Panax notoginseng saponins (PNS) have been deemed effective herb compounds for treating ischaemic stroke (IS) and improving the quality of life of IS patients. This study aimed to investigate the underlying mechanisms of PNS in the treatment of IS based on network pharmacology.  PNS were identified from the Traditional Chinese Medicine System Pharmacology (TCMSP) database, and their possible targets were predicted using the PharmMapper database. IS-related targets were identified from the GeneCards database, OMIM database, and DisGeNET database. A herb-compound-target-disease network was constructed using Cytoscape, and protein-protein interaction (PPI) networks were established with STRING. GO enrichment and KEGG pathway analysis were performed using DAVID. The binding of the compounds and key targets was validated by molecular docking studies using AutoDock Vina. The neuroprotective effect of TFCJ was substantiated in terms of oxidative stress  and the levels of IGF1/PI3K/Akt pathway proteins.  A total of 375 PNS targets and 5111 IS-related targets were identified. Among these targets, 241 were common to PNS, and IS network analysis showed that MAPK1, AKT1, PIK3R1, SRC, MAPK8, EGFR, IGF1, HRAS, RHOA, and HSP90AA1 are key targets of PNS against IS. Furthermore, GO and KEGG enrichment analysis indicated that PNS probably exert therapeutic effects against IS by regulating many pathways, such as the Ras, oestrogen, FoxO, prolactin, Rap1, PI3K-Akt, insulin, PPAR, and thyroid hormone signalling pathways. Molecular docking studies further corroborated the experimental results.The network pharmacology results were further verified by molecular docking and in vivo experiments.  The ameliorative effects of PNS against IS were predicted to be associated with the regulation of the IGF1-PI3K-Akt signalling pathway. Ginsenoside Re and ginsenoside Rb1 may play an important role in the treatment of IS. Stroke is a common disease worldwide, accounting for 11.59% of all deaths in 2019. Stroke, which accounts for more than 20% of China's total deaths, is the most common cause of death in China . At presPanax notoginseng, as a traditional Chinese medicine, removes blood stasis, stops bleeding, promotes blood circulation, and relieves pain. The main active ingredients of Panax notoginseng are Panax notoginseng saponins (PNS), including notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, and ginsenoside Rd-qt [Traditional Chinese medicine compounds, which act through multiple components, pathways, and targets, have shown good clinical effects and thus are increasingly used to treat IS. This study provides new strategies for the treatment of IS in China. de Rd-qt , 12. Stude Rd-qt . HoweverAngelica to treat acute myocardial infarction (AMI) might be related to the AMPK signalling pathway. This prediction was verified by in vitro and in vivo experiments. In vivo and in vitro verification experiments have indicated that Taohe Chengqi decoction may regulate the PI3K/AKT/mTOR and HIF-1α/VEGF signalling pathways, consistent with the predicted results of network pharmacology [Panax notoginseng against cardiovascular diseases and verified this prediction by in vitro experiments. Zhang et al. [Network pharmacology is a method of predicting drug targets and pharmacological mechanisms . By predmacology . Fu et amacology  predictemacology  predicteg et al.  predicteg et al.  predicteWe explored the mechanism of PNS against IS through network pharmacology and provided a theoretical basis for the experimental research on the effect of PNS against IS and the efficacy of PNS in the clinical treatment of IS. The research process is shown in http://tcmspw.com/tcmsp.php), Integrative Pharmacology-Based Research Platform of Traditional Chinese Medicine [http://www.tcmip.cn/), and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine [http://bionet.ncpsb.org/batman-tcm/). The gene targets of the active compounds were downloaded from the PharmMapper database [http://www.lilab-ecust.cn/pharmmapper/).The main PNS were identified from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform  , OMIM database (https://www.omim.org/), and DisGeNET database [https://www.disgenet.org/). We used the keywords “brain infarction,” “cerebral ischaemia,” “cerebral infarction,” and “stroke” to search these databases for IS-related genes, merged the data from the databases, and deleted the duplicate data.IS-related targets were identified from the following three disease-related databases: the GeneCards database (database  (https:/https://string-db.org/) [Homo sapiens,” the confidence level was set to > 0.700, the “hide disconnected nodes in the network” setting was simplified, the protein interaction information was obtained, the file was saved in the TSV format, and the data for node1, node2, and the combined score were imported into Cytoscape 3.7.2 [The PNS targets and IS-related targets that intersected were identified as common targets. The common PNS and IS-related targets were uploaded to the STRING 11.0 database (db.org/) , the typpe 3.7.2  to estabThe active compounds, common targets, PNS, and IS were arranged in a relationship pair file and then imported into Cytoscape 3.7.2 to construct a visual “herb-compound-target-disease” network.https://david.ncifcrf.gov/), the select identifier was limited to “OFFICIAL GENE SYMBOL,” the list type was limited to “Gene List,” and the species was limited to “Homo sapiens.” Gene Ontology (GO) and KEGG pathway enrichment analysis of the common targets were performed with this database.To further determine the mechanism of PNS against IS, the common PNS and IS targets were uploaded to the DAVID 6.8 database  (https:/http://autodock.scripps.edu/). Docking simulations were performed using AutoDock Vina [http://www.rcsb.org) [https://pubchem.ncbi.nlm.nih.gov/).Preprocessing structures were generated using AutoDock 4.2 (ock Vina . The crycsb.org) . The 2D Adult male Sprague-Dawley rats (260 ± 20 g bodyweight) were purchased from Huaxing Experimental Animal Farm in Huiji District, Zhengzhou City . All the animals were housed in an environment with a temperature of 23 ± 2°C, relative humidity of 55 ± 5%, and a light/dark cycle of 12/12 hr with free access to food and water. In addition, all animal studies (including the mice euthanasia procedure) were performed in compliance with the regulations and guidelines of Anhui Science and Technology University Institutional Animal Care and conducted according to the AAALAC and the IACUC guidelines. Rats were randomly divided into six groups as follows: (1) Sham group received 10 mL/kg 0.5% CMC daily, and the procedure was the same as that of MCAO except for the insertion of a filament; (2) MCAO group received 10 mL/kg 0.5% CMC solution daily before MCAO surgery. (3) PNS-L group received 40 mg/kg PNS  daily before MCAO surgery. (4) PNS-H group received 80 mg/kg PNS daily before MCAO surgery; and (5) nimodipine group received 14.4 mg/kg nimodipine  daily before MCAO surgery. All the above rates were given intragastric administration once a day for 7 days.Before the operation, the rats were fasted for 12 h to reduce the mortality during and after the operation and anesthetized by intraperitoneal injection of 2% pentobarbital sodium (4 mL/kg). According to the previous literature, we slowly inserted the threaded plug from the left external carotid artery (ECA) through the common carotid artery (CCA) into the internal carotid artery (ICA) until the middle cerebral artery (MCA) was blocked. After ischaemia 2 h, the blood supply was restored, and reperfusion was achieved. The reperfusion process continued for 24 h.SOD, GSH-Px, CAT activities, and the content of MDA in the brain homogenate were determined according to manufacturer's instructions. All kits were purchased from Nanjing Jiancheng Bioengineering Institute.