting line between two nodes represented the relationship between them. It meant the stronger the degree of the node, the greater the role of the target in the network [We took “mahuang” as the keyword and searched “Related Targets” in the TCMSP database to find target proteins corresponding to the active ingredients of EH . Then, t network .https://www.genecards.org), PharmGKB (https://www.pharmgkb.org/), and OMIM (https://www.omim.org) databases. The three databases are recognized as good references for the collection of disease targets. Results accessed from GeneCards were screened for the relevance score ≥ 1.00 as the screening index. The repeated target genes corresponding to NS and the active ingredients of EH were deleted by Excel. Finally, the intersection targets were obtained successfully through Venn diagram in the website http://bioinformatics.psb.ugent.be/webtools/Venn/.With the keyword of “nephrotic syndrome”, target genes related to NS were collected from GeneCards  was built by Cytoscape software (version 3.9.0) based on the interaction between drug (EH), active ingredients, intersection target genes, and diseases (NS).https://string-db.org/) is an online protein-protein interaction (PPI) analysis database. It can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct and indirect interactions [Homo sapiens, and the isolated proteins were hidden.The STRING database (ractions . Therefohttps://david.ncifcrf.gov/).To further explore the pathways of the disease, the intersection targets of EH and NS were entered in the DAVID database  was used to obtain the 2D structure diagrams of active ingredients, and we transformed them into 3D structure diagrams through Chem3D software (version 14.0). Then, protein crystal forms corresponding to the targets were obtained from the PDB database (https://www.rcsb.org/). After that, we imported them into PyMOL software (version 2.5) to remove water molecules. Next, the ingredient and corresponding protein were both processed by AutoDockTools 1.5.7 software and saved in pdbqt format as a ligand and a receptor, respectively. Finally, docking work was performed to analyze the results using PyMOL software and AutoDock Vina software (version 1.1.2).We selected two targets with the largest value in the PPI network as the receptors and chose the corresponding active ingredients as the ligands for molecular docking verification. Firstly, the PubChem database , active ingredients, target genes, and disease (NS) were imported into Cytoscape 3.9.0 software to construct the D-I-T-D network. As shown in We imported 105 overlapping targets into the STRING database to obtain the PPI relationship. As shown in P < 0.01). It showed that the predicted target genes of EH were mainly enriched in 540 biological processes (BP), 64 cellular components (CC), and 93 molecular functions (MF). According to the number of target gens, the top 10 enriched conditions of GO analysis were listed, respectively, in To further explore the mechanism of EH on NS more systematically, the intersection target genes were inputted into DAVID database for GO analysis and KEGG pathway analysis  as shown in The KEGG pathway enrichment was, respectively, completed via DAVID database and ClueGO plugin in Cytoscape. Through DAVID database, the KEGG pathway enrichment obtained a total of 114 pathways, of which the top 20 pathways were shown according to the size of the We selected AKT1 and TNF as the protein receptors because they had the highest degree value in the PPI network. Accordingly, the active ingredients including kaempferol, luteolin, quercetin, and naringenin were used as ligands for molecular docking verification. As shown in NS is a disease with considerable morbidity and high risk for relapse . DespiteAs is known, network pharmacology is a rapidly emerging discipline, which has the same thinking with TCM. In this study, we used network pharmacology theory and related tools to select the active ingredients and related targets of EH and linked them with NS. A total of 23 active ingredients with OB ≥ 30% and DL ≥ 0.18 were screened in the TCMSP database. From the “active ingredient-target” network diagram and “drug-ingredient-target-disease” network diagram, it could be seen that active ingredients including quercetin, kaempferol, luteolin, and naringenin were important nodes in the network. Besides, the network also intuitively displayed the specific relationship between EH and NS through 105 consensus genes.Among them, quercetin is a natural flavonoid extracted from EH, other herbs, and numerous fruits and vegetables, which is known as a powerful herbal antioxidant. Pharmacological studies have shown that quercetin has several biological activities, such as antioxidant, anti-inflammatory, and anticarcinogenic effects . More anThe PPI network of the intersection targets of EH and NS clearly showed that AKT1, TNF, IL6, VEGFA, IL1B, TP53, MAPK3, CASP3, JUN, and MMP9 were the top 10 nodes with the highest degree value, which interacted with multiple active ingredients and played a key role in the network diagram. AKT family exists in three different isoforms, and they are named AKT1, AKT2, and AKT3, respectively. These unique isoforms are differentially expressed in mangy signaling pathways according to the developmental stage. Among them, AKT1 is involved in the proliferation, activation, apoptosis, and cell survival of interstitial fibroblasts