Heart failure (HF), a clinical syndrome with a high incidence due to various reasons, is the advanced stage of most cardiovascular diseases. Huangqi is an effective treatment for cardiovascular disease, which has multitarget, multipathway functions. Therefore, we used network pharmacology to explore the molecular mechanism of Huangqi in treating HF. In this study, 21 compounds of Huangqi, which involved 407 targets, were obtained and reconfirmed using TCMSP and PubChem databases. Moreover, we used Cytoscape 3.7.1 to construct compound-target network and screened the top 10 compounds. 378 targets related to HF were obtained from CTD and GeneCards databases and HF-target network was constructed by Cytoscape 3.7.1. The 46 overlapping targets of HF and Huangqi were gotten by Draw Venn Diagram. STRING database was used to set up a protein-protein interaction network, and MCODE module and the top 5 targets with the highest degree for overlapping targets were obtained. GO analysis performed by Metascape indicated that the overlapping targets were mainly enriched in blood vessel development, reactive oxygen species metabolic process, response to wounding, blood circulation, and so on. KEGG analysis analyzed by ClueGO revealed that overlapping targets were mainly enriched in AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, c-type lectin receptor signaling pathway, relaxin signaling pathway, and so on. Finally, molecular docking showed that top 10 compounds of Huangqi also had good binding activities to important targets compared with digoxin, which was carried out in CB-Dock molecular docking server. In conclusion, Huangqi has potential effect on regulating overlapping targets and GE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, and so on to be a latent multitarget, multipathway treatment for HF. Heart failure (HF) is a clinical syndrome of cardiac insufficiency caused by abnormalities in cardiac structure remodeling due to various reasons, which is the advanced stage of most cardiovascular diseases' progression . The preTraditional Chinese Medicine (TCM) has received extensive attention and research for its long-lasting effects and fewer side effects . TCM notDue to the characteristics of multicompound, multipathway, and multitarget of TCM, it is difficult to clarify the pharmacodynamic basis and mechanism of TCM. The concept of network pharmacology was first proposed by the British pharmacologist Hopkins in 2007 . The molhttp://lsp.nwu.edu.cn/tcmsp.php), one of the world's largest noncommercial TCM molecular databases, collects 499 herbs from the 2015 Chinese Pharmacopoeia and the ingredients of each herb, including 13144 molecules and 29384 compounds [https://www.ncbi.nlm.nih.gov/pccompound) to confirm the final compounds of Huangqi.Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP: ompounds . TCMSP pompounds . In thisompounds . The prihttp://www.drugbank.ca) [http://www.uniprot.org/), a database that centrally collects protein resources, is the most comprehensively and functionally annotated database of protein sequences [Homo sapiens.” The repeated, nonhuman, and nonstandard targets were eliminated, and the targets related to compounds of Huangqi were finally obtained through retrieval and transformation to gene symbols.The targets related to compounds of Huangqi were further predicted based on the computer targeting technology developed by the TCMSP. Targets' information was obtained from TCMSP and the predicted targets were reconfirmed by DrugBank (bank.ca) . UniProtequences . Then, thttp://ctdbase.org/) and GeneCards (https://www.genecards.org/) were used to select targets associated with HF. CTD is an open database that provides a centralized, integrated data of different types of molecules and toxicological data from various organisms. It integrates information about chemical-gene/protein interactions, chemical-disease, and gene-disease interactions [http://bioinformatics.psb.ugent.be/Webtools/Venn/) was used to analyze the targets' intersection of HF and Huangqi.Comparative Toxicogenomics Database (CTD: ractions . GeneCarractions . “Heart https://string-db.org/) to perform PPI analysis. STRING, a database that predicts direct and indirect interaction and builds the network between proteins and proteins, has 9,443,763 proteins from 2031 species of organisms. A protein with a combined score >0.4 was selected to construct a interaction network [The targets' intersection of HF and Huangqi was imported into the STRING 11.0 ( network . The ful network . Therefo network .http://metascape.org/gp/index.html#/main/step1), an efficient and intuitive tool that has the Integrated Discovery and Annotation capabilities, primarily provides classical batch annotation and GO terminology enrichment interpretation to emphasize the most relevant GO terms related to an offered gene list [Homo sapiens” [GO is a system widely used in the field of biology for the classification of gene functions and for describing the functions of gene products . Metascaene list . The tarsapiens” .P value ≤0.05 [KEGG, a knowledge base for gene function system analysis, links genomic information to higher-order functional information to obtain significantly enriched biological pathways . The tarue ≤0.05 .Network Analyzer plugin in Cytoscape 3.7.1 software was used to analyze the topology parameters of the network. This study evaluated the criticality of nodes in the network based on several parameters: (1) Average Shortest Path Length (ASPL) means the average density of the shortest path between all pairs of nodes, the smaller the ASPL, the closer the relationship between the nodes. (2) Betweenness Centrality (BC) can calculate the number of shortest paths passing through a node. The greater number of shortest paths passing through a node represents the higher BC. (3) Closeness Centrality (CC) reflects the proximity of a node to other nodes in the network. The closer a node to other nodes, the more central it is. (4) Degree represents the number of a node connected to the other nodes in a network. The greater the degree of a node, the more critical it is.http://cao.labshare.cn/cb-dock/) predicts the binding activities of proteins to compounds and calculates the center and size of the cavity. It is also integrated with AutoDock Vina and has been carefully optimized with a success rate of 70% [http://www.rcsb.org) and PubChem, respectively [CB-Dock -3-chroman-7-ol, astragalus polysaccharide, astragaloside IV, and cycloastragenol were an exception with no targets. Each edge represents the interaction between compound and target and a node with higher degree represents its importance in the network. Compound-target network shows an interaction between a compound and multiple targets and a relationship between one target and multiple compounds, which proves the synergistic effect of TCM . The topR-3-, TNF (37), AKT1 (36), VEGFA (35), and NOS3 (33) . MCODE wP value. The deeper the orange, the smaller the P value  were selected as the group representative. A spacefill and a cartoon chain represent a ligand and a protein, respectively. It is generally believed that the value of Vina score indicates that a certain binding activity between a protein and a compound. First, the more negative the binding energy (Vina score), the more stable the compound binds to the target. Second, if a cavity size is close to or bigger than the ligand, the accuracy of docking tends to increase . Molecul digoxin .TCM has a long history of understanding the etiology and pathogenesis of HF and its effect is obvious. According to the principle of syndrome differentiation and treatment of TCM, the clinical syndromes of HF mostly rely on “Qi” deficiency and “Water” stagnation. Therefore, TCM frequently strengthens “Qi” and accelerates “Water” excretion to treat HF as the main principle. Huangqi is one of the effective drugs that can strengthen “Qi” and accelerate “Water” excretion due to its “Gan flavor” and “Wen nature” . Althougβ, and RAF1. Simultaneously, the results also showed that Huangqi may ameliorate HF through regulating multiple pathways like GE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, c-type lectin receptor signaling pathway, and relaxin signaling pathway. Moreover, the main compounds of Huangqi also had good binding activities to important targets compared with