β-actin served as a loading control, and densitometric analysis was determined by ImageJ software.The total proteins in the cerebral ischemia-reperfusion brain were were quantified with a bicinchoninic acid protein assay kit , loaded into 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and transferred to polyvinylidene fluoride (PVDF) membranes. After blocking with 5% fat-free milk or BSA for 1 h, the PVDF membranes were incubated with primary antibodies at 4°C overnight , followit-tests were used for comparison of variable pairs. P < 0.05 was considered as statistically significant.All data were expressed as mean ± SD; SPSS 25.0 statistical software  was adopted for all statistical analyses. The multiple variables were performed by one-way analysis of variance (ANOVA), and Student”s The details of the 5 compounds in PNS are described in Supplementary Targets related to IS were identified from the GeneCards, OMIM, and DisGeNET databases. The targets were renamed using the UniProt KB database. After deduplication, all information was merged, resulting in a database of 5111 IS-related targets, including 934 common targets .A total of 241 common targets of IS and PNS were identified with a Venn tool. The network constructed with the STRING 11.0 database ignored disconnected targets, resulting in a PPI network of only 224 targets .In the PPI network, there were a total of 224 nodes and 1119 edges. The three main parameters “degree (DC),” “closeness,” and “betweenness (BC)” were set as filters to select core targets and construct the main hub of the node representing the effect of PNS against IS. The first screening threshold DC ≥ 6, BC ≥ 132.806, closeness≥0.199, and 73 nodes and 488 edges were identified. The second threshold DC ≥ 17, BC ≥ 634.078, closeness≥0.220, and 23 nodes and 124 edges were identified .Based on these 23 core targets, we further constructed a “compound-core target” network . GinsenoThe triangular nodes represent compounds, and the circular node represents core targets.SRC, MAPK1, NOS2, NR3C1, ALB, HRAS, NOS3, CASP3, MAPK8, RHOA, FABP5, HSP90AA1, BCL2L1, PRKACA, PIK3R1, F2, AKT1, IGF1, EGFR, MMP9, AR, RXRA, and ESR1 were arranged according to the DC . The topThe herb-compound-target-disease network was composed of one herb, five compounds, 241 targets, and one disease. A diagram of this network is shown in The circles represent PNS, the triangles represent PNS compounds, the squares represent the 241 targets, and the V represents IS.p value < 0.01 and FDR < 0.05. The top 5 entries in the BP category were the steroid hormone-mediated signalling pathway, transcription initiation from RNA polymerase II promoter, peptidyl-tyrosine autophosphorylation, protein autophosphorylation, and phosphatidylinositol-mediated signalling. The top 5 entries in the MF category were steroid hormone receptor activity, RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding, protein tyrosine kinase activity, and serine-type endopeptidase activity. The top 5 entries in the CC category were cytosol, extracellular exosome, extracellular space, extracellular region, and membrane raft. The results are shown in We performed GO enrichment and KEGG pathway analysis of the common targets. For GO enrichment analysis , and the concentration of MDA was lower in the MCAO group than in the sham group (##p < 0.01).The activity of SOD, GSH-Px, and CAT in the brain tissue was higher in the MCAO group than in the sham group . The concentration of MDA in the brain tissue of the PNS group was significantly lower compared to that in the MCAO group (∗∗p < 0.01).The activity of SOD, GSH-Px, and CAT in the brain tissue of the PNS group was much greater than in the MCAO group . The IGF1/β-actin, p-Akt/Akt, and p-mTOR/mTOR expression in the PNS treatment rats were significantly higher than in the MCAO control rats (∗∗p < 0.01). There was no significant change in total PI3K and Akt protein expression in each group (p > 0.05).As the top 20 of the KEGG pathway, the PI3K-Akt signalling pathway played an important role, and IGF was its upstream protein. Therefore, we focused on the regulation of PNS on the protein of IGF and its downstream PI3K-Akt signal pathway. The IGF1/IS is a clinically acute disease. Within the first few minutes to a few hours after intracerebral artery occlusion, the infarcted area containing ischaemic tissue gradually spreads outward. As time passes after reperfusion, the ability of brain tissue to recovery decreases gradually . ArteriaMost Chinese medicines regulate the expression of pathways by acting on multiple genes or proteins and usually do not have serious side effects. For this reason, it is impossible to confirm how traditional Chinese medicines affect protein expression in organisms. However, network pharmacology can determine the potential molecular mechanism of drugs with multiple targets. Network pharmacology mainly involves prediction of compound and disease targets, construction of networks, PPI analysis, GO and KEGG enrichment analysis, and molecular docking studies and was used here to evaluate the mechanism underlying the therapeutic effect of PNS in IS.In this study, it was confirmed that PNS and IS have 224 common gene targets; a PPI network was constructed; DC, BC, and closeness were determined for two screenings; and 23 targets were identified. The targets with the top ten DCs were MAPK1, AKT1, PIK3R1, SRC, MAPK8, EGFR, IGF1, HRAS, RHOA, and HSP90AA1, which are the key targets.According to GO analysis, the targets of PNS against IS regulate BP terms, affecting CC and MF terms. The results showed that PNS mainly regulate the steroid hormone-mediated signalling pathway, RNA polymerase II promoter transcription, autophosphorylation of peptidyl-tyrosine, autophosphorylation of proteins, and phosphatidylinositol-mediated signalling. It was also shown that PNS affects specific CC and MF terms, including cytosol, extracellular region, steroid hormone receptor activity, RNA polymerase II transcription factor activity, protein tyrosine kinase activity, and serine endopeptidase activity.γ-Nrf2-NF-κB signalling pathway can reduce neuroinflammation after stroke [KEGG enrichment analysis indicated that that the targets were mainly enriched in signalling pathways, cell connection types, cancer-related diseases, biochemical substances, and pathways. According to reports in the literature, inhibition of the expression of related proteins in the Ras-MEK-ERK pathway can inhibit vascular intimal hyperplasia after stroke and reduce neuronal damage –42. Actir stroke . Activatr stroke . The pror stroke –47. Therr stroke , 49. By r stroke . Electror stroke . Ligustir stroke . SOX5 prr stroke . PNS actr stroke . Maintair stroke .Notoginsenoside Rb1 regulates the Akt-mTOR-PTEN signalling pathway and can alleviate motor and cognitive impairment induced by MCAO in rats . PNS mayThe results of the network pharmacology study were also confirmed by molecular docking studies. Generally, a binding free energy lower than −5.0 kcal/mol indicates that the docking molecule has excellent binding activity with the target; a binding energy lower than −7.0 kcal/mol indicates that the binding force is strong and that there is a significant interaction. The binding energies of the compounds and core targets were less than −6.0 kcal/mol, indicating good docking and high binding activity; in particular, the binding energy of SRC and ginsenoside Rb1 was −10.0 kcal/mol. Ginsenoside Rb1 protects the endothelial barrier function through the suppression of Src activation . From thIGF1, acts as a ligand for IGF1R, activated the PI3K-AKT  and Ras-β-actin, p-Akt/Akt, and p-mTOR/mTOR after IS 24 h. These results showed PNS anti-IS through the PI3K-AKT pathway by MCAO rats.In our study, we found that PNS upregulated the activities of SOD, GSH-Px, and CAT after IS 24 h, whereas PNS downregulated the MDA level, and PNS increased the ratio of IGF1/In this study, the targets, mechanism of action, and associated signalling pathways of PNS in the treatment of IS were predicted through network pharmacology; the binding abilities of the active ingredients of PNS and their targets were verified; and a fundamental basis for elucidating the mechanism of action of PNS was provided. Ginsenoside Rb1 and ginsenoside Rd-qt were related to most core targets. The key targets in the PPI network of the effect of PNS against IS were MAPK1, AKT1, PIK3R1, SRC, MAPK8, EGFR, IGF1, HRAS, RHOA, and HSP90AA1. According to molecular docking analysis, ginsenoside Re and ginsenoside Rb1, the active components of PNS, bind well with the key targets SRC, MAPK1, AKT1, and PIK3R1. Ginsenoside Re and ginsenoside Rb1 may play an essential role in IS treatment. The IGF1/PI3K-Akt signalling pathway, as a strongly associated pathway verified by in vivo experiments, may be used as a critical pathway underlying the effect of PNS against IS. Network pharmacology helps to reveal the mechanisms through which Chinese herbs can treat diseases.