and glomerular mesangial cells . Kim et GO analysis can be used to make a significant annotation of gene products. Among them, BP enrichment showed the coupling effect and transport mode of proteins in biological pathways. The results of CC analysis proved that intersection proteins participated in the cellular environment. Through MF analysis, we could demonstrate that some protein receptor activities were regulated by drugs, such as protein binding, which was closely related to proteinuria . KEGG paα [α and additionally increasing the expression of anti-inflammatory cytokine IL-10 [α and IL-6 [The PI3K-Akt signaling pathway has been proposed to modulate diverse biological processes and plays a key role in podocyte apoptosis, which is connected with the progress of proteinuria and renal function . It has α . As one ne IL-10 . Furtherand IL-6 . The AGEand IL-6 . TherefoThe results of molecular docking were consistent with the results of network pharmacology because active ingredients such as kaempferol, luteolin, quercetin, and naringenin all had good binding to the target protein AKT1 or TNF, which provided a strong basis for the results of network pharmacology. So, the feasibility of TCM such as EH can be affirmed in the treatment of NS.On the basis of network pharmacology and molecular docking, this study focused on the active ingredients of EH and its multitarget and multipathway characteristics in the process of treating NS. The results indicated that the potential mechanism of EH in treating NS may be related to PI3K-Akt signaling pathway, TNF signaling pathway, and AGE-RAGE signaling pathway. It may provide new ideas and better approaches for the treatment of NS. However, further in vivo and in vitro experimental verifications are expected to be conducted in the future.


To explore the mechanism of Dahuang Fuzi decoction in the treatment of incomplete intestinal obstruction (IIO) based on network pharmacology and molecular docking.  The chemical components of Rhubarb, Aconite, and Asarum were searched by the Traditional Chinese Medicine Systems Pharmacology database, where the possible active components were screened by oral bioavailability and drug likeness as filtering indicators. The relevant targets in the Swiss Target Prediction database were obtained according to the structure of the chemical components confirmed by the PubChem database. Disease targets of IIO were collected using GeneCards and OMIM databases. We obtained the cross-target using VENNY to capture the common targets. PPI analysis was performed on the intersection genes combined with Cytoscape 3.7.2. Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out by David database. The core targets and active ingredients were molecularly docked through AutoDock Vina software to predict the detailed molecular mechanism of Dahuang Fuzi decoction for treating IIO.  There are 45 active components in Dahuang Fuzi decoction, with 709 corresponding targets, 538 IIO targets, and 97 common targets, among which kaempferol, deltoin, and eupatin are the main active ingredients. 10 core targets were obtained by protein-protein interaction network analysis. Through GO enrichment analysis, it was found that Dahuang Fuzi decoction may be involved in biological processes such as signal transduction, anti-apoptosis, promotion of gene expression, regulation of cell proliferation, and differentiation. Besides, KEGG pathway analysis revealed that it mainly relates to PI3K-AKT signal pathway and HIF-1 signal pathway, etc. Molecular docking results showed that the active ingredients of Dahuang Fuzi decoction possess a good binding activity with the core targets. α, IL-6, iNOS, and COX-2 to play a role in the treatment of IIO. Dahuang Fuzi decoction may act on target genes such as TNF, IL6, AKT1, VEGFA, SRC, EGFR, and STAT3 through active ingredients such as kaempferol, deltoin, and eupatin to regulate signaling pathways such as PI3K-AKT and HIF-1 and reduce the expression of various inflammatory factors such as TNF- Intestinal obstruction (IO), one of the common surgical acute abdomen, can be divided into complete intestinal obstruction (CIO) and incomplete intestinal obstruction (IIO) according to the degree of obstruction. IIO is a condition in which the normal flow of intestinal contents is interrupted and the intestinal proximal to the obstruction becomes dilated while the distal intestinal is normal or collapsed , with thRadix Rhei Et Rhizome (Rhubarb), Aconiti Lateralis Radix Praeparata (Aconite), and Asari Radix Et Rhizoma (Asarum), which was originally described in Synopsis of Golden Chamber (Jin Kui Yao Lue), a treatise on febrile and miscellaneous diseases written by physician Zhang Zhongjing in Han Dynasty, and has been widely used in China for thousands of years [α and IL-6 levels. Experimental studies have also demonstrated that Dahuang Fuzi decoction is capable of promoting intestinal peristalsis, reducing the damage to the epithelial cells of small intestinal mucosa, and protecting the intestinal barrier. However, the underlying mechanism of action of Dahuang Fuzi decoction for the treatment of IIO is unveiled, whose action targets, molecular mechanisms, and related signaling pathways have not been fully elaborated. Chinese herbal formulation has the characteristics of multi-component, multi-target, and multi-pathway, but the current research on action mechanisms is still limited