digoxin, which further proved that Huangqi had potential multitarget, multipathway effects on treating HF.This study used network pharmacology to explore the material basis and molecular mechanism of Huangqi in treating HF. It was found that Huangqi played a potential role in improving HF by regulating multiple targets such as IL-6, AKT1, VEGFA, NOS3, IL-1β, TNF-α, IL-10, IkBα, and NF-kB in patients with HF are reduced under the treatment of quercetin, showing that quercetin possesses the obvious anti-inflammatory properties [κB, and Akt/GSK-3β signaling pathways to reduce apoptosis [β/NF-κB signaling pathway to improve coronary hemorheology and regulate endothelial dysplasia [κB, IL-1β, IL-6, TNF-α, and so on, restoring normal autophagic flux [In this study, flavonoids, saponins, and alcohols with better OB and DL in Huangqi were screened by TCMSP and PubChem databases, which indicated that the main material basis in treating HF of Huangqi may be flavonoids, saponins, and alcohols. Flavonoids included kaempferol and quercetin, saponins contained mairin and hederagenin, and alcohols included 7-O-methylisomucronulatol. To further confirm more important material basis of Huangqi, we constructed a compound-target network; the top 10 compounds with higher degree consisted of kaempferol, quercetin, mairin, hederagenin, 7-O-methylisomucronulatol, and so on. In the compound-target network, some key compounds are highly central and are used in cardiovascular research: the levels of IL-1operties . Moreoveoperties , 28. Stuoperties . Kaempfepoptosis . Hederagysplasia . Althouggic flux , 32–36. gic flux . Previougic flux –39. Thisβ, and TNF. Functions of overlapping targets can be classified into steroid hormone receptor activation and regulation of heart rate and blood pressure. These functions also associated with cardiomyocyte metabolism, electrocardiogram (ECG) activity, blood perfusion, inflammation, and oxidative stress. Moreover, overlapping targets of Huangqi and HF had similar biological processes and multitarget synergistic characteristics. For example, enhancing autophagy of cardiomyocytes by inhibiting AKT1 expression and VEGFA increase contribute to improved cardiac dysfunction, angiogenesis, and remodeling [β can induce cardiomyocyte apoptosis and reduce ventricular wall compliance and cause coronary microvascular endothelial inflammation [A total of the most 16 significant GO functional annotations were enriched in 46 overlapping targets of Huangqi and HF. More importantly, the most pivotal 7 targets from of PPI and KEGG analysis were screened using network analysis including IL-6, AKT1, VEGFA, NOS3, RAF1, IL-1modeling , 41. Eleammation . Study sammation . Both ADammation . KCNH2 aammation , 46. NOSammation . SOD1, Tammation –52. Atteammation , 54. Theα expression can improve cardiac function in HF rats [β signaling pathway in endothelial cells [In addition, the 65 pathways were mainly involved in regulation of inflammation and oxidative stress, immune response, fat and glucose metabolism, and regulation of angiogenesis. Diabetic cardiomyopathy is one of the mechanisms of disability and high mortality in diabetic patients and severely impairs myocardial contractile and diastolic function. The activation of AGE-RAGE signaling pathway in diabetic complications will lead to excessive production of advanced glycation end products to cause damage to cardiomyocytes and lead to HF . IL-17 s HF rats . The bodal cells . Moreoveal cells . The stual cells . Researcal cells –61. VariIn conclusion, 18 compounds with better OB and DL, 3 compounds with excellent pharmacological effects, and 407 proteins were selected as targets of Huangqi. 378 targets associated with HF and 46 targets' intersection of HF and Huangqi were obtained. The compound-target network revealed that the main compounds of Huangqi had anti-inflammation, antioxidation, antiapoptosis, improvement of blood perfusion, and regulation of vascular endothelial function to exert anti-HF effect. Moreover, GO analysis prompted that 46 targets' intersection of HF and Huangqi mainly enriched in steroid hormone receptor activation, regulation of heart rate and blood pressure, cardiomyocyte metabolism, ECG activity, blood perfusion, inflammation, and oxidative stress. KEGG analysis showed that the overlapping targets were involved in AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, c-type lectin receptor signaling pathway, relaxin signaling pathway, and so on. The docking results demonstrated that main compounds exhibited good affinity to the most critical targets, especially the hederagenin. This study supplied vision for further research into protective mechanisms of Huangqi for HF and provided a method to explore the material basis of TCM or TCM formula.


Non-alcoholic fatty liver (NAFLD) is a chronic disease worldwide, which poses a huge threat to human health. Xiaochaihu decoction is a well-known traditional Chinese medicine prescription. It has been proven effective in treating NAFLD but its mechanism is still unclear.Multiple mechanisms of Xiaochaihu decoction are explored by identifying and connecting potential targets and active ingredients in the treatment of NAFLD.Active ingredients and related targets of seven herbs were collected from TCMSP database. The related targets of NAFLD were obtained from Genes cards database, TDD and OMIM database. The intersected targets of disease targets and drug targets were input into STRING database to construct protein-protein interaction network. DAVID database was used for GO enrichment analysis and KEGG enrichment analysis.After screening and removal of duplicates, a total of 145 active ingredients and 105 potential targets were obtained. PPI network manifested that AKT1, IL6, JUN MAPK8 and STAT3 were the key target proteins. The results of GO enrichment analysis mainly involved cytokine receptor binding, cytokine activity, and heme binding. The results of KEGG analysis suggested that the mechanism mainly involved in AGE-RAGE signaling pathway in diabetic complications, Hepatitis C, fluid shear stress and atherosclerosis. The signaling pathways were further integrated as network manner, including AGE-RAGE signaling pathway in diabetic complications, Fluid shear stress and atherosclerosis, Insulin resistance, HIF-1 signaling pathway, Th17 cell differentiation and IL-17 signaling pathway. The network contained immunity regulation, metabolism regulation and oxidative stress regulation.Xiaochaihu decoction plays a key role in the treatment of NAFLD with multiple targets and pathways. Immunity regulation, metabolism regulation and oxidative stress regulation consist of the crucial regulation cores in mechanism.Design and workflow of this study Non-alcoholic fatty liver disease (NAFLD) referring to the excessive fatty liver accumulation, is one of the most rapidly increasing liver diseases all over the world . In decaXiaochaihu decoction comes from ShangHanLun, a well-known book in traditional Chinese medicine and is widely used in clinical practice with the effect on liver protection . Many reRadix Bupleuri), Banxia (Arum Ternatum Thunb), Renshen (Panax ginseng C. A. Mey.), Gancao (licorice), Huangqin (Scutellariae Radix), Shengjiang  and Dazao (Jujubae Fructus). The active ingredients in Xiaochaihu Decoction were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (http://tcmspw.com/tcmsp.php) with the keywords Banxia, Renshen, Gancao, Huangqin, Shengjiang and Dazao respectively. TCMSP, a unique Chinese herbal medicine system pharmacology platform comprising of 449 herbs, is capable of capturing the relationship between drugs, targets and diseases. The database includes chemical substances, targets and drug target networks, as well as related drug target networks. Oral Bioavailability (OB) is a significant pharmacokinetic parameter, which reflects the degree of drug absorption by the body. Drug-like (DL) refers to the similarity of the active ingredient with known drugs. The active ingredients with OB > 30% and DL > 0.18 are screened for further research [The constituent herbs in Xiaochaihu decoction are comprised of Chaihu , Therapeutic Target Database  [https://www.omim.org/).Gene cards and OMIM are online websites integrating the information about genes, disease and proteins and used for finding related targets of NAFLD. In addition, UniProt (https://www.uniprot.org) is used for finding gene names and Uniprot numbers of all targets [Potential targets of active ingredients were retrieved from TCMSP database Related targets of NAFLD are obtained from Gene cards (et/ttd/)  and Onli targets .In order to identify the corresponding relationships between the active ingredients and potential targets, we imported the active ingredients and potential targets into Cytoscape 3.7 to construct ingredient-target-disease network.http://string-db.org/cgi/input.pl) is an online database widely used for constructing protein-protein interaction (PPI) network [Homo sapiens” for searching and setting minimum required interaction score to high confidence (0.7). Finally, the top ten target proteins were input into Cytoscape to construct the interaction network.STRING ( network  STRING dhttps://david.ncifcrf.gov/) for GO and KEGG analysis. The DAVID database is an online website for elucidating the potential targets in the treatment of NAFLD in biological functions and signaling pathways [Homo sapiens.To further explore the role of the mentioned screening targets above with biological signaling pathways, the potential targets are imported into the Database for Annotation, Visualization and Integrated Discovery website  on protein level. There were 105 nodes and 608 edges in the network Fig. . The aveP value (P < 0.05). It included 496 of biological progress, 100 of molecular function and 55 of cellular components. In biological progress, potential targets were mainly concentrated in response to nutrient levels, response to steroid hormone, response to oxidative stress and so on . A total of 148 pathways were obtained. The top 20 pathways in KEGG enrichment analysis was demonstrated according to P value , Banxia (Arum Ternatum Thunb), Renshen (Panax ginseng C. A. Mey.), Gancao (licorice), Huangqin (Scutellariae Radix), Shengjiang  and Dazao (Jujubae Fructus). To date, many studies have discovered that Chaihu and its prescription bring out positive therapeutic effects on NAFLD [As one of the most common liver diseases, the incidence of NAFLD is increasingly rising worldwide and its prevalence is approximately 25.24% in liver disease. Patients with NAFLD are more likely to suffer from cardiovascular and tumor diseases and have a lower survival rate compared with ordinary patients . Xiaochaon NAFLD , 20. Ginon NAFLD . In addion NAFLD , 23. In In the network of ingredient-target-disease, key ingredients with higher degree includes quercetin, kaempferol, stigmasterol and beta-sitosterol. Quercetin is a widely existing flavonoid in nature. A recent research showed that quercetin had anti-oxidant, anti-inflammatory and immunomodulatory effects in NAFLD treatment . A cliniThe target with the highest degree was PTGS2. The following targets were ESR1, NOS2, PPARG in the network of ingredient-target-disease. PTGS2 is an oxidative stress-related factor related to prostaglandin biosynthesis related to inflammation and mitosis. He found that down-regulation of PTGS2 mRNA was related to the repair of drug-induced liver injury in mice . ESR1 isThe target protein levels of AKT1, IL6, JUN, MAPK8 and STAT3 were excessively expressed in PPI network. This result indicated the series of targets of Xiaochaihu decoction treating NAFLD. An animal model research has proven that the decreased expression of AKT1 resulted in FoxO1 inhibition and further achieved the effect of ameliorating NAFLD in diabetic rat . AnotherDAVID database was used for conducting KEGG analysis and GO analysis. The result of GO indicated that potential targets were mainly enriched in response to nutrient levels in biological progress, cytokine receptor binding in molecular function and membrane raft in cellular components. In addition, the results of KEGG were directly related to the NAFLD pathway. We retrieved six pathways related to liver disease as core pathways from the top 20 pathways of KEGG enrichment analysis and divided them into three aspects: metabolism, oxidative stress and immunity. As shown in Fig. In this study, network pharmacology systematically uncovered the key targets, active ingredients and the crucial signaling pathways of Xiaochaihu decoction for the treatment of NAFLD. It also provided a direction and evidence for future researches. Nonetheless, there still exists the limitation in this study. The source, performance and dosage of traditional Chinese medicine in actual application are not considered in this study. This is whatever so important during research. Moreover, there are many other variable factors should be noticed including NAFLD models in different species. In addition, network pharmacology is based on the data in the existing literature. In other words, if the data in the existing literature is biased, the results of network pharmacology will be inaccurate, which is the limitation of prediction. The treatment of NAFLD by natural products is often achieved through multiple targets and multiple pathways. Therefore, more researches approaches are ought to carry out in the future to further reveal the mechanisms of well-known formula and the compounds derived from these for various liver diseases .In summary, Xiaochaihu decoction plays a key role in the treatment of NAFLD with multiple targets and pathways. Immunity regulation, metabolism regulation and oxidative stress regulation consist of the crucial regulation cores in this mechanism.


Inflammation and immunity play a causal role in the pathogenesis of pulmonary vascular remodelling and pulmonary arterial hypertension (PAH). However, the pathways and mechanisms by which inflammation and immunity contribute to pulmonary vascular remodelling remain unknown. RNA sequencing was used to analyse the transcriptome in control and rats injected with monocrotaline (MCT) for various weeks. Using the transcriptional profiling of MCT‐induced PAH coupled with bioinformatics analysis, we clustered the differentially expressed genes (DEGs) and chose the increased expression patterns associated with inflammatory and immune response. We found the enrichment of Toll‐like receptor (TLR) and Nod‐like receptor (NLR) pathways and identified NF‐κB‐mediated inflammatory and immune profiling in MCT‐induced PAH. Pathway‐based data integration and visualization showed the dysregulated TLR and NLR pathways, including increased expression of TLR2 and NLRP3, and their downstream molecules. Further analysis revealed that the activation of TLR and NLR pathways was associated with up‐regulation of damage‐associated molecular patterns (DAMPs) and RIPK3‐mediated necroptosis was involved in the generation of DAMPs in MCT‐induced PAH. Collectively, we identify RIPK3‐mediated necroptosis and its triggered TLR and NLR pathways in the progression of pulmonary vascular remodelling, thus providing novel insights into the mechanisms underlying inflammation and immunity in the pathogenesis of PAH. Patient survival of advanced PAH remains poor,It is widely accepted that inflammation and immunity are linked to pulmonary vascular remodelling in PAH.The monocrotaline (MCT) model of PAH was widely used for over 50 years.The transcriptomic change during the PAH progression was investigated by using microarray.The changes of inflammation and immunity and pulmonary vascular remodelling that occur during the PAH progression largely result from the changes in the transcriptome. Therefore, in the current study, we use the RNA‐seq data set and bioinformatics approach to carry out a further analysis of the transcriptome in MCT‐induced PAH, aiming to have a deeper understanding of inflammatory and immune mechanisms in pulmonary vascular remodelling.22.1All procedures have been conducted in accordance with the ARRIVE guidelines and were approved by the Laboratory Animal Welfare and Ethics Committee of Fujian Medical University . Sprague‐Dawley rats  were purchased from Shanghai SLACCAS Laboratory Animal Co., Ltd. (Certificate No. SCXK 2012‐0002). The rats were raised and housed in the animal room and received food and water ad libitum. PAH model in rats was induced by a single intraperitoneal injection of 40 mg/kg MCT (Sigma‐Aldrich) as described previously.2.2Total RNA was isolated from 50 mg lung tissues using 1 mL TRIzol reagent (Life Technology) following the manufacturer's instructions. RNA integrity and quality