Tinglizi has been extensively used to treat chronic heart failure (CHF) in modern times, but the material basis and pharmacological mechanisms are still unclear. To explore the material basis and corresponding potential targets and to elucidate the mechanism of Tinglizi, network pharmacology and molecular docking methods were utilized.  The main chemical compounds and potential targets of Tinglizi were collected from the pharmacological database analysis platform (TCMSP). The corresponding genes of related action targets were queried through gene cards and UniProt database. The corresponding genes of CHF-related targets were searched through Disgenet database, and the intersection targets were obtained by drawing Venn map with the target genes related to pharmacodynamic components. Then, drug targets and disease targets were intersected and put into STRING database to establish a protein interaction network. The “active ingredient-CHF target” network was constructed with Cytoscape 3.8.2. Finally, Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of intersection targets were analyzed using metascape. With the aid of SYBYL software, the key active ingredients and core targets were docked at molecular level, and the results were visualized by PyMOL software. Molecular docking was carried out to investigate interactions between active compounds and potential targets. P < 0.05), and 10 signal pathways were screened by KEGG pathway enrichment analysis (P < 0.05), which is closely related to the treatment of CHF by Tinglizi. The constructed drug compound composition action target disease network shows that quercetin, kaempferol, and other active compounds play a key role in the whole network. The results of molecular docking showed that all the key active ingredients, such as quercetin and isorhamnetin, were able to successfully dock with ADRB2 and HMOX1 with a total score above 5.0, suggesting that these key components have a strong binding force with the targets.  A total of 12 active components in Tinglizi were chosen from the TCMSP database, and 193 corresponding targets were predicted. Twenty-nine potential targets of Tinglizi on CHF were obtained, of which nine were the core targets of this study. Twenty GO items were obtained by GO function enrichment analysis , heme oxygenase 1 (HMOX1), and so on. The main pathways are advanced glycation end products/receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications, hypoxia-inducible factor (HIF-1) signaling pathway, estrogen signaling pathway, and so on. They play an integrated role in the treatment of CHF. Through network pharmacology and molecular docking technology, we found that the main components of Tinglizi in the treatment of CHF are quercetin, kaempferol,  Heart failure (HF) is a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood. According to the time and progression of heart failure, it can be divided into CHF and acute heart failure (AHF). The cardinal manifestations of HF are dyspnoea and fatigue, which may limit exercise tolerance, and fluid retention, which may lead to pulmonary and/or splanchnic congestion and/or peripheral oedema. Some patients have exercise intolerance but little evidence of fluid retention, whereas others complain primarily of oedema, dyspnoea, or fatigue . Most paSemen Descrurainiae seu Lepidii) has been quite frequently used, ranking the sixth [The latest epidemiological survey of HF in China shows that the prevalence rate of residents over 35 is 1.3%, and it is estimated that currently there are about 8.9 million patients with HF in China . The trehe sixth .Descurainia Sophia (L.) Webb. Ex Prantl. and Lepidium apetalum Willd [Tinglizi is sourced from the ripe seeds of um Willd . Tinglizum Willd . Pharmacum Willd . HoweverBased on systemic biology and pharmacology, network pharmacology provides a new “multitarget, multichannel, and multilink” network model. It offers a new research paradigm for the transformation of TCM from empirical medicine to evidence-based medicine. Network pharmacology has become an indispensable method to explore the potential mechanism of TCM , 10.Molecular docking is one of the most commonly used methods in drug design. It can predict the binding conformation between small molecule ligands and target binding sites. In this study, by searching the effective components and target proteins and constructing the target network, the core targets in the network were determined, and the biological function and pathway enrichment analysis were carried out. The molecular docking between the effective components and core targets of Tinglizi was verified by molecular docking technology, in order to explore the possible pharmacological mechanism of Tinglizi in the treatment of CHF at the molecular level.https://lsp.nwu.edu.cn/tcmsp.php) [The TCMSP (msp.php)  is genermsp.php) .https://www.disgenet.org/) [https://www.uniprot.org/) [We searched the target genes related to CHF with the keyword “chronic heart failure” in Disgenet database (et.org/) . The humot.org/) . We intehttps://string-db.org/) [STRING is a known database for predicting protein–protein interactions. Here, the intersection target of effective components of Tinglizi and CHF was entered into the STRING database  , and theThe visual “active ingredient-CHF target” network was constructed with Cytoscape 3.8.2 software. The nodes in the network represent active components or CHF targets, and the edges between nodes represent the interaction between active components and targets.P < 0.05 were chosen.GO enrichment and KEGG pathway enrichment of target of Tinglizi in the treatment of CHF were analyzed using an R package, clusterProfiler. After screening, GO enrichment and KEGG pathway with http://www.rcsb.org/). Hydrogen atoms, charges were added, and missing amino acid residues were repaired to the target protein by SYBYL X-1.2 software. The protein model was first optimized in AMBER FF99 force field for 1000 iterations by steepest descent (SD) and then optimized to a convergence gradient as 0.05 kcal/(Å mol) by conjugated gradient (CG). The planar structures of active compounds were converting into 3D structure. Polar hydrogens and charges were added by Gasteiger–Hückel method, and energy optimization was implemented using the Tripos force-field for 1000 iterations. After completing each run of the docking, the ten best conformers achieved through docking were then sorted in a molecular spreadsheet. Their binding affinities were represented as -lgKd based on the surflex-dock scoring function .Molecular docking studies were performed by Sulflex-Dock module in the SYBYL X-1.2 software . The crystal structure of protein was obtained from RCSB Protein Date Bank  is 23, that of mol000422 (kaempferol) 10, that of mol000358 (beta-sitosterol) 7, and that of mol000354 (isorhamnetin) 5.As is shown in The PPI network was built in the STRING database, as shown in P < 0.05 were considered as significant and top 10 terms were visualized. A total of 1323 items were obtained in the biological process analysis, mainly including cellular response to oxidative stress, cellular response to chemical stress, reactive oxygen species metabolic process, response to oxidative stress, response to reactive oxygen species, positive regulation of epithelial cell migration, tissue remodeling, regulation of transporter activity, regulation of reactive oxygen species metabolic process, and response to nutrient levels, as shown in We used the clusterProfiler package to perform GO enrichment analysis on 29 gene targets. Only GO terms with In the part of cellular component analysis, 12 items were obtained, mainly including membrane raft, membrane microdomain, pore complex, apical plasma membrane, myelin sheath, mitochondrial outer membrane, apical part of cell, focal adhesion, cell-substrate junction, and organelle outer membrane, as shown in A total of 41 items were obtained in the molecular function analysis part, mainly including DNA-binding transcription factor binding, cytokine activity, cytokine receptor binding, receptor ligand activity, signaling receptor activator activity, BH domain binding, transcription coregulator binding, death domain binding, repressing transcription factor binding, and phosphatase binding, as shown in P < 0.05 were considered as significant, and 104 relevant pathways were obtained. In P values. As shown in Figures Similarly, we used the R package to perform KEGG enrichment analysis on 29 gene targets. Only KEGG terms with To further explore the interactions between the active ingredients of Tinglizi and key targets, molecular docking studies were performed between 12 active compounds and the selected potential targets. These targets were chosen for docking because of their higher degree and betweenness centrality value in PPI analysis and also superior performance in clustering analysis. The compound–target pairs with the score ≥4 are considered to have particular binding activity, and when the score ≥6, it indicates strong binding efficiency between the compound and the target. As shown in Supplementary Shennong's Book of Materia Medica. Literature mining suggested that Tinglizi was one of the most commonly used Chinese herbal medicines for the treatment of CHF [CHF is a major public health challenge in China. With the worsening population aging in China, the incidence of chronic diseases, such as coronary heart