to a single target pathway, which is not in line with the features of Chinese medicine and led to an incomplete explanation of the mechanisms.Dahuang Fuzi decoction is composed of three herbs including of years . Nowadayof years , acute kof years , acute iof years , and IIOof years . At presNetwork pharmacology is a new discipline based on the theory of systems biology, the network analysis of biological systems, and the selection of specific signal nodes (nodes) for the design of multitarget drug molecules, which can be adopted to predict the molecular mechanism of drug action in disease and is very suitable for the multi-component, multi-target, and multi-pathway research. Molecular docking is mainly used to study intermolecular interactions and predict the binding mode and relationship. Besides, molecular docking can also be utilized for the prediction of drug and protein function. By integrating network pharmacology and molecular docking, we aimed to explore the underlying mechanism of Dahuang Fuzi decoction for the treatment of IIO in order to facilitate the condition of patients with IIO and reduce the mortality rates.We searched Rhubarb, Aconite, and Asarum in the herb name of TCMSP   to generate a “Herb-Disease-Ingredient-Target” network, where the nodes in the network diagram are the chemical components and targets and the correlation between components and targets is represented by edges.The intersections of active ingredients and IIO targets were uploaded to the online site of STRING. The protein type was set to “Homo sapiens,” and the other parameters were set to default values. The protein interaction relationships were retrieved. The combined score from the export file was imported into Cytoscape to conduct topology analysis, build a PPI network diagram, and select the core proteins with the top 10 degree values by the cytoHubba.P set less than 0.05, and the top 20 KEGG signal pathways were ranked according to the results in descending order of count value.Based on the David database, GO analysis of the relevant obtained intersection target proteins was performed, involving the biological process (BP), cellular component (CC), and molecular function (MF). KEGG was selected for target pathway annotation analysis with Molecular docking is a method for drug design by exploring the interaction and recognition between receptors and ligands, which is a theoretical simulation method that focuses on the study of intermolecular interactions and the prediction of their binding patterns and affinities. In recent years, molecular docking methods have become an important technique in the field of computer-aided drug research. AutoDock Vina is an open-source molecular docking program designed by the Scripps Research Institute for the computation of semiflexible molecular docking. AutoDock Vina utilizes a sophisticated gradient algorithm and multithreaded techniques for superior accuracy and faster prediction speed in comparison with AutoDock4. Semiflexible docking means that the conformation of the ligand molecule can be changed according to the receptor molecule and is flexible, while the receptor molecule does not change and is rigid . Then, tBased on the screening conditions of the compounds, 35 active ingredients were collected from the TCMSP database, among which 13 components of Rhubarb were obtained after excluding 3 components with no target, and 14 components of Aconite were acquired after excluding 7 components with no target, and 8 active components of Asarum all with corresponding targets were collected. The details are shown in The GeneCards and OMIM databases were searched with the keyword “Incomplete Intestinal Obstruction”, yielding 538 IIO targets based on the relevance score. The 97 intersecting genes of Dahuang Fuzi decoction and IIO were obtained, as shown in The “Herb-Disease-Ingredient-Target” network graph created by Cytoscape 3.7.2 reflects the correspondence of the compound targets, as shown in The 97 intersection targets of the predicted Dahuang Fuzi decoction and IIO were imported into the STRING database to obtain protein interaction relationships. The analysis results were topologically analyzed by Cytoscape 3.7.2, as shown in P < 0.05). The bubble chart  parameters. Consequently, 35 active components of Dahuang Fuzi decoction were obtained, and 709 candidate targets of 35 active components were retrieved through Swiss Target Prediction. Subsequently, we constructed a Herb-Disease-Ingredient-Target network, which can reflect the relationship between numerous components and targets of the drug. As a result, three main active components of Dahuang Fuzi decoction for the treatment of IIO were identified involving kaempferol, deltoin, and eupatin. Kaempferol, as a flavonoid, can scavenge free radicals, reduce the release of inflammatory factors, and improve intestinal homeostasis. The paper published in 2020 by Yao et al.  revealedM et al.  found thM et al. . DeltoinM et al.  proposedu et al. . COX-2 gu et al. . In concα and IL-6 are sensitive indicators of inflammatory response in patients with IIO, with TNF-α being the most critical initiator of inflammatory response in the body [α and IL-6 levels [α, and other inflammatory factors, while EGFR activation can promote the repair and regeneration of peripheral blood vessels, lymphatic vessels, and peripheral nerves and regulate various biological processes such as cell