were assessed by gel electrophoresis, and its concentration and purity were determined by the Thermo Scientific NanoDropTM instruments. Total RNA with high quality was used for cDNA library preparation. Library preparation and RNA‐seq were performed on an Illumina HiSeq 2000 platform by Genergy Biotechnology (Shanghai) Co., Ltd. The generated raw sequences were processed through a series of steps: (a) removing the low quality reads and adapter sequences, (b) quality control using the FastQC software, (c) mapping the clean reads to rat reference genome using STAR software, (d) assembling transcripts using the software of StringTie and Cufflinks‐Cuffmerge, (e) calculation of transcripts abundance using FPKM, and (f) identification of DEGs using DESeq2 software.2.3https://software.broadinstitute.org/morpheus/). Gene Ontology (GO) enrichment analysis of clusters was performed by using DAVID (https://david.ncifcrf.gov/). KOBAS (http://kobas.cbi.pku.edu.cn/) was used for KEGG pathway enrichment analysis on the selected clusters. The overlapped inflammatory and immune genes in cluster 1 and cluster 3 of GO and KEGG pathway analysis were determined by Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html). The genes annotated in pathways of TLR, NLR and necroptosis could be retrieved in KEGG PATHWAY Database (https://www.kegg.jp/kegg/pathway.html) by using the keywords of TLR, NLR and necroptosis pathway. GeneMANIA (http://bioinformatics.sdstate.edu/idep/) was used for analysis of the interaction among the overlapped genes. The modified Pathview (https://pathview.uncc.edu/home) was used for data integration and visualization of gene expression change in TLR, NLR and necroptosis pathways. Pathview analysis of gene expression change was modified by showing P‐values change instead of showing expression value ratio change. The enrichment of highly represented transcription factors was conducted by Enrichr (http://amp.pharm.mssm.edu/Enrichr/). The significant enrichment terms were determined by combined score, owing to the combination of P‐value and z‐score.Bioinformatic analysis tools, including DAVID,2.4−ΔΔCT method and expressed as fold changes.Total RNA was isolated from lung tissues of control and rats injected with MCT for 4 weeks. First‐strand cDNA was synthesized by using the Transcriptor First Strand cDNA Synthesis Kit, according to the manufacturer's protocol. Real‐time PCR was performed in accordance with the manufacturer's instructions, as previously described.2.5P ≤ .05. Data were shown as mean ± SEM, and comparison of two conditions in Pathview was performed by using Morpheus software. More details of the statistical analysis were provided in the figure legends.Morpheus software and R package software were used for RNA‐seq data set analysis. The DEGs between control and MCT treatment were identified by DESeq2 package in R using a threshold of fold‐change ≥2 and 33.1Our previous study has showed the elevated markers of macrophage infiltration and inflammatory mediators, such as TNFα and IL‐6, with concomitantly increased pulmonary arterial remodelling parameters WT% and WA% in the progression of MCT‐induced PAH.3.2Further analysis of enriched inflammatory and immune genes in cluster 1 and cluster 3 using Venny 2.1 showed that a total of 70 and 41 genes were linked to inflammation and immunity, of which 23 and 15 were overlapped Figure . Hierarc3.3Due to having enrichment of TLR and NLR pathways in cluster 1 by KEGG pathway enrichment analysis, we then characterized the changes of TLR and NLR pathways in response to MCT treatment. The TLRs are specific families of pattern recognition receptors capable of detecting microbes and generating innate immunity. Upon recognizing specific structures of microorganisms, TLRs activate NF‐κB pathway, resulting in the alteration of effector mechanisms, including up‐regulation of TNFα, IL‐1β, IL‐6 and IL‐12.The NLRs were the inflammasomes consisted of an inflammasome sensor such as NLRP3 and AIM2, caspase‐1 and often an adaptor protein ASC. Upon inflammasomes assembly and subsequent caspase‐1 activation, their effector mechanisms were then triggered, including release of activated IL‐1β and IL‐18 and initiation of gasdermin D (GSDMD)–mediated pyroptosis.Visualization of global pathway change using Pathview showed that the gene expression changes in TLR and NLR pathways were not always synergistic. As an example, the reduced expression of IκBα, TRAF6 and TAB2 was identified in TLR pathway Figure . The red3.4et al), cyclophilin A (PPIA), peroxiredoxin 1(PRDX1) and heat shock proteins  in MCT‐induced PAH , and a series of DAMPs and related receptors have been identified by previous studies Tables . In our H Figure . In addiH Figure . NotablyH Figure .3.5Receptor interacting protein kinase‐3 (RIPK3)–mediated necroptosis, a regulated necrosis, is recognized to involve in exposure or generation of DAMPs and then elicits inflammation.4In the present study, RNA‐seq and bioinformatics methods were used to identify the pathways related to inflammation and immunity in pulmonary vascular remodelling in PAH. We found dysregulated TLR, NLR and necroptosis pathways and a series of elevated DAMPs, as well as an inflammatory and immune profiling that could lead to pulmonary vascular remodelling in MCT‐induced PAH.It was well established that IL‐1β, IL‐6 and TNF‐α were associated with pulmonary vascular remodelling in PAH.The innate immune system is an evolutionally conserved host defence mechanism against pathogens and innate immune responses are initiated by pattern recognition receptors.TLR4 may also be of relevance, due to up‐regulation of its coreceptor CD14 and LBP and downstream molecules. Furthermore, it was reported that genetic deletion of TLR4 attenuated chronic hypoxia‐induced pulmonary hypertension.The expression of IL‐1β, IL‐18, GSDMD and NLRP3 was elevated in NLR pathway, which was consistent with previously reported activation of NLRP3 inflammasome in PAH.Increased deposition of ECMs in pulmonary arterioles contributes to the progression of PAH, and both inhibition of synthesis and genetic deletion of ECMs reduce pulmonary arterial remodelling.It was assumed that up‐regulation of proteolytic enzymes would result in the degradation of the ECMs and consequently, increased release of ECM fragments into the peripheral circulation. Consistent with this notion, circulating degradation products of ECMs including collagens ,It is noteworthy that in normal physiological conditions, intracellular and extracellular DAMPs are segregated and cannot activate TLR and NLR pathways. Once cell necrosis, intracellular DAMPs would be passively released. By contrast, extracellular DAMPs are generated from the degradation products of ECMs by proteolytic enzymes.To our knowledge, this is the first time to reveal a role of RIPK3‐mediated necroptosis in the pathogenesis of PAH through bioinformatics analysis. Thus, it is likely that PAH also belongs to the necroptosis diseases. Accordingly, a model illustrating the role of necroptosis and its triggered TLR and NLR pathways in PAH is proposed in Figure In summary, we identify dysregulated TLR and NLR pathways in the progression of pulmonary vascular remodelling. RIPK3‐mediated necroptosis may be associated with exposure of DAMPs and consequent activation of TLR and NLR pathways. Thus, these results provide novel insights into the mechanisms underlying immunity and inflammation in PAH.The authors confirm that there are no conflicts of interest.Genfa Xiao: Conceptualization ; Data curation ; Formal analysis ; Investigation (lead); Methodology (lead); Writing‐original draft (lead); Writing‐review & editing . Wei Zhuang: Resources ; Writing‐review & editing (supporting). Tingjun Wang: Resources ; Writing‐review & editing (supporting). Guili Lian: Conceptualization (supporting); Investigation (supporting); Methodology (supporting); Writing‐review & editing (supporting). Li Luo: Investigation (supporting); Writing‐review & editing (supporting). Chaoyi Ye: Investigation (supporting); Writing‐review & editing (supporting). Huajun Wang: Supervision (supporting); Writing‐review & editing (supporting). Liangdi Xie: Conceptualization ; Funding acquisition (lead); Resources (lead); Supervision (lead); Writing‐review & editing .Supplementary MaterialClick here for additional data file.