disease, hypertension, diabetes, and obesity, is showing an upward trend. The survival time of patients with heart disease has prolonged for the improvement of medical conditions. The prevalence rate of CHF in China has been increasing. TCM has a long history of clinical practice, and many Chinese herbal medicines have played good clinical effects in the treatment of CHF. With the increase of clinical evidence, TCM is more and more recognized. Tinglizi was first recorded in t of CHF . In ordeβ-sitosterol, and isorhamnetin. They have effect on 29 targets. Quercetin, kaempferol, and isorhamnetin play a great role in the treatment of CHF. This study suggests that quercetin, kaempferol, and isorhamnetin play a significant role in the treatment of CHF.The TCMSP database and Cytoscape 3.8.2 were used to construct the pharmacodynamic target component interaction network. Nine pharmacodynamic components were selected for the treatment of CHF, including quercetin, kaempferol, κB protein to reduce ventricular hypertrophy in rats [κB and Nrf-2 activation [κB/mitogen-activated protein kinase and AMPK/Nrf2 pathways [β-Sitosterol has anti-inflammatory effects. Sitosterol may inhibit renal and cardiac necrosis and apoptosis by limiting inflammatory response and oxidative stress [β and collagen type I alpha 1 mRNA expression induced by AgII exposure, which causes cardiovascular remodeling in ventricular tissue [κB signaling pathway [β-sitosterol, and isorhamnetin, collectively resist inflammation and oxidative stress, protect cardiomyocytes, and inhibit ventricular remodeling to treat CHF.The mechanism of quercetin in the treatment of CHF may be through the expression of various regulatory proteins, inhibition of MAPK pathway, inhibition of inflammatory response, inhibition of oxidative stress, and so on. Studies have shown that quercetin can downregulate the expression of NF- in rats –19. Quer in rats . Quercet in rats . Kaempfe in rats . Kaempfetivation . Kaempfepathways . β-Sitose stress . Isorhame stress . ISO wasr tissue . Isorham pathway . ComponePPI protein interaction diagram shows that the targets of Tinglizi in the treatment of CHF include IL-6, MMP9, VEGFA, MAPK8, HMOX1, and so on. There is a significant correlation between serum IL-6 level and cardiac function in patients with CHF. It may be used as an evaluation index for the severity and prognosis of elderly patients with CHF. Inhibiting the increase of IL-6 level plays an important role in the phased treatment of CHF , 30. IL-GO biological process analysis showed that the targets of Tinglizi are mainly related to the regulation of cell proliferation, anti-inflammatory response, inhibition of cell oxidative stress, and regulation of cell apoptosis. It is suggested that the effective components of Tinglizi may play a role in CHF treatment through the above biological processes.KEGG pathway enrichment analysis showed that pathway ko05418 (fluid shear stress and atherosclerosis) is an important way to treat CHF. Coronary artery disease is a serious cardiovascular disease, because atherosclerosis can hinder the flow of blood, so that the heart fails to obtain sufficient supply of nutrition, which eventually leads to HF .ko04933  enriched 10 targets, suggesting that the treatment of diseases may be related to this pathway. Diabetic patients have a higher risk of cardiovascular diseases (such as HF) than healthy adults . The cauhsa04066  is a signaling pathway that is only activated under hypoxic conditions in the human body. It is an important regulatory pathway of various biological processes in the human body, and the pathway has been proved to have potential value for the treatment of HF . Studies2+ heart signal and heart disease. The correlation between TPRC pathway and Ca2+ signaling pathway in cardiomyocytes has been confirmed to affect the development of cardiac diseases [Both hsa04915  and hsa04020  enriched five targets. Estrogen signal is very important in the normal function of the heart. Estrogen signal mainly induces the transcription function of myocardial nucleus in the heart, and estrogen receptor plays an important role in fibroblasts, pulmonary septum and platelets . Obstrucdiseases . Studiesdiseases .In this study, the key active ingredients and core targets were docked at molecular level, and the results showed that the key active ingredients, such as quercetin and isorhamnetin, were able to successfully dock with ADRB2 and HMOX1 with a total score above 5.0, suggesting that these key components have strong binding force with the targets. This work provides important basis for exploring the integrative mechanism of Tinglizi against chronic heart failure from multiple components and multiple targets.With the help of network pharmacology and molecular docking technology, this study speculates the potential role of Tinglizi in the synergistic treatment of CHF through multicomponent, multitarget, and multichannel, which reflects the integrity of TCM. However, there are some limitations in this study. For example, this study relies only on data analysis. Relevant experiments can be carried out in the future to further verify this study.β-Sitosterol, isorhamnetin, and so on. The action targets are ADRB2, HMOX1, SLC6A2, NOS2, DPP4, NOS3, RELA, XDH, OPRM1, BCL2, BAX, TGFB1, PON1, NR3C2, MAPK8, and so on. The main pathways are AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, estrogen signaling pathway, and so on. They play an integrated role in the treatment of CHF.Through network pharmacology and molecular docking technology, we found that the main components of Tinglizi in the treatment of CHF are quercetin, kaempferol,


Asthma is a chronic inflammatory disease characterized by Th2-predominant inflammation and airway remodeling. Modified Guo Min decoction (MGMD) has been an extensive practical strategy for allergic disorders in China. Although its potential anti-asthmatic activity has been reported, the exact mechanism of action of MGMD in asthma remains unexplored.Network pharmacology approach was employed to predict the active components, potential targets, and molecular mechanism of MGMD for asthma treatment, including drug-likeness evaluation, oral bioavailability prediction, protein–protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Reactome pathway annotation. Molecular docking was carried out to investigate interactions between active compounds and potential targets.A total of 92 active compounds and 72 anti-asthma targets of MGMD were selected for analysis. The GO enrichment analysis results indicated that the anti-asthmatic targets of MGMD mainly participate in inflammatory and in airway remolding processes. The Reactome pathway analysis showed that MGMD prevents asthma mainly through regulation of the IL-4 and IL-13 signaling and the specialized pro-resolving mediators (SPMs) biosynthesis. Molecular docking results suggest that each bioactive compounds  is capable to bind with STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.This study revealed the active ingredients and potential molecular mechanism by which MGMD treatment is effective against airway inflammation and remodeling in asthma through regulating IL-4 and IL-13 signaling and SPMs biosynthesis. Asthma is a globally prevalent chronic inflammatory pulmonary disease, with an estimated 358 million affected individuals . PersistAirway remodeling is another key pathological features of asthma. It is characterized by thickening of basement membrane, goblet cell metaplasia, smooth muscle cell hyperplasia and hypertrophy, deposition of extracellular matrix proteins, vascular activation and angiogenesis . SynthesRadix Saposhnikoviae divaricate, FF), Wu Mei , Wu Wei Zi , Yin Chai Hu , Qian Hu , and Jie Geng . Previous studies showed that prescriptions based on GMD increased the proportion of Th1 cells in the spleen tissue of asthmatic mice, reduced the Th2 subset of T cells, and balanced the number of Treg and Th17 cells  has been used to treat allergic diseases in Asia for hundreds of years . “Guo Mi17 cells . However1  database4. The SMILES format files of the compounds were obtained from the PubChem5 database 7 , Online 7 , the candidate targets were inputted to STRING” specie . The visnetworks . The tarMolecular Complex Detection (MCODE) of Cytoscape was used to searching the highly connected sub-networks in the PPI network. In the illustrations depicting MCODE results, a vertex-weighting-based scheme was used to identify local high-density areas in the graph. Density was generally defined based on the connection level of the graph .12 and Reactome13  algorithm for each term.To reveal the potential biological functions of MGMD in the treatment of asthma, functional enrichment analyses were performed by the Database for Annotation, Visualization and Integrated Discovery actome13 . The resTo demonstrate the multi-compound therapeutic features of MGMD, network constructions were performed as follows: (1) herb-compound-target Network (H-C-T network) was constructed to explore the active compounds and their potential targets. The core compounds were obtained through the H-C-T network. (2) PPI networks were built to analyze the target interactions. Hub targets involved in MGMD treatment of asthma were selected from the PPI network. (3) BP sub-networks were established for classification analysis of BPs in MGMD treatment for asthma. (4) Target pathway network (T-P network) was constructed to show the functional pathways of MGMD for the therapy of asthma.14  and the attribute values of the cluster were shown in Clusters in a PPI network are often protein complexes and parts of pathways, while clusters in a protein similarity network represent protein families. Cluster 1 comprised nine nodes and 33 edges with a score of 8.250 . The seeTo further explore the biological functions of the 72 potential targets of MGMD, GO enrichment analysis was constructed on DAVID website and obtained 76 terms (FDR < 0.05) of BPs. We found the top 25 most enriched BP terms represented in a bubble chart . BP analAs shown in –5 were displayed  and the top 16 pathways at a significance threshold of FDR < 10isplayed . The patThe T-P network showed the associations of the top 16 pathways with 75 nodes and 187 edges . The resFurthermore, we verified the binding affinity between core compounds and protein targets of MGMD by molecular docking. As screened in H-C-T network, quercetin, wogonin, luteolin, naringenin, and kaempferol were the core compounds of MGMD. The hub protein targets were identified by top 5 node degree of PPI network and seed node of clusters, included STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.The results obtained by the molecular docking software were shown in Asthma is a major public health challenge in China that affecting 45.7 million adults aged 20 years or older . TCM hasin vovo , ALOX15 (15-LOX), PTGS1 (COX1), PTGS2 (COX2), cytochrome P450 proteins CYP2C9 and CYP2C19, which paly pivotal role in the resolution of inflammation in asthma . 5-LOX iFurthermore, STAT3, JUN, EGFR, and VEGFA were also the core potential targets in our PPI network results. STAT3 and JUN are closely related to the pathological process of asthma . STAT3 cThe GO enrichment results strengthen the evidence that candidate protein targets of MGMD are involved in airway inflammation and remodeling. The enriched BPs of anti-inflammatory in our results included inflammatory response, leukotriene metabolic process and arachidonic acid secretion, suggesting that MGMD could influenced the lipid mediators, both pro-inflammatory mediators and SPMs, to ameliorate the airway inflammation in asthma patients. Moreover, the typical airway remodeling processes, including positive regulation of smooth muscle cell proliferation, angiogenesis and collagen catabolic process, were also enriched by MGMD related targets. Combined with the PPI network results, we propose that MGMD regulates SPMs biosynthesis by ALOX5 to reduce airway inflammation and relieve airway remodeling process by suppressing the EGFR and VEGFA signaling.Our Reactome pathway enrichment results consistently argue that MGMD reduces allergic airway inflammation through the biosynthetic pathways of SPMs. Arachidonic acid metabolism, metabolism of lipids, biosynthesis of EPA-derived SPMs, biosynthesis of DHA-derived SPMs and biosynthesis of DPAn-3 SPMs were all enriched in Reactome pathway enrichment analysis. Additionally, interleukin-4 and interleukin-13 signaling, signaling by interleukins and immune system pathways were also enriched in our results. IL-4 and IL-13 are principal regulatory cytokines mainly secreted by activated Th2 cells, crucially important during the immune response in allergy and asthma . The recAccording to the compound-target interaction analysis, six targets with higher degrees on the PPI network  were identified. In our molecular docking analysis, the binding mode with the best docking score was selected to analyze the interaction between the protein receptor and its predicted active compounds. The docking scores of all compound-target pairs are lower than −5 kcal/mol, indicating that each of the five core compounds  has good binding affinity to all six targets. These results suggested that the core ingredients of MGMD could relieve airway inflammation and remodeling through binding STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.On the ground of the above, network pharmacology strategy was presented to investigate the active compounds, potential anti-asthmatic targets and involved regulatory pathways in MGMD. Our results revealed the active ingredients and potential molecular mechanism by which MGMD treatment is effective against airway inflammation and remodeling in asthma through regulating IL-4 and IL-13 signaling and SPMs biosynthesis. These findings were partially validated previous studies. Our subsequent molecular docking showed that each bioactive compounds  of MGMD has favorable binding abilities with STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5, further arguing the potential molecular mechanism of action of MGMD in asthma. Our results will serve as a comprehensive reference for investigation of the mechanism by which MGMD intervene asthma.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/GW, BZ, and ZW: conceptualization. GW and BZ: methodology and visualization. BZ: software. GW and ZW: writing–original draft preparation. BZ, YM, YL, and XY: writing–review and editing. XY and CF: supervision and project administration. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Coronary heart disease (CHD) is one of the most important complications of diabetes mellitus, having a high disability fatality rate. Qiweitangping is a Chinese medicine to control diabetes (type 1 and type 2 diabetes) and complications, having been used in clinic for more than 20 years, with the expected therapeutic effect. In a previous clinical study, the total effective rate of the drug for the treatment of type 2 diabetes reached 92.7%. However, the mechanism of the treatment process is unclear. Therefore, this research was conducted to explore the mechanism of treating diabetic coronary heart disease with the assistance of bioinformatics methods.  The TCMSP database was used to collect the effective chemical constituents of Qiweitangping and the target genes of the chemical constituents, and the related genes of diabetic CHD were obtained from the GeneCard database. Furthermore, the intersection was found between the target gene of the drug and the related gene of the disease to obtain the candidate genes; the STRING database and DAVID database were used to perform protein interaction analysis and KEGG enrichment analysis on the candidate genes. Also, molecular docking was used for auxiliary verification. Finally, a “drug component-gene target-pathway” network was constructed by using Cytoscape software.  Sixty-two effective chemical components including naringin, diosgenin, formogenin, isorolin, and isocryptanshinone, fifty-nine candidate target genes , and thirty-nine related pathways in Qiweitangping were obtained. In addition, two pairs (CASP-naringenin and STAT3-cryptotanshinone) of molecular docking results showed good affinity .  The results of the study indicate that Qiweitangping treats diabetic CHD with multiple chemical components. Its mechanism of action may be related to the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway. STAT3 and CASP genes have been verified by molecular docking to have a relatively good combination with Qiweitangping. This study is the theoretical basis for further experimental study on the treatment mechanism of diabetic CHD with Qiweitangping. Diabetes is a metabolic disease characterized by high blood sugar. According to data released by the International Diabetes Federation in 2019, 463 million adults aged 20 to 79 have diabetes worldwide [Diabetes can cause complications that cause severe damage to the heart, blood vessels, eyes, kidneys, and nerves, especially diabetic coronary heart disease (CHD). Diabetic CHD is one of the most common complications of diabetes . In the Liu Shaowu, a Chinese medicine practitioner, proposed a unique theory of traditional Chinese medicine (TCM). Liu's medical theory has been successfully used in his clinical practice. Qiweitangping is a Chinese medicine developed based on Liu's medical theory. Qiweitangping, made from 27 kinds of Chinese herbal medicine, is a medicine to control diabetes (type 1 diabetes and type 2 diabetes) and the complications. A clinical study of 150 diabetic patients  showed tHowever, Qiweitangping's chemical components are relatively complex, for it is made from a variety of Chinese herbal medicine, which make the mechanism of treating disease difficult to study. Nowadays, with the assistance of bioinformatics methods, it is possible to study the mechanism of Qiweitangping's disease treatment.This research used the network pharmacology method in bioinformatics to find potential mechanisms of Qiweitangping on the treatment of diabetic CHD and use molecular docking for auxiliary verification. Network pharmacology is an emerging discipline that reveals the effect of Chinese medicine on the body's regulatory network. Due to its holistic and systematic research methods and its emphasis on drug interactions, it is consistent with the characteristics of multitarget and multipathway in Chinese medicine. It has become an effective tool to systematically analyze the multitarget and multipath mechanism of traditional Chinese medicine compounds. The technical flow chart of this study is shown in https://tcmspw.com/index.php) of traditional Chinese medicine system, twenty-seven Chinese herbal medicines were used as keywords to search and filter chemical composition. The database contains