proliferation, differentiation, migration, and survival [κB signaling pathway, and NF-κB is involved in the transcriptional regulation of proinflammatory response factors [Third, we retrieved 538 candidate IIO targets from the GeneCards and OMIM databases, and 709 candidate targets of Dahuang Fuzi decoction were selected through Swiss Target Prediction. We found 97 targets in common between the disease and herbs, which were considered potential targets for treating IIO. The degree of association between these genes is shown in the body , causingthe body . Clinica6 levels . Intestisurvival  to inhibsurvival . Activat factors , where V factors . SRC-3 i factors  in IIO.  factors . JAK/STA factors . Effectiα and IL-6 induce activation of the PI3K/Akt pathway through multiple mechanisms [α is a subunit of HIF-1, which is upregulated in response to hypoxic stress and acts as a nuclear transcription factor, targeting and regulating the expression of downstream genes [α, which can promote the repair of damaged epithelial cells, cellular collagen synthesis, and extracellular matrix synthesis and strengthen the intestinal mucosal barrier [α. In human intestinal endothelial cells, direct interaction of the thyroid hormone nuclear receptor with the p85 subunit of PI3K leads to T3-dependent PI3K activation, resulting in phosphorylation and activation of AKT and iNOS [2+ does not reach the appropriate concentration, followed by the relaxation of the smooth muscle cell, thus reducing gastrointestinal motility. When the calcium signaling pathway is activated, Ca2+ flows into the cell through this channel in the cell membrane and promotes the contraction of smooth muscle [The target genes of Dahuang Fuzi decoction for the treatment of IIO were used to obtain an enrichment map of the GO and KEGG pathway analyses based on the David database. chanisms . HIF-1α am genes . IGF-1 i barrier . Therforand iNOS , which eh muscle . Dahuangα, IL-6, iNOS, and COX-2 to play a role in the treatment of IIO. In conclusion, Dahuang Fuzi decoction acts through multiple targets and pathways in the treatment of IIO, and the results of the present study provide a basis for further research on the clinical application of Dahuang Fuzi decoction in IIO and also offer guidance and reference for the next stage of study on the action mechanism of Dahuang Fuzi decoction.We hypothesize that Dahuang Fuzi decoction may act on target genes such as TNF, IL6, AKT1, VEGFA, SRC, EGFR, and STAT3 through active ingredients such as kaempferol, deltoin, and eupatin to regulate signaling pathways such as PI3K-AKT and HIF-1 and reduce the expression of various inflammatory factors such as TNF-


P < .05), 153 entries on biological process (BP), 12 entries on BP and 11entries on cell composition (CC) were included. A total of 100 signaling pathways were obtained by KEGG pathway enrichment screening (P < .05).The pathways that are closely related to the pathogenesis of ASD are estrogen signaling, thyroid hormone signaling pathway, prolactin signaling pathway, and endocrine resistance pathway. Molecular docking results showed that NR had the best docking activity with the core target CASP3, and had good binding ability with AKT1, ESR1, ACTB and MAPK3. Taken together, our findings support that NR exerts therapeutic effects on ASD with multi-target, and multi-pathway characteristics, which provides a preliminary theoretical basis for clinical trials. The mechanism of anti-oxidative stress response, anti-apoptosis, regulation of cell growth and metabolism, anti-inflammatory, balance hormone levels may be important for the therapeutic effect.Naringenin (NR) is a kind of flavonoid which plays a great role in the treatment of autism spectrum disorder (ASD). However, the underlying mechanism of NR in treating ASD still remains unclear. This study used network pharmacology and molecular docking to examine the potential targets and pharmacological mechanism of NR on ASD. Targets related to NR were screened from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), Encyclopedia of Traditional Chinese Medicine Database (ETCM), Traditional Chinese Medicine Integrated Database (TCMID), PharmaMapper database, and targets related to ASD were screened from Online Mendelian Inheritance In Man (OMIM), Disgenet, GeneCards, Therapeutic Target Database (TTD), Drugbank, and ETCM. Screened of the intersected gene targets. Then, we used the protein–protein interaction (PPI) networks to construct a PPI network and used Network Analyzer plug-in to perform topological analysis to screen out the core target. We used Metascape platform to perform gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and used Chem draw, Pymol, AutoDock 1.5.6 software for molecular docking verification with core targets. A total of 149 targets of NR and 1594 potential targets of ASD were screened, and 43 intersected targets and 8 key targets were obtained and screened. A total of 176 GO items were obtained by GO enrichment analysis ( In China, autistic children account for about 36.9% of mentally ill children, which brings a heavy economic burden to society and their families. Clinical treatment with western medicine consists primarily of drug combinations and behavior modification, educational intervention, speech training, and modern rehabilitation training. The target drugs for this disease are also in the exploratory stage, requiring a prolonged disease