As genome sequencing efforts are unveiling the genetic diversity of the biosphere with an unprecedented speed, there is a need to accurately describe the structural and functional properties of groups of extant species whose genomes have been sequenced, as well as their inferred ancestors, at any given taxonomic level of their phylogeny. Elaborate approaches for the reconstruction of ancestral states at the sequence level have been developed, subsequently augmented by methods based on gene content. While these approaches of sequence or gene-content reconstruction have been successfully deployed, there has been less progress on the explicit inference of functional properties of ancestral genomes, in terms of metabolic pathways and other cellular processes. Herein, we describe PathTrace, an efficient algorithm for parsimony-based reconstructions of the evolutionary history of individual metabolic pathways, pivotal representations of key functional modules of cellular function. The algorithm is implemented as a five-step process through which pathways are represented as fuzzy vectors, where each enzyme is associated with a taxonomic conservation value derived from the phylogenetic profile of its protein sequence. The method is evaluated with a selected benchmark set of pathways against collections of genome sequences from key data resources. By deploying a pangenome-driven approach for pathway sets, we demonstrate that the inferred patterns are largely insensitive to noise, as opposed to gene-content reconstruction methods. In addition, the resulting reconstructions are closely correlated with the evolutionary distance of the taxa under study, suggesting that a diligent selection of target pangenomes is essential for maintaining cohesiveness of the method and consistency of the inference, serving as an internal control for an arbitrary selection of queries. The PathTrace method is a first step towards the large-scale analysis of metabolic pathway evolution and our deeper understanding of functional relationships reflected in emerging pangenome collections. PathTrace module written in Perl and Java, and appropriately documented along with sample input data for experimentation by the community through the GitHub repository at https://github.com/CGU-CERTH/PathTrace under an MIT license. Query enzyme sequences, target pangenome ensembles and similarity search results are available on Figshare (https://figshare.com/); specific links are provided at the GitHub directory listed above.We provide the entire Metabolic pathways represent an elegant formulation of functional units for cell physiology, whose efficacy relies on the combined activities of enzymes catalysing a set of interconnected reactions. As in any phylogeny, pathways reflect patterns of evolutionary events, leading to stability, loss or gain – observed as intact sets, fragmented remnants or augmented decorations, of the corresponding enzyme groups, respectively. With multiple genomic sequences at hand, methods that allow the inference of ancestral states for genes or genomes have been developed. In order to bridge these two ultimate aspects of evolutionary change, i.e. gene-sequence evolution and genome phylogeny, the inference of ancestral metabolic pathways offers a fresh view that focuses on genome function and physiology, explaining how pathway presence or absence reflects the functional capabilities of particular species. In this work, we describe a parsimonious method based on the ancestral inference of gene content, using metabolic pathways and entire pangenomes, collections of genomic sequences across taxonomic groups. We provide examples of how pangenomes contribute to the accurate detection of pathways and how pathways are inherited, lost or gained across a phylogeny of distantly related taxa. Our approach forms a basis upon which metabolic pathways can be understood in the context of pangenome information, in an automated, highly scalable fashion for large-scale computations of ancestral inference.The investigation of evolutionary histories of characters in terms of structure and function lies at the heart of biological research . With thGeneTrace  or absence =p[TRUE(vj)]=TRUE, then p(u)=TRUE. Simply put, a parent node is assigned to pathway presence if the difference between the number of potential gains and losses is larger than a threshold value GAIN, and pathway presence is detected in at least two children subtrees.Given a parent node u and all respective k children nodes vi, then if ∃ vj : p(vj) ≠ p(vi), ∀ vi ∈ {, i=1.k}, vi, p(vi)=FALSE -|vj, p(vj)=TRUE >LOSS, then p(u)=FALSE. Simply put, a parent node is assigned to pathway absence if the difference between the number of potential gains and losses is larger than a threshold value LOSS.Given a parent node GeneTrace algorithm, the ancestral pathway reconstruction process is largely insensitive with regards to changes in the GAIN and LOSS thresholds, due to the composite and thus robust nature of pathway profiles.Finally, it is important to note that, in contrast to the BioPAX and visualization.► Step 5: output to PathTrace method is the output of the reconstruction information into a computer-readable and universal format. To this end, we have selected the BioPAX file format as the most suitable choice [The final step of the e choice .BioPAX core functionality with the addition of a flag within a BioPAX file, indicating the presence or absence of an entire pathway or of specific proteins of the pathway. The BioPAX ontology representation is OWL-based and, in this sense, pathways are represented as ‘Pathway’ xml-elements while proteins are represented as ‘ProteinReference’ xml-elements within a BioPAX file [BioPAX elements share some common attributes and also possess some additional ones, depending on the functionality of the biological entity they describe. However, the BioPAX L3 format release does not provide attributes that could designate via a flag the presence or absence of a BioPAX element in a BioPAX file. To this end, and in order to avoid ambiguity and potentially erroneous interpretation of the data within a BioPAX file, we decided to insert the presence/absence flag through a custom structured comment. The custom comment should start with a predefined prefix, as a sub-element of a ‘Pathway’ or a ‘ProteinReference’ element in a BioPAX file. Specifically, the prefix of the custom comment we have introduced is ‘$$custom comment$$:’ (without the quotes) and it is followed by the word ‘present’ or ‘absent’ (without any spaces) in case the protein/pathway is present or absent, respectively.We have extended the PAX file . Both ofProteinReference instance (namely ‘protRefId1’) is absent from a pathway then the corresponding ‘ProteinReference’ element in the BioPAX file will have the following form:For example, if a BioPAX functionality extension provides us with the possibility of both creating alternative versions of pathways where some or all of the nodes are missing, and additionally highlighting the presence or absence of entire pathways from specific organisms.Finally, the aforementioned Development and analysis were performed using both sequence and metabolic pathway information.EnsemblGenomes database , from which we retrieved a total of 937 529 protein sequences from 249 entries, organized into ten major collections . The ten collections provided by EnsemblGenomes, namely Mycobacterium, Streptococcus, Bacillus, Staphylococcus, Wolbachia, Buchnera, Escherichia/Shigella, Neisseria and Borrelia provide an extensive coverage of the domain Bacteria, while the collection for Pyrococcus represents an – arbitrarily chosen – representative from the domain Archaea, serving as an ougroup for phylogenetic tree construction , nine pathways were defined as queries  against BioPAX files from the BioCyc database (https://biocyc.org/). The selection of pathways was such that it can provide a wide coverage of representative pathways with multiple enzymes/reactions, including some of the best-characterized cases biochemically.Metabolic pathway data for target pathways were retrieved as Level 3 (L3) BlastP  [Unix, using a parameter set as follows , GeneTrace gain=5/loss=2.Similarity searches that are used to detect the number of homologues were performed using ry 2016)  for Unixlows see : CAST thrameters , blast ePathTrace was performed using Perl (version 5.24.0), coupled with the Tree::Simple module and other Unix utilities (such as awk).Software development for Mesquite platform was also used [compare-classes from the RSAT/NeAT suite [Evaluation for comparison purposes with existing tools as well as visual representation of the output, the lso used . The tooAT suite  was usedThe analysis presented in this work is fully documented, and the relevant input/output files are provided along with the application, to ensure reproducibility and optimal use by the community.PathTrace method and validate results through a number of experiments, we have performed the following analyses; (i) an exploration of the impact that target genome selection might have on the pathway reconstruction process, (ii) a validation of the fuzzy-pathway profile formulation, utilizing an alternative mapping using E.C. numbers, and (iii) an assessment of the sensitivity of the method regarding the GAIN and LOSS parameters. The latter involves a carefully