about 500 drugs included in the Pharmacopoeia of the People's Republic of China, which provides component absorption, distribution, metabolism, and excretion (ADME) data and target and disease information. In addition, the components not included in the TCMSP database were collected by querying the BATMAN (http://bionet.ncpsb.org/batman-tcm/) database. With reference to the relevant literature [In the pharmacology database and analysis platform TCMSP  database to form a disease gene dataset.The gene targets corresponding to the effective chemical components were searched and exported through the TCMSP database to form a dataset of drug target genes. Target genes related to diabetic CHD were collected through the GeneCards  was used to construct a PPI (protein-protein interaction) network map of the proteins translated by the candidate target genes, and the connectivity value of each candidate target genes were calculated.Biomolecular functional annotation system STRING (http://www.rcsb.org) website, and the structural formula of the drug molecule was obtained from the TCMSP (https://tcmspw.com/tcmsp.php) and TCMID websites. AutoDockTools 4.2.6 software was used to process and dock the target protein and drug (http://119.3.41.228:8000/tcmid/) molecule, and PyMOL 2.7 software was used to visualize the results.Three-dimensional structures of the target protein were downloaded from the PCSB PDB  database, and KEGG enrichment analysis was performed.Then, gene clusters were entered in the DAVID ) to intervene in animals for 4 weeks. The results show that naringenin can significantly increase the level of nitrate/nitrite (nitrogen oxide) and upregulate the expression of phosphorylated eNOS (p-eNOS) and endothelial nitric oxide synthase (eNOS), while the high fructose diet will reduce the expression of eNOS (p-eNOS) and endothelial nitric oxide synthase (eNOS) . Naringeβ events caused by STZ [Diosgenin has potential effects on the initiation and cell regeneration of d by STZ . Dioscind by STZ .Isorhamnetin inhibits the instantaneous release of calcium ions in cells, thereby resisting the damage of oxidative damage to endothelial cells , 14.Experimental studies have shown that the function of formononetin to relax blood vessels may be related to processes such as inhibition of voltage-dependent calcium channels, intracellular calcium ion release, activation of potassium ion channels, and NO release , 16.In the GeneCard database, 940 diabetic-CHD-related genes are collected. Then, 59 common genes , obtaineIn We input the common gene information into the string database and get the protein-protein interaction (PPI) network diagram of these 59 genes . The staEach circular node in The ordinate in From the analysis of the PPI network diagram , the relAKT1 regulates the basic functions of vascular cell proliferation, differentiation, migration, and apoptosis and is very important for endothelial cell function and vascular integrity. Its mechanism may be related to the activation of the PI3K-Akt signaling pathway and upregulation of VEGF expression. It promotes angiogenesis and reduces the risk of myocardial ischemia caused by CHD .CASP3 belongs to the subfamily of apoptotic proteases. Its activation mode is mainly through the damaged cell mitochondrial membrane, released from the cell to the outside of the cell to induce cell apoptosis .VEGF-A specifically acts on vascular endothelial cells, promoting the proliferation and migration of vascular endothelial cells, inhibiting endothelial cell apoptosis, promoting angiogenesis and maintaining blood vessels, improving insulin resistance, etc. .Studies have pointed out that silencing MAPK1 promotes autophagy and inhibits the further development of CHD .STAT3 is an important part of the JAK/STAT3 signaling pathway. At the same time, STAT3 can have a beneficial effect on cardiomyocyte function by activating the mitochondria and transcriptional response, and it can protect the heart by regulating the cardiac microenvironment and the activity of cardiac fibroblasts, which may involve reducing oxygen consumption of cardiomyocytes, inhibiting cardiomyocyte apoptosis, promoting myocardial differentiation, anti-inflammatory, etc. , 23.We further applied molecular docking in our research to verify the binding energy between the gene targets and drug compounds. The results of molecular docking are shown in The lower the binding energy, the stronger the binding force between the protein and the drug molecule. Affinity < −4.25 kcal/mol indicates there is possibility of binding between the ligand and the receptor, affinity < −5.00 kcal/mol indicates good binding strength, and affinity < −7.00 kcal/mol indicates satisfactory binding strength . In the With the assistance of MCODE analysis, three candidate gene clusters of the constructed PPI network were obtained . MeanwhiTwo modules with higher score and larger number of genes were selected for follow-up research. The gene information of gene cluster 1 and cluster 2 were uploaded to the DAVID database, and signal pathways such as the HIF-1 signaling pathway, TNF signaling pathway, and VEGF signaling pathway were obtained Tables . These sKEGG pathway enrichment analysis results show that Qiweitangping in the treatment of diabetic CHD involves the HIF-1 signaling pathway, PI3K-Akt signaling pathway, ErbB signaling pathway, and other signaling pathways.α regulates the expression level of downstream functional genes, mediating the response of organs and tissues to microcirculation hypoxia, and participates in the occurrence and development of diabetes complications [The HIF-1 signaling pathway can promote cardiomyocytes to adapt to ischemia and hypoxia by affecting VEGF expression, mitochondrial function, cell apoptosis, antioxidative stress, etc., to protect or restore their functions . HIF-1α ications . Knockinications .The JAK/STAT3 signaling pathway is a modulator of cytokine signal transduction, which can be used in a variety of stress responses, including ischemia, hypoxia, and oxidative stress, and plays an important role in mediating insulin resistance damage to myocardial protection . Animal The ErbB signaling pathway plays an important role in the maintenance of cardiac function. In cardiac tissue, neuregulin-1 can bind to ErbB4 receptors and form heterodimers with ErbB2 receptors. Phosphorylation of specific tyrosine residues on the protein takes place, thereby activating the PI3K/Akt signaling pathway .The U.S. Food and Drug Administration issued an announcement stating that GnRH agonist drugs lead to an increased incidence of cardiovascular disease and diabetes. This indicates that the GnRH signaling pathway may be related to the occurrence of diabetic CHD .To clarify the relationship between drug chemical components, candidate genes, and signaling pathways, a visualized multivariate network, basing on the data collected in the database, was constructed .Fifty-nine blue square nodes represent target genes, sixty-two yellow nodes represent chemical components, and nine green triangles represent signaling pathways.The “drug component-gene target-pathway” network displays the associations between drugs, candidate gene targets, and main signal pathways with lines. The density of the lines in the network reflects the complexity of the mechanism of action and the degree of association between the elements. This network visualizes the treatment mechanism of multicomponent, multitarget, and multisignal pathways. It is consistent with Liu's “harmonious whole” medical theory.This study lays the foundation for further research on the mechanism of Qiweitangping's disease treatment. Subsequent researchers may use the genes and signal pathways obtained in this study to conduct further experimental verification to determine the mechanism, for example, detecting changes in gene expression of the candidate genes we obtained such as AKT1, CASP3, VEGF-A, testing changes in signal pathways such as the HIF-1 signaling pathway, PI3K-Akt signaling pathway, and ErbB signaling pathway, and studying biological processes such as apoptosis, angiogenesis, and inflammation. Meanwhile, the verification results of the molecular docking of STAT3 and CASP are good, which means better expectations for the experimental study of the expression of these two genes.Although this article has found some potential targets and pathways of Qiweitangping in the treatment of diabetic CHD through network pharmacology, what we know is still not enough to clarify all the actual mechanisms of action of the drug.Compared with Western medicine, Liu's medical theory, like traditional Chinese medical theories, treats the whole human body and treats diseases from a macroperspective. When treating diabetes, a complex disease that can cause multiple complications, Qiweitangping, developed based on the thinking of “coordinating the whole, highlighting the local,” performs well in clinical practice. Compared with Western medicine, Chinese herbal medicine not only can treat diseases in all directions and multiple targets but also has milder medicinal properties and will not cause obvious side effects to the human body while treating.However, Qiweitangping's shortcoming is also obvious. Patients often need to take drugs for months or even years to improve their condition. Therefore, patients often need to use it together with common drugs in the early stage of treatment.


Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (  •ACSNI uses deep neural network to decompose transcriptomic measurements•ACSNI maps pathway components to target biological or clinical phenotypes•ACSNI robustly extracts regulatory output of transcription factors•ACSNI enables annotation of genes with unknown functions Methods that group genes into functional units to quantify pathway activities are critical in the analysis of biological systems. Although many components of biological pathways have been described in detail, these tend to be limited to well-studied genes. In contrast, the majority of possible components remain unexplored. Here, we present a machine-learning tool for constructing and predicting tissue-specific components of biological pathways from large biological datasets. Our algorithm, ACSNI, can tackle incomplete pathway descriptions and enhance current pathway analysis methods' performance. We anticipate that, by dissecting the complex signals in biological data in a flexible and context-specific manner, ACSNI can facilitate the full characterization of physiological systems of interest. ACSNI software presents a flexible approach to discover unknown components of a biological pathway. We demonstrate through simulation and biological user cases that ASCNI has superior performance in inferring pathway activities compared to other approaches. In particular, it is able to identify unknown components of pathways in a tissue-specific manner, facilitating the full characterization of physiological systems of interest. One feature common to all cells is the dynamic ability to coordinate activities through many pathways that receive and process signals from the environment and different cell regions.Genetic, biochemical, and biophysical techniques such as small interfering RNAs, small molecular inhibitors, immune precipitation, and gel filtration are common procedures for identifying molecular functions and pathway components.Z score (Z score),Another approach for identifying unknown components of pathways involves first estimating a summarized pathway activity from GEPs based on prior knowledge of the target pathway using gene set enrichment analysis tools, such as gene set variation analysis (GSVA), pathway-level analysis of gene expression (PLAGE), single-sample gene set enrichment analysis (ssGSEA), and the combined Here we provide software termed automatic context-specific network inference (ACSNI), which leverages artificial intelligence for the reconstruction of a biological pathway and aids the discovery of pathway components and classification of the crosstalk between pathways in specific tissues. The method draws on the principle that cell signaling networks are organized into several small, highly connected modules  that combine hierarchically into larger units to regulate cellular functions.ACSNI is a three-step method to infer tissue-specific components (genes) of a pathway from tissue GEPs. It performs (1) unsupervised derivation of pathway subprocesses, (2) estimation of subprocess-gene interaction scores, and (3) inference of tissue-specific components of the target pathway . ACSNI rTo assess the performance of ACSNI in reconstructing signaling networks and their subprocesses, we utilized pseudo-simulated and real expression datasets. It is natural to ask to what extent the ACSNI predictions improve on current methods for inferring the component of a pathway from GEPs. Unfortunately, there are no existing tools that provide such a granular view of pathway subprocesses. Therefore, we focused our performance evaluations on reconstruction of simulated signals and concordance between two cohorts of similar samples.,In biological systems , multiple signaling pathways are executed concurrently with a certain level of dispersion  across a group of samples. ACSNI has been designed to extract differences in pathway activity in a cohort of samples and detect unknown components of the pathway. To assess how well the algorithm achieves this aim, we first evaluated its ability to reconstruct a known signal using pseudo-simulated GEPs under different parameter settings see . We simu−13, To measure the type I error rate, we divided the number of random predictions  by the total number of predictions  in the above simulation and assessed the impact of different parameter settings. We observed a median false discovery rate (FDR) of 0.0045  for default parameter settings across three feature selection thresholds . This ob−7, −3, We then tested whether the approach was a robust predictor of pathway components across different cohorts of similar samples. To this end, we randomly sampled two non-overlapping groups of 290 samples from the 580 samples of the GTEx healthy adult lung dataset to generate a training cohort and a test cohort. We also randomly shuffled the test cohort to generate a null model see . Next, w,To evaluate the performance of ACSNI on real data, we simulated a condition whereby known context-dependent signaling crosstalk was expected. Specifically, a recurrent event in clear-cell renal cell carcinoma (ccRCC) is the hyperactivation of the mTOR signaling that promotes oncogenic metabolic programs.,−12, −3, w3, w7, and w9 were exclusive to KLF6 signal and w0 was exclusive to EPAS1, while w4, w5, and w2 captured both signals but have relatively more enrichment of EPAS1 signal  appear to capture signals unrelated to KLF6 and EPAS1. These observations demonstrate that ACSNI models the complexities of a signaling network as represented by an experimentally curated gene set.Consistent with the above, we hypothesized that mTOR signaling network derived from GEPs in ccRCC patients could enrich for both KLF6 and EPAS1 signals. To test this hypothesis, we collected data of 73 ccRCC samples with matched normal adjacent tissues from the TCGA ccRCC project . We applied ACSNI to the RNA-seq data using a publicly available curated mTOR signaling gene set from the PID (n = 67).1 signal C. Three +]). We found an insignificant over-representation for DE genes (n = 119) derived from EPAS1-KO in HUVECs Z score  were strongly associated with the disease status (w8 and w6) were not associated with KLF6 nor EPAS1 signal (w8-associated genes (the top disease-associated subprocesses) by mTOR. To this end, we analyzed published GEP data (GEO: GSE106819)w8 in ACSNI predictions, 77% (10/13 genes) were significantly altered by mTOR inhibition  have been identified as essential ccRCC genes in a CRISPR genome-wide loss-of-function screenThe identification of biologically or clinically essential pathway subprocesses is a crucial aim of many signaling network analyses. ACSNI provides additional functions to map the derived network to a target biological or clinical variables. To demonstrate the utility of this function, we analyzed the distribution of the mTOR subprocesses across the tumor and normal adjacent tissue from the TCGA datasets (variable: disease status). We found that three subprocesses , we evaluated the enrichment of the available eight TFs  at the promoter regions (±1 kb from transcription start site [TSS]) of the predicted genes compared with the background (enrichment = mean peak density of predicted genes/mean peak density of background genes). ACSNI-predicted genes were strongly enriched for bZIP transcription factor binding (ratio > 1) but depleted of other families of TFs (ratio < 1) (−9), CEBPD (p = 1.278 × 10−4), NFE2L2 (p = 1.168 × 10−3), and JUN (p = 1.041 × 10−2) compared with the background genes. Conversely, we observed significantly lower binding for EP300 (p = 7.582 × 10−32), RELA (p = 7.132 × 10−10), ERG (p = 1.841 × 10−5), and IRF1 (p = 3.148 × 10−2) compared with the background genes. Interrogating the ACSNI-derived subprocesses of ATF2 signal and their associated genes revealed unique associations with different bZIP dimerization patterns tio < 1) E. We obstio < 1) , confirmpatterns F. Specifese data F. These Z score . To demonstrate this utility, we focused on the annotation of the lncRNA HOTAIRM1. Specifically, HOTAIRM1 is a natural antisense transcript of the HOXA1 gene that plays roles in kidney differentiation and regulation of HIF1-dependent angiogenic pathways.ACSNI-derive module to first generate a de novo gene set (n = 50) before running two iterations of ACSNI (see GSE136604)−5, only genes measured in both datasets, To this end, we leveraged the GTEx project of GEPs in normal kidney tissues and investigated the network of HOTAIRM1. Because this case deals with a single gene, we utilized the CSNI see . We idenWe next evaluated the robustness of the predictions by comparing the enrichment with predictions from a null model derived by random shuffling of the expression matrix. We found no significant enrichment of genes differentially expressed in the HOTAIRM1-KO data F, confirOur knowledge of biological pathways and their components is far from complete. This situation limits the accuracy of the current pathway and network analysis methods that depend on accurate and detailed pathway annotations. ACSNI enables the identification of tissue-specific components of biological pathways from gene expression data. This approach overcomes the limitations of using composite scores or representative values, which are unable to fully capture the process or handle the inherent variability in GEPs within a gene set. By contrast, ACSNI can capture pathway modules while reducing the impact of technical noise. This capability was demonstrated in our identification and validation of mTOR signaling crosstalk with KLF6 and EPAS1 in ccRCC. ACSNI can also reveal the context-specific output of TFs in a gene set and identify lncRNA network in expression data. This utility is especially vital in mechanistic studies where the target is context-specific gene regulation. By using prior knowledge and interrogating associated expression variability in GEPs, ACSNI can identify biological network within a specific tissue and has the flexibility to apply weights to the prior information.Using an autoencoder the core of ACSNI decomposes GEP into multiple pathway subprocesses, which are then used to infer unknown pathway components. Other dimension-reduction methods, including principal component analysis (PCA) and non-negative matrix factorization (NMF), could also theoretically be used to estimate pathway subprocesses from GEPs. The key limitation of these methods is that the mapping from the gene expression space to the low-dimensional representation is restricted to be linear.ACSNI-derive, which enables de novo generation of gene sets as demonstrated in the annotation of HOTAIRM1 functions. In addition to the availability of prior information, ambiguous annotations, sample size, gene set size, and expression variance can all affect the performance of ACSNI. Currently, ACSNI depends on RNA expression across a cohort of samples to infer pathway circuitry and will miss post-translation interactions, since RNA levels may not correlate with protein output.One potential limitation is a lack of prior knowledge in the form of a gene set; this can be partially remedied by Independent validation of the inferred network is an essential step in the discovery of pathway components and their functional interactions. Ideally, such analysis should involve independent genetic perturbation data, reporter assays, or DNA occupancy analyses as demonstrated in our use cases. However, genetic perturbation or DNA occupancy data may not always be available. Literature mining can be an alternative approach, but the results may be confounded by publication bias. Our results show that this framework effectively infers biological networks from GEP datasets and enables the discovery of novel functional interactions. Analysis of publicly available GEP data using ACSNI