course to achieve curative effect while growing evidence suggests that traditional NR can be beneficial.Autism Spectrum Disorder (ASD) is a complex chronic neurodevelopmental disease that occurs in childhood and is accompanied by persistent deficits such as various social impairments, behavioral impairments, language communication impairments, and repetitive stereotyped behaviors. These include anxiety, intellectual disability, motor abnormalities, epilepsy, attention disturbance, hyperactivity, sleep disturbance, immune system deregulation, and gastrointestinal problems.,7 Children with ASD often show typical heart and spleen deficiency symptoms and gastrointestinal problems such as food intolerance, food allergy, constipation or diarrhea, partial eclipse, and picky eating. TCM often involves a combination of syndrome differentiation and syndrome differentiation, which can effectively improve the core symptoms of children. NR is the main natural dihydroflavonol in various TCMs, such as tangerine peel. It is easily absorbed by the intestinal tract and has anti-inflammatory, anti-oxidative, lipid-regulating, and anti-apoptotic activities, can improve immunity, repair DNA damage and eliminate free radicals in the body, which can protect nerve and gastrointestinal function. Researchers such as Ranjana Bhandari established the pharmacokinetic and pharmacodynamic model to found that NR as an adjunct neurotherapeutic moiety in attenuating neuropsychopathology associated with ASD, such as sensorimotor dysfunction, hyperlocomotion, anxiety-like behaviors, social interactions, and repetitive behaviors. Likewise, it indicates anti-hyperlipidemic, anti-depressant, anti-proliferative effects and its effect on metabolic disorders and cognitive dysfunction which provides a pharmacological and experimental foothold for the prevention and treatment of ASD.–14In ancient Chinese medicine books, “dementia,” “late speech,” “fetal weakness,” “coma” and “depression syndrome” were assigned to the autism category. The Traditional Chinese Medicine (TCM) pathogenesis is based on deficiency and excess and is closely related to the brain, heart, kidney, and liver, involving qi deficiency, essence deficiency, blood deficiency, fire (heat), and phlegm-dampness, which can easily lead to brain disorders and insufficiency. but the targets, pathway, and mechanism remain unclear. Hitherto, few specific studies have been conducted. As an emerging frontier science for TCM research, network pharmacology can integrate and systematize drugs, targets, and diseases and also embodies the overall concept and dialectical thinking of TCM concepts. Therefore, this study sought to explore the potential targets and molecular mechanisms of NR in the treatment of ASD from multiple perspectives, with network pharmacology and molecular docking technology, hoping to provide a scientific theoretical basis for the optimization of autism drug selection in the future and also lay a theoretical basis for subsequent experimental research  was used to obtain the structure and target information of NR, and the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain the Canonical simplified molecular-input line-entry system (SMILES) number of NR, which was imported into the Swiss Target Prediction database (http://www.swisstargetprediction.ch) for target prediction.The TCMSP (http://www.tcmip.cn/ETCM/), TCMID (http://www.megabionet.org/tcmid/), and PharmaMapper (http://lilab.ecust.edu.cn/pharmmapper/) platform using “naringin” as the keyword, which were merged to remove duplicates, calibrated and compared through the Uniprot database (http://www.uniprot.org/). After unifying the ID name, we finally obtained the corresponding compound target genetic information.The target genes of NR were screened from ETCMhttp://omim.org/), Disgenet (https://www.disgenet.org/), GeneCards (http://www.genecard.org/), TTD (http://bidd.nus.edu.sg/group/cjttd), Drugbank (http://www.drugbank.ca), and ETCM to obtain ASD-related genes,–27 and the target names were standardized through the Uniprot database. Then, the target names were combined and deduplicated to obtain ASD-related genes.By using “Autism Spectrum Disorder” as the keyword, we searched 6 disease databases of OMIM (https://bioinfogp.cnb.csic.es/tools/venny) to obtain the intersected genes, and a Venn plot was created to visualize the potential targets of NR in the treatment of ASD.The NR action targets and disease targets retrieved above were imported into Venny 2.1.0  platform. “Organism” was set to “Homo Sapiens,” free nodes were hidden, and PPI network was constructed to derive node information to prepare for the next step, which involved generating a network graph to explore the interaction between NR-related targets and ASD targets.The construction of PPI is crucial for understanding the indication to what extent 2 proteins interact with each other. to analyze the network topology parameters. The node size and color depth reflected the degree value. And the key targets were filtered through the plug-in centiscape2.2 by calculating the mean values of betweenness, closeness, and degree. The targets above the 3 thresholds of betweenness, closeness, and degree were selected as “key targets,” which could be considered as the core target genes of NR in the treatment of ASD.In order to obtain core target genes, we used the tool Network Analyzer in Cytoscape 