crafted case study using several well-investigated metabolic pathways and the validation of some of the evolutionary scenarios through comparison with the Mesquite suite [To establish the te suite .One of the key issues in any ancestral reconstruction analysis can be the optimal selection of the target genomes, i.e. the selection of the subset of the current genomes against which the reconstruction is performed. The range of options is essentially as follows: (i) several target genomes of varying phylogenetic distance, (ii) a single target pangenome and (iii) several different target pangenomes.In the first case, the quasi-random choice of distinct genomes in the target set leads to a heterogeneous collection of elements with a broad range, potentially yielding a sharp contrast of phylogenetic profiling, which can be hard to evaluate . The revWe have learnt, through much experimentation with genome and pangenome ensembles and parameter choices, that an optimal contrast for the target genome set can be achieved through a combination of the previous options in their extremes, i.e. using several pangenomes . In thisPathTrace. A recommendation is to maintain these two metrics in the resulting homology matrices, as above.The above analysis demonstrates that the estimation of pathway presence or absence critically depends on an adequate selection of target genomes. This choice needs to reflect both the conservation of pathway elements in terms of phylogenetic profiles of the corresponding enzyme sequences  as well as the variation of those elements . We thus require that any set of genomes selected as a target collection needs to exhibit both high contrast (i.e. entropy) as well as high segmentation (i.e. gradient entropy) – quantified by those parameters, following basic principles from the field of pattern recognition . In our RSAT suite and specifically the ‘NeAT – compare-classes’ module [An alternative approach for the detection of presence or absence of a given pathway against a target genome list, is to utilize reaction information related to that pathway, i.e. the E.C. numbers of the participating enzymes . In orde’ module . This al’ module .E. coli DH10b can be seen  [Evidently the TCA cycle appears to be absent from Streptococcus (block 3) and Buchnera (block 7) and entirely from Borrelia (block 10) : in the . coli) , while a. coli) , includi. coli) .PathTrace allows us to capture the presence or absence of the enzyme/protein group across the target pathway with finer resolution than the RSAT approach.Thus, in two separate cases of complex pathway evolution, using the Leucine biosynthesis  and the In all, the homology matrix in both cases provides evidence of presence of specific enzymes in certain species/strain groups that are consistently detected in both approaches, namely PathTrace and RSAT, used as internal controls in this validation step. Again, these patterns are consistently observed for different choice of other target pathways and species collections.PathTrace in a real-world situation and a high-throughput mode, we have applied the entire process to a set of nine metabolic pathways across a larger collection of bacterial genomes. Pathway queries were selected from common metabolic routes with a sufficient number of enzymes, 86 in total, typically well-characterized .Deploying the structed , on the structed , with a structed . The reastructed  through Pyrococcus spp. – only enolase and pyruvate kinase have similarity to their E. coli homologues with >40 % sequence identity) while the remaining pathways appear at the root of the bacterial clade of the test set, implying that they are not detected as intact pathways in the Archaea ; we reason that this pathway has been lost in the entire pangenome of this clade. The Lysine II variant has been lost in this branch, as SpoV genes as well as DapL are not detectable. It should be noted that Buchnera synthesizes both leucine and lysine, through other pathway variants not used in this study [Case C: The loss of Leucine biosynthesis I in t others ; only hot others . Interest others , pointinis study . For Biois study , and cleis study  Fig. 9)BuchneraStreptococcus clade, with the exception of Methionine biosynthesis, which presents a more complex pattern (S. pneumoniae [S. pneumoniae and S. equinus), while all enzymes except IlvE (branched-chain aminotransferase) are missing from S. pyogenes. The Streptococcus pangenome apparently lacks the TCA cycle  as well as the Isoleucine (IlvM/IlvN missing) and Biotin I (BioC/BioH missing)/II (limited distribution of certain enzyme homologues across the pangenome) biosynthesis pathways [S. pneumoniae, while SpoV genes and DapF are missing (except in S. pneumoniae); all genes with the exception of a DapA homologue are missing from S. pyogenes, reflecting a genuine, established biological fact [PathTrace returns its results according to the generic threshold imposed, whereas in the Buchnera case the number of homologues is relatively restricted, in the case of Streptococcus it is evident that in certain cases the presence of a pathway is more difficult to establish, as there is a much wider variation in the number of enzyme homologues across strains and species.Case D: A similar group of undetectable pathways emerges for the  pattern . Indeed,umoniae . For Leupathways . For Lyscal fact  Fig. 9)StreptocoPathTrace for S. pyogenes SF370 and the Streptococcus pangenome are consistent with the absence of most enzymes in the former strain and their fragmented distribution across other strains [S. pneumoniae and S. agalactiae (Table S2). In the case of S. pneumoniae, while most enzymes have homologues in the case of Lysine I biosynthesis, Asd, ArgD, DapE and DapF (four out of nine instances) are missing; this might be due to the threshold imposed by the algorithm (Table S2). Similarly, S. pneumoniae lacks MetE in Methionine biosynthesis (one out of six instances), while S. pyogenes only possesses MetB and MetC enzymes. For the entire pangenome, a fragmented distribution of homologues is observed, with MetH for instance being partially absent , but potentially absent from P. abyssi and the pangenome – where it is known to be replaced by the AAA lysine biosynthesis pathway [Case F: Finally, in the case of the no. 5 in  is detec pathway . The preFASTA format in Data S1 and targets the specific databases restricted to the taxon  of interest.All the above results are available on the Figshare repository  and might be reproduced either in automated or interactive fashion, using as queries the sequences from the nine pathways documented in PathTrace are consistent with our knowledge of pathway evolution, supported by corroborating evidence from the literature. It also demonstrates the difficulties of establishing pathway presence or absence, in particular with a single threshold – an ongoing challenge in fields such as similarity searches and clustering [The detailed case study results for the assessment of ustering .PathTrace, namely the ancestral inference given a pathway profile, we deployed Mesquite, a suite for evolutionary inference [Mesquite is not capable to execute the early phases of the PathTrace protocol, but can be used for a parsimony-based inference of ancestral states. To this end, we used the fuzzy, real-value profiles of the nine selected pathways, and applied the maximum parsimony process of Mesquite using default parameters. The produced patterns closely follow the results of PathTrace as can be seen in To further compare the last element of nference . MesquitPathTrace pathway profiles capture succinctly the distribution of these patterns with reasonable accuracy, reflecting genuine biological knowledge supported by existing knowledge. While it has been difficult to interpret partial presence of pathways (i.e. a fraction of their enzyme sequences), the most challenging issue has been the assertion of absence for certain reactions/pathways, as negative results can rarely be established, or in fact reported in the literature.This final, complex section summarizes the presence/absence patterns of query pathways across the target genomes and their corresponding clades and demonstrates that the PathTrace is able to detect unambiguously the presence or absence of a pathway across a phylogeny. While the algorithm is based on parsimony and inspired by previous, similar implementations of phylogenetic profiling, it is evident that its performance is significantly increased when coherent, phylogenetically similar genome collections are used as targets, eliminating individual effects deriving from gene gain or loss. Indeed, following extensive experimentation, we have established the advantage of pangenome collections for a more accurate detection of pathway groups. It is evident that query pathways, represented as groups of genes, are less sensitive to parameters of parsimony-based algorithms for gain or loss [By extending the notion of a phylogenetic profile to a composite group represented by the corresponding enzyme profiles of a pathway against a target selection of genomes, and typically organized into a pangenome,  or loss ; howeverPathTrace with related tools into software pipelines and relevant resources [More sophisticated models using taxon-specific values and genome-size normalization  or dynamesources . Those dClick here for additional data file.