will lead to new insights into the molecular basis of a phenotype and its deregulation in disease.It is well established that signaling pathways are coordinated by complex interdependences between chromatin structure, DNA modifications, RNA abundance and modifications, and protein abundance and modifications. While ACSNI includes the option to account for one additional piece of regulatory information as an integer variable, this is unlikely to model the complete interdependences. We plan to further develop the ACSNI software by expanding the utilization of other genomic information.ACSNI-run and ACSNI-derive) for predicting the components of a signaling pathway from GEPs using prior knowledge. It also provides a function (ACSNI-get) to describe the correlation between the derived pathway subprocesses and biological traits. The ACSNI package includes a function (ACSNI-split) to process GEPs for bootstrap analysis. Users can install the Python package and its dependencies through the pip package installer .ACSNI offers the cell and molecular biologist a flexible approach to infer potential pathway components that can be used to guide functional analysis in a target signaling network. The ACSNI package provides command-line functions (a.anene@qmul.ac.uk).Further information and requests for the implementation and troubleshooting the ACSNI software should be directed to and will be fulfilled by the lead contact, Chinedu Anthony Anene  a curated set of genes associated with P based on current knowledge of their biological functions (gene set).P is organized into several small, highly connected subprocesses (Si–n) that combine hierarchically into larger units.Si is the contribution of the ith subprocess to the pathway P and n is the total number of subprocesses.We introduce ACSNI, an algorithm to infer the components of a biological pathway from bulk tissue expression profiles and prior knowledge of the pathway A. Let P n) is unknown a priori. Our best estimate of n is the current knowledge of the target biological pathway (P), as specified by the gene set K. To address this issue, we let n depend on the cardinality of the gene set |K|, defined as a percentage of |K| and estimated using a recent algorithm explicitly developed for autoencoders (described below).n can be specified by the user based on their knowledge of the target pathway.While the hierarchical model is a faithful description of cell signaling pathways, a practical issue is that the number of subprocesses :f (encoder) is a function of the expression to the pathway (P), d (decoder) is a function of the pathway to the expression, and k ∈ K. We restrict feature selection to the removal of uninformative genes that have a median absolute deviation below a threshold . ACSNI starts from the RNA expression values for the n subprocesses in 1|K|/100) and extract the latent structure.Run the autoencoder above using 50% of the gene set's cardinality . Therefore, we also implement PCA and NMF, two linear dimension-reduction methods, into the ACSNI software, allowing users to cross-compare the results.Other dimension-reduction methods, including PCA and NMF, have been applied to gene expression datasets and could theoretically be used to estimate pathway subprocesses (W matrix) of the first hidden layer, which represents the reduction of the gene set expression profiles to latent structures. W has a dimension of m × n  and is the activity of the subprocesses defined in The neural network above minimizes the error between the input expression vector W above corresponds to per-gene interaction between individual latent vectors and the gene. We model this interaction asβ is the slope, Si is the activity of ith subprocess, and For the rest of genes in the transcriptome, the strength of the linear relationship between their expression and the vector of 2 ; this can be interpreted as the coefficient of determination for the proportion of the variance in that gene's expression across the samples that is predictable from each vector of the W matrix, defined asj, j and Next, for each run of e) with genes in the row and subprocesses in the column as the (N matrix), which is used for prediction of unknown pathway components below.The Code), which represents the reduction of the subprocesses to the pathway. Code has a dimension of m × n (m is the number of estimated subprocesses and n is the number of estimated higher pathway units) and is the activity of the pathway defined in Further, we extract the learned weights of the second hidden layer (en be the scores for the interaction between the ith subprocess and the genes in the rest of the transcriptome (bound 0–1). α and β . The cutoff can be interpreted as the probability threshold to reject the null hypothesis that such an extreme value is expected less than 1% of the time. Simulation analysis showed that the 0.01 cutoff reduces the FDR without affecting the ability to recover the expected signal compared with the traditional 0.05 for rejecting a null hypothesis  to each subprocess; the higher is SSw, the more biologically significant is subprocess w for the given variable. We derive SS using logistic regression for categorical variable or simple linear regression for numerical variables, implemented as the ACSNI-get module (see code at https://github.com/caanene1/ACSNI).Here, we define a subprocess significance measure as a function x neurons in the first layer, and (x/2) neurons in the second layer of the network. x is automatically determined, as described in −3 for training. Activities of the neurons were normalized using layer normalization that calculates the normalization statistics for all units in the same layer. The RELU function, defined as RELU(x) = max, was used as a non-linear activation in the two layers. In the mirror image, the Sigmoid function, defined as Sigmoid(x) = 1/1 + ex−, was used as the activation function. We train the model for 3,000 epochs and configure the checkpoint to save at the end of every epoch, if it is the best model seen . Regularizers l1 and l2 in the ACSNI objective function were set to 1 × 10−16 and 1 × 10−9, respectively. ACSNI is implemented in Python 3 and relies on the tensorflow, sklearn, numpy, and pandas packages.The neural network used in ACSNI consists of two fully connected layers and a mirror image of this neural network as a decoder . The topmost correlated targets are then selected to construct a search space and negative controls (top 50 with r > 0.6 and r < −0.6). We search this space using multiple iterations of ACSNI and extract genes predicted in more than 60% of the iterations in the presence of the target as the functional gene set (see code at https://github.com/caanene1/ACSNI).The requirement for a pre-annotated gene set is a challenge because many biological processes remain completely unknown. For example, few gene sets are available for the pathways of non-coding RNAs. To address this problem, we develop the W matrix and interaction prediction (P matrix). This process is included in the ACSNI software and generated automatically.For the evaluation of randomness in the ACSNI predictions, we randomly shuffled the gene expression values of each sample. This process generates random expression profiles without altering the samples' library sizes. We then apply the ACSNI model as described before under the assumption that the predictions must be false positives, originating from an uninformative pattern extraction in the 1For a given gene set, split the expression data into two groups: (1) expression of genes in the gene set (data 1) and (2) expression of the rest of the genes (data 2).2.Individually shuffle the column values of data 2, such that the row values are randomly assigned without altering the original column sums .3For each row in data 1, individually reverse the ordering of the values (right to left), whereby the last column gets the expression in the first column and the first column gets the expression in the last column . This signal is expected because ACSNI is invariant to the direction and maintains the order of the columns.4Create ten versions of data 1 with different log-normal noise levels  randomly added to each value . This signal can be interpreted as the technical noise caused by RNA-seq and alignment errors. Each of the ten versions has one level of noise and independent of the other.5Finally, for the given gene set merge the transformed datasets from steps 2 to 5 with data 1 to form one expression matrix . Output the row names of data 1 as the new gene set, creating expression and gene set pairs.To evaluate how well ACSNI reconstructs a known signal and its FDR, we created pseudo-simulated RNA-seq data. We did not use purely synthetic data because we wanted to ensure that our simulation reflects the expected results from real RNA-seq datasets. Specifically, we extracted the RNA expression values from the GTEx project  and 50 gene sets from the MSigDB database.Note that these datasets were analyzed with ASCNI as pairs because each expected signal is specific to a given gene set. We provide details of the ACSNI parameters used to analyze the simulated datasets in the Z score.Z score implemented in the GSVA R package to estimate pathway activity for each sample. We then correlated the pathway activities from each method with the expression of each gene across the samples. The gene-wise correlation coefficients were classified at >0.4 and < −0.4 to predict pathway genes (see code at https://github.com/caanene1/ACSNI). We did not apply the ACSNI custom classification method to correlation coefficients because our approach required a beta distribution, and the correlation coefficients have a different distribution  with four alternative approaches: GSVA, PLAGE, ssGSEA, and ribution B.ACSNI-derive module), we compared the ACSNI prediction with the pairwise correlation analysis and differential expression analysis between discretized expression groups (HOTAIRM1-high: >median versus HOTAIRM1-low: <median). For correlation analysis we used the default pearsonr and for differential expression, ttest_ind implemented in the SciPy Python package. Genes were classified as pathway components at correlation coefficient of >0.6 and <−0.6 or two-tailed adjusted p values of <0.05 (see code at https://github.com/caanene1/ACSNI).For the single gene example  and used in the rest of the ACSNI calculations.For all methods, the input data were either RSEM or transcripts per million (TPM), which are the preferred data transformations and are also used in ACSNI.For GEO RNA-seq datasets, the raw reads were filtered to remove the adaptors and the low-quality reads (Q < 20) using Trimmomatic.q value <0.01 or otherwise stated in the figure legends.The R ClusterProfiler and the human Bioconductor annotation database (org.Hs.eg.db) were used to investigate the biological processes associated with the predicted components of the studied pathway. Ontologies were filtered for redundancies by semantic similarity analysishttps://github.com/caanene1/ACSNI.We utilized a non-parametric randomization test to evaluate the significance of the overlap between the genes predicted by ACSNI and genes in a validation signal, or between two ACSNI predictions. For the comparator variable, we randomly reassigned the labels and calculated the overlap statistic with the target labels. We repeated this process 10,000 times and counted the number of times the overlap statistic was greater than the observed statistic. We then estimated the empirical probability  as the count divided by the total number of randomizations . The R implementation of the randomization test analysis is available at https://github.com/caanene1/ACSNI.The R fitdistrplus and logspline packages were used to investigate the interaction scores' distribution . We comp