3.9.0(https://metascape.org/). “Homo Sapiens” (human source) was selected for the species background of Metascape; the P-value was set to <0.01, the Min Overlap value was 3, and the Min Enrichment value was 1.5. GO annotation was conducted on 3 aspects: BP, CC, and molecular function (MF). The microscopic letters (http://www.bioinformatics.com.cn) generated the bubble plots and histogram, and the Cytoscape 3.9.0 platform was used to draw the “component-target-disease-pathway” network diagram.GO biological function enrichment analysis and KEGG metabolic pathway enrichment analysis were performed on the screened key targets through the Metascape platformWe conducted a molecular docking analysis of the key targets screened in 1.4 with the NR component. The structure of the NR compound was obtained from the PubChem database. The structure optimization was performed by Chem3D 17.0 software; the energy of the downloaded compound was minimized and converted into mol2 format. Small molecule compounds were imported into AutoDock Tools-1.5.6 software and saved as Protein Data Bank (PDB) files after adding atomic charges, assigning atomic types, and making all flexible bonds rotatable by default.The crystal structure of the target protein was obtained from the Uniprot protein database. After using Pymol 2.1 software to delete irrelevant small molecules in the protein molecule, the protein molecule was imported into AutoDock Tools-1.5.6 software to delete water molecules, add hydrogen atoms, set the atom type, and finally saved as PDB files.The treated compound was used as a small molecule ligand, and 8 protein targets were used as receptors. The center position and length, width, and height of the Grid Box were set to 50 × 50 × 50 according to interactions between the small molecule and the target. Finally, molecular docking was performed by AutoDock Tool software to verify their binding efficiency, and a Lamarckian genetic algorithm was used for molecular docking calculation.15H12O5, molecular weight 272.27, the ratio of lipid water partition coefficient (AlogP) 2.30, OB 59.29, blood-brain barrier (BBB) −0.37, and drug-likeness (DL) 0.21, drug half-life (HL) 16.98.Through the TCMSP and the PubChem database, we obtained the structure of NR. The 2D and 3D structures of NR are shown in Figure Thirty-six potential targets of NR were obtained from the TCMSP database, 29 potential targets were obtained from the Swiss Target Prediction database, and 44, 48, 65 potential targets of NR were obtained from ETCM, TCMID, and PharmaMapper, respectively. After the targets obtained in the above databases were merged and deduplicated, 149 NR-related targets were obtained.Ninety-one, 1113, 733, and 68 ASD-related targets were obtained from the OMIM, Disgenet, GeneCard, and Drugbank databases, respectively, and combined with the 2 databases of TTD and ETCM to supplement related targets. A total of 1594 ASD disease targets were obtained after merging and deleting duplicate targets.The screened NR and ASD-related targets were mapped, and the intersection of the 2 was obtained, yielding 43 targets, and a “drug-disease” Venn plot was generated in Figure The protein interaction PPI information obtained from the STRING database was imported into Cytoscape 3.9.0 software as a “TSV” file to generate a PPI network map of potential targets involving 40 nodes and 234 edges , the top 20 representative pathways were selected for bubble diagram according to the P-value, as shown in Figure The KEGG pathway enrichment analysis revealed 100 enriched metabolic pathways , playing an essential role in stabilizing small molecules in the protein cavity. This compound can also form hydrogen bonds with GLU-228 and ALA-230 amino acids, which contribute to the small molecules in the anchor protein cavity. Moreover, we found that NR and the ESR1 protein site can form hydrogen bonds, hydrophobic interactions, van der Waals forces, and other interactions; for example, NR can interact with LEU-387, PHE-404, LEU-391, ALA-350, VAL-533, MET -343, LEU-346, and other amino acids to form strong hydrophobic interactions, and can form strong hydrogen bond interactions with LEU-387, ARG-394, GLU-353.In addition, NR can interact with several other targets in various ways, effectively promoting the formation of stable complexes between proteins and small molecules and having strong associations with the targets. Other risk factors include increasing parental age, influenza vaccine, drug infection or exposure to valproic acid and folic acid during pregnancy, maternal obesity, maternal smoking, preterm birth, low birth weight, and altered gut microbiota. The current treatment approach for ASD involves modulating neurotrophic factor levels, regulating gene expression, restoring cell synapse levels, restoring protein homeostasis, and stimulating oxidative stress responses.In recent years, there has been a burgeoning interest in exploring the underlying molecular and cellular mechanisms of ASD. Ample evidence suggests that its pathogenesis includes genetic, environmental, and immune factors. High genetic variability involving disruption of messenger ribonucleic acid (mRNA) transcription or protein function, deletion of gene exons, abnormal gene methylation, single nucleotide polymorphism, and copy number variation represents a significant risk factor for disease. Flavonoid compounds show considerable potential and advantages for cancer