Compound Kushen injection (CKI) has been extensively used in treating breast cancer (BC). However, the molecular mechanism remains unclear. In this study, 16 active compounds of CKI were obtained from 3 articles for target prediction. Then, a compound-predicted target network and a compound-BC target network were conducted by Cytoscape 3.6.1. The gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the DAVID database. The binding energy between the key targets of CKI and the active compounds was studied by molecular docking. As a result, 16 active compounds of CKI were identified, corresponding to 285 putative targets. The key targets of CKI for BC are HSD11B1, DPP4, MMP9, CDK1, MMP2, PTGS2, and CA14. The function enrichment analysis obtained 13 GO entries and 6 KEGG pathways, including bladder cancer, cancer pathways, chemical carcinogenesis, estrogen signaling pathway, TNF signaling pathway, and leukocyte transendothelial migration. The result of molecular docking indicated that DPP4 had strong binding activity with matrine, alicyclic protein, and sophoridine, and MMP9 had strong binding activity with adenine and sophoridine. In conclusion, the therapeutic effect of CKI on BC is based on the overall pharmacological effect formed by the combined effects of multiple components, multiple targets, and multiple pathways. This study provides a theoretical basis for further experimental research in the future. Sophora flavescens and Smilax glabra, which is able to clear heat and promote diuresis, cool blood and detoxify, resolve stagnation, as well as relieve pain [Sophora flavescens alkaloids can play a role in regulating tumor cell proliferation, inducing tumor cell differentiation and apoptosis, and inhibiting tumor cell invasion and metastasis. It can also reduce tumor neovascularization and regulate body immunity [Although modern medicine has made great progress in cancer research, breast cancer (BC) remains an important health issue. BC is the most common cancer affecting women's health around the world, and its morbidity and mortality are expected to increase dramatically in the next few years . Currenteve pain . The cheeve pain . Sophoraimmunity . At presimmunity –18. Howeimmunity . Therefohttps://pubchem.ncbi.nlm.nih.gov), and putative targets were collected from the Search Tool for Interacting Chemicals , SuperPred (http://prediction.charite.de/), and Swiss Target Prediction (http://www.swisstargetprediction.ch/) databases and TCM Pharmacology Database and Analysis Platform  [There is a systematic and comprehensive search of published literature on the active compounds of CKI in CNKI, Wanfang, VIP database, and PubMed. The SMILES (Simplified Molecular Linear Input Specification) structures of the screened compounds were obtained using the PubChem database (msp.php) –23.http://db.idrblab.net/ttd/) [https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas  were applied to search differentially expressed genes of BC and then combined the obtained genes and removed duplicate data. The final results are the targets related to BC [“Breast cancer” was identified as a keyword to collect proteins related to BC in the TTD databases  . At the ed to BC , 26.The active compounds of CKI and the predicted targets were introduced into Cytoscape 3.6.1 to construct a compound-predicted target network. A compound-BC target network was set up by intersecting the predicted targets of the compounds with the targets related to BC. The network analyzer plug-in was used to analyze the key targets on the three key topological parameters of the network: degree, betweenness, and closeness. “Degree” refers to the number of connections between a node and other nodes in the network. “Betweenness” means the ratio of the shortest path through a node to the total number of paths through all nodes. “Closeness” shows the inverse of the sum of the distances of a node from other nodes . The valhttps://david.ncifcrf.gov/) platform was applied to perform GO functional enrichment analysis and KEGG pathway enrichment analysis on the potential key genes for anti-BC of the CKI obtained after topology analysis [In this study, the DAVID  was performed, and the PDB format files were downloaded [The SDF format files of compounds were downloaded from the PubChem database and converted into mol2 format files. Protein conformation screening on key targets in the RCSB PDB database  were embryo implantation (GO: 0007566), positive regulation of vascular smooth muscle cell proliferation (GO: 1904707), endodermal cell differentiation (GO: 0035987), proteolysis (GO: 0006508), collagen catabolic progress (GO: 0030574), extracellular matrix disassembly (GO: 0022617), ephrin receptor signaling pathway (GO: 0048013), response to hypoxia (GO: 0001666), and angiogenesis (GO: 0001525); items related to molecular function (MF) include serine-type endopeptidase activity (GO: 0004252), metallopeptidase activity (GO: 0008237), and metalloendopeptidase activity (GO: 0004222) related to MF; and the GO entry related to cell composition (CC) is proteinaceous extracellular matrix (GO: 0005578) . It suggKEGG enrichment analysis totally obtained 6 pathways, namely, bladder cancer (hsa05219), pathways in cancer (hsa05200), chemical carcinogenesis (hsa05204), estrogen signaling pathway (hsa04915), TNF signaling pathway (hsa04668), and leukocyte transendothelial migration (hsa04670) . These pThe structure of key targets and compound with highest value were introduced into AutoDockTools 1.5.6 for molecular docking Tables  and 3. IBC is a common malignant tumor that threatens women's health and lives. In clinical practice, patients are often treated with integration of TCM and western medicine. Although the effect of traditional Chinese medicine on tumor shrinkage is not as obvious as that of radiotherapy and chemotherapy, it has less toxic and side effects and high safety, which helps slow down the clinical condition and improve the quality of life of patients . A largeIn this study, the interpretation of the mechanism of CKI in the treatment of BC was performed by integrated target prediction, network construction, and molecular docking. First, 16 active compounds were found in CKI, and 285 potential targets were obtained by target prediction. It is reported that matrine alkaloids have analgesic, heart-strengthening, antiarrhythmic, antiviral, anti-inflammatory, antitumor, swelling and diuretic, immunosuppressive, antibacterial, and insecticidal effects . MatrineSecond, it was found that HSD11B1, DPP4, MMP9, CDK1, MMP2, PTGS2, and CA14 may be key targets through a comprehensive analysis of the compound-predicted target network and compound-BC target network. These targets participate in the regulation of cell cycle, cell proliferation, inflammation induction, and other related proteins. Heather-SF conducted a nested case-control study on the HSD11B1 gene region and found that the HSD11B1 gene region may contain BC risk alleles, and the common variation may be related to BC in postmenopausal white women . MMP2 anα and ERβ. It plays an opposite role in cell proliferation, apoptosis, and migration and has different effects on the occurrence and development of tumor by inducing different transcription reactions [α sign plays a key role in BC cell migration and its level has great potential to be prognostic cancer biomarkers [According to the GO functional enrichment analysis and KEGG pathway enrichment analysis results, the key targets of the active compound regulation of CKI are significantly enriched in a variety of biological processes, molecular functions, and cellular components, such as embryo implantation, positive regulation of vascular smooth muscle cell proliferation, endoderm cell differentiation, proteolysis, collagen catabolism, extracellular matrix breakdown, adrenergic receptor signaling pathway, hypoxia, angiogenesis, serine-type endopeptidase activity, metallopeptidase activity, metalloendopeptidase activity, and protein extracellular matrix. The main enriched pathways are cancer pathway, chemical carcinogenesis, estrogen signaling pathway, TNF signaling pathway, and leukocyte transendothelial migration. Chemical carcinogenesis refers to the characteristics of some cancer cells, such as nongenotoxic carcinogens exposed to the environment, which changes the signal transduction pathway and eventually leads to high variability, genomic instability, uncontrolled proliferation, and resistance to apoptosis . The esteactions . Tumor neactions . Studiesomarkers . The migIn summary, this study revealed the potential pharmacological mechanism of CKI in the treatment of BC at a system level, which may involve synergistic regulation of cell proliferation, apoptosis, cell migration, immune regulation, and inflammation induction. Besides, the present study provides clues to understand and evaluate the synergistic effect of TCM in the treatment of complex diseases. Considering that this research is mainly based on data analysis, further biological experiments are essential for verifying the result.