prevention, cardiovascular diseases, and diabetes mellitus, mostly postulated to be due to their antioxidant effects. NR as a flavanone flavonoid that can have a protective effect on the gastrointestinal tract and effectively improve neurological diseases. Oral bioavailability (OB) is an important index to evaluate the effectiveness of drugs entering human circulation and the OB value of >30 is often used as a standard for compound screening. Our study found that the OB value of NR was 59.29, indicating that NR may be easily absorbed into the blood after oral administration, and it thus can conveniently exert biological activity. Some studies showed NR could improve spatial learning and memory in aging mice by inhibiting Aβ levels and neuroinflammation, phosphorylation, and oxidative stress. It can also downregulate anti-apoptotic genes to improve cognitive dysfunction, dyskinesia, depression, and anxiety-like behavior in hypoxic mice.NR, chemically known as 5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one, is a disaccharide derivative at position 7 via a glycosidic linkage. It is a natural flavonoid glycoside with various pharmacological activities including high antioxidant, anti-inflammatory, anti-apoptotic and neuroprotective properties that can be found in a diversity of citrus fruits, vegetables, nuts and Chinese medicine. Experiments have shown that Casp3-deficient mice exhibit abnormal motor behaviors such as impaired social interaction, limited interest, and repetitive stereotypes associated with endoplasmic reticulum stress. Ample evidence substantiates that children with ASD have significantly higher levels of CASP3, which can lead to apoptosis in autistic brain tissue. NR can inhibit CASP3 elevations and alleviate endoplasmic reticulum stress and oxidative stress-induced apoptosis in ASD.–46 Moreover, AKT1 (AKT serine/threonine kinase 1) reportedly regulates cell proliferation, metastasis, and apoptosis. Abnormalities in the PI3K/AKT/mTOR pathway have been associated with abnormal synaptic protein synthesis and the development of ASD. Current evidence suggests that NR can control cell apoptosis and maintain cell activity by regulating AKT1 target proteins. At the same time, NR can significantly inhibit AKT1 phosphorylation, promote gene transcription and protein synthesis, and activate Akt-mTOR signaling to inhibit apoptosis in autism and alleviate abnormal social behavior in mice.,49In our study, a total of 43 common targets of NR in the treatment of ASD were obtained through TCMSP, OMIM, and other related databases, and 8 key targets were obtained after screening. Topological analysis and molecular docking results showed that CASP3 had the closest binding ability to NR, and other targets such as AKT1, ESR1,ACTB, MAPK1, MAPK3, suggesting that their binding ability was also good. CASP3 (Caspase 3) is a group of proteases that play a key executive role during apoptosis, capable of eliminating redundant and nonfunctional synapses and removing extra cells during early childhood development, and play an important role in embryonic neuronal development. Interestingly, women with abnormal antenatal testosterone and sex steroid precursor exposure have higher rates of autistic traits. Abnormal chemical stimuli into cells send signals at the membrane and nuclear levels through the Estrogen receptor, stimulate kinases and phosphatases in the body, change the phosphorylation state of key protein kinases, and reduce estrogen biosynthesis.,52 Experiments have demonstrated that the ERɑ/β-mediated estrogen system of NR regulates oxytocin secretion in rodent hypothalamic neurons during rodent central nervous system development and can effectively activate ER-α inhibitory activity, reduce cellular stress through the inhibition of ESR1-mRNA expression or the downregulation of estrogen protein involving the corresponding signaling pathway. Moreover, estrogen-induced activation of the estrogen results in a reduction in the levels of nuclear DNA-binding activity of NF-kB, which in turn regulates the expression of inflammatory genes. Therefore, anti-inflammatory action as a potential mechanism in mediating the neuroprotective effects of estrogen.ESR1, a transcription factor of the nuclear receptor family, can mediate estrogen ligands, promote growth and cell survival, and is associated with social behavior and emotional regulation in children with ASD.GO and KEGG enrichment analysis enabled further analysis of their gene functions. The results showed that the therapeutic effect of NR in the treatment of ASD involves various pathways such as estrogen signaling, thyroid hormone signaling pathway, prolactin signaling pathway, endocrine resistance pathway, platelet activation pathway, and apoptosis pathway while involving different biological processes such as ROS metabolism, chemical stress, oxidative stress, cell growth, and apoptosis.,56 Environmental and genetic risk factors may exacerbate ASD patients’ vulnerability to oxidative stress, impair antioxidant defense mechanisms, and lead to cell membrane damage, altered membrane fluidity, and permeability. For example, when a child is exposed to toxic air or environmental pollution in the mother’s body, an excess of free radicals is produced in the child’s body, leading to increased inflammation, toxicity, and oxidative stress. On the other hand, patients with autism exhibit blood-brain barrier leakage and are relatively sensitive to oxidative damage, leading to the manifestations of neuropsychiatric disorders, such as depression, cognitive dysfunction, psychosis, anxiety, etc. Recent studies found that NR is a flavonoid that has an inhibitory effect on signal transduction enzymes and controls cell growth by regulating proteins such as protein tyrosine and protein kinases. Moreover, NR possesses antioxidant properties, which can regulate the levels of ROS, inhibit oxidative stress and neuroinflammation, thereby improving cognition levels.ROS are widely acknowledged as a major cause of cellular damage, premature cellular aging, and neurological diseases. ROS have been associated with increased activity of mitogen-activated protein kinases and play essential roles in cell growth, differentiation, development, cell cycle, survival, and cell death by activating the redox reaction of tyrosine kinases while affecting the activity of critical growth and metabolism-related transcription factors, and is sensitive to redox changes. NR can reduce hormone levels through the thyroid hormone signaling pathway to restore normal cell growth and metabolism in children with ASD. In this regard, mothers who drink heavily or take antipsychotics for a long time during pregnancy can cause hormone imbalance, reduce oxytocin secretion, and have a higher chance of developing ASD in the baby. Overwhelming evidence substantiates that plasma oxytocin levels in autistic patients are lower than usual, and low levels of oxytocin can affect children’s social recognition behavior.–63 It has also been reported that children with ASD have hypoplasia of the nervous system, accompanied by inflammation, oxidative stress and mitochondrial dysfunction, and defective platelet aggregation in endothelial cells. Current evidence suggests that platelets release brain-derived neurotrophic factor (BDNF) upon activation. BDNF is a growth factor and a member of the neurotrophic factor family. After platelets activate BDNF, angiogenesis-related and inflammatory cytokines are released, while NR can regulate platelet activation, aggregation, secretion, and promote the expression of BDNF factor, exhibiting good antiplatelet activity and antidepressant effect.[67 ASD patients are associated with a higher prevalence of obesity, overweight, and cancer due to their endocrine disorders. Importantly, NR can regulate hormone levels through multiple pathways such as endocrine resistance and proteoglycans in cancer to increase gut microbiota diversity and regulate hormone imbalances and maternal metabolic disorders.Interestingly, thyroid hormone signal transduction pathways have been shown to play important roles in metabolic processes such as central nervous system development and anti-apoptosis during early development.There is growing evidence that NR has potential effects in the protection against ASD. Importantly, our study provides a preliminary theoretical basis and basis for new drug development and clinical trials for ASD and provides the foothold for future studies to explore the mechanism in treating underlying therapeutic effect of other Chinese herbal medicines and NR-rich vegetables and fruits. The diversity of ASD phenotypes and the complexity of molecular mechanisms emphasize the need for future studies to find reliable drugs and explore their mechanisms of action to improve the quality of life of families affected by ASD.In summary, the present study found a series of novel targets and pathways for NR treating ASD and the mechanism of action on the human body suggests the involvement of multiple pathways and multiple targets. It was preliminarily predicted that NR could regulate the targets of CASP3, AKT1,ESR1,ACTB,MAPK3 and may modulate various pathways like estrogen signaling, thyroid hormone signaling pathway, prolactin signaling pathway, proteoglycan in cancer pathway, endocrine resistance pathway. Its mechanism of action are involving anti-oxidative stress response, anti-apoptosis, regulation of cell growth and metabolism, anti-inflammatory, balance hormone levels. At the same time, the molecular docking also corresponds to the predicted results, showing good target binding ability, which verifies the feasibility of the predicted components, core targets and their pathways in this study.This study provides a potential biological basis for the further study of NR in the treatment of ASD, but still has some limitations. We used only modern bioinformatics methods to explore the effects of NR in the treatment of ASD by using network pharmacology and molecular docking. Firstly, data from online databases are based on reviewed and predicted data, and unconfirmed and unrecorded data were not included in our study. Secondly, although CASP3, AKT1, ESR1, ACTB, MAPK3, were identified as core targets, pharmacodynamic and molecular biology experiments need to be considered to further investigate our results. We believe that this topic has great research potential and application value.Conceptualization: Jialin Gai.Data curation: Jinxiao Xing.Formal analysis: Jialin Gai, Yangyang Wang.Funding acquisition: Jiqin Tang.Methodology: Jialin Gai, Junfang Lei, Chengdong Zhang.Software: Jialin Gai, Junfang Lei.Supervision: Jiqin Tang, Jinfei Zhang.Writing—original draft: Jialin Gai.Writing—review & editing: Jialin Gai, Jinxiao Xing, Yangyang Wang, Junfang Lei, Chengdong Zhang, Jinfei Zhang, Jiqin Tang.