To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE).GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764.Four microarray datasets: A total of 54 EC samples from three EMs subtypes  and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment.By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis. Endometriosis (EMs), characterized by the growth of endometrium-type tissue outside the uterine cavity, is a common and usually chronic (long-term) inflammatory disorder, affecting 5–10% of women in their reproductive years . EMs is GSE7305 and GSE7307 of OE, E-MTAB-694 of PE, and GSE25628 of DIE to obtain common differentially expressed genes (DEGs) among three EMs subtypes along with specific DEGs in each subtype by comparing ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Then, the Kyoto Encyclopedia of Genes (KEGG), protein-protein interaction (PPI) network and validation analysis were performed to analyze these common and specific DEGs  and Array-Express (https://www.ebi.ac.uk/ar-rayexpress/). The keywords: ‘endometriosis’, ‘endometrium’, ‘tissue’, ‘homo sapiens’ or ‘human’ respectively with ‘ovarian’, ‘peritoneal’, ‘DIE’, ‘deep’ or ‘infiltrating’ were employed to mine the datasets for three EMs subtypes. Additionally, all selected datasets were based on Affymetrix platforms to reduce the ‘platform effect’ due to different probe designs among different companies. Finally, four datasets were included: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE . Since our analysis did not involve any experiment on humans or animals directly, ethical approval was not necessary.Besides, all EC and EU samples in  article ) and GSEGSE7305 and GSE7307 of subtype OE were combined into an integrated dataset, which was subsequently named the ‘merged OE dataset’. Then, CEL files of this ‘merged OE dataset’ as well as the individual PE dataset E-MTAB-694 and DIE dataset GSE25628 were read by ‘Affy’ R package (http://www.affymetrix.com/), and unmatched probes were abandoned.The raw data CEL files and group information of four datasets were downloaded from the GEO and Array-Express database. Firstly, the CEL files of  package , and fur package . Moreove package  was utilGSE7305 and GSE7307, the individual PE dataset E-MTAB-694 and DIE dataset GSE25628, respectively, with the cutoff criteria: —log2 fold change (FC)—>1 and p-value <0.05. Additionally, the intersection analysis was conducted among three sets of DEGs.After pretreatment, R package limma  was applGSE7305 and GSE7307, the individual PE dataset E-MTAB-694 and DIE dataset GSE25628 was performed by DAVID (http://david.abcc.ncifcrf.gov/), an online tool for comprehensive functional annotation of genes and proteins. Furthermore, to explore specific pathogenesis of these three subtypes, specific DEGs in each dataset were also conducted with KEGG pathway enrichment analyses by DAVID. A threshold of p-value <0.05 was utilized to filter all KEGG pathways, which were also ranked by the p-value.To explain common pathogenesis among three EMs subtypes , KEGG pathway enrichment analysis of shared DEGs among the merged OE dataset of GSE105764 from the GEO database, which contained 8 paired EC and EU tissue samples based on GPL20301 (Illumina HiSeq 4000)  . The raweq 4000)  to obtaihttp://string-db.org/; version 11.0) to explore their interactions at the protein level, with an interaction score >0.4 as the cutoff value. Afterward, the PPI network was visualized in software Cytoscape (version 3.6.1) and analyzed by the ‘Degree method’ in plugin Cytohubba to identify the top 5 hub nodes in the network.The identified common DEGs among three EMs subtypes were uploaded to the online database STRING  metabolism, vascular smooth muscle contraction, drug metabolism-cytochrome P450, complement and coagulation cascades, tyrosine metabolism, according to the  ranking .GSE7305 and GSE7307 were staphylococcus aureus infection, PI3K-Akt signaling pathway, ovarian steroidogenesis, Rap1 signaling pathway, and cell adhesion molecules (CAMs) , leukocyte trans-endothelial migration, regulation of lipolysis in adipocytes, and natural killer cell-mediated cytotoxicity  ; the toptoxicity ; 36 specoebiasis . FurtherGSE105764. The consistency results between microarray training datasets and the RNA-sequencing validation dataset showed that most of these genes were found to be significantly higher in EC tissue than EU endometrium, except for CYP2J2  was considered as a heterogeneous disease and its subtypes: ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE) were likely to possess different aetiologies, which might require different diagnostic markers as well as treatments . HoweverGSE7305 and GSE7307 of OE, E-MTAB-694 of PE, and GSE25628 of DIE. By analyzing DEGs between EU and EC samples, we found that 148 shared DEGs were common to three EMs subtypes, which were mainly involved in the pathways of arachidonic acid (AA) metabolism, vascular smooth muscle contraction, drug metabolism-cytochrome P450, complement and coagulation cascades, tyrosine metabolism revealed by KEGG enrichment analysis. Moreover, both the microarray training datasets and validation OE RNA-sequencing dataset showed that most of the genes in the top 1 enriched pathway—arachidonic acid (AA) metabolism pathway were activated in the EC tissues, except for CYP2J2.In this article, we conducted an integrative analysis of EMs mRNA microarray datasets belonging to three different subtypes: In the arachidonic acid (AA) metabolism pathway, under the action of bioactive enzymes, substrate AA was catalyzed to generate endogenous eicosanoids such as prostaglandins (PGs), leukotrienes (LTs) and epoxyeicosatrienoic acids (EETs), which acted as mediators of various inflammatory disorders , includiInterestingly, the 5 hub nodes RRM2, AURKB, DTL, CCNB1, CCNB2 identified from the PPI network constructed by148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in the ectopic lesions in our analysis while always up-regulated in cancer tissues , thus prMoreover, besides the common pathogenesis, we also explored the peculiar molecular signature in each subtype of EMs by the KEGG analysis of specific DEGs in different subtypes. For instance, most genes in the pathway of ovarian steroidogenesis were significantly up-regulated in EC in subtype OE while with no significant difference between EU and EC in the PE and DIE, which suggested ectopic tissue specificity and focal prominent aberrant hormonal environment in OE. Furthermore, the most up-regulated gene STAR in the pathway was proved to be correlated with the severity of OE and PE . That OEWe also noticed that both specific DEGs in OE and DIE enriched in ‘pathways in cancer’, though enriched genes were different: DIE was associated with the risk of malignant transformation by enriched genes COL4A3, COL4A6, RAD51, F2R, PTGER3, while OE mainly by those PI3K pathway-related genes, such as FN1, GNG2, KIT, PTGS2, PDGFRA, LPAR1, CCND1, ITGA6, FGFR3, VEGFA, PIK3R1, FGFR2, MET. However, PE was more likely related to dysregulated peritoneal immune and inflammatory microenvironment, indicated by its specific DEGs enriched pathways, such as maladjusted cytokine-cytokine receptor interaction, leukocyte trans-endothelial migration, and natural killer cell-mediated cytotoxicity. Whether PE could be regarded as more ‘superficial’ or ‘slight’ disease primarily affected by peritoneal fluid factors, than OE and DIE mainly influenced by blood or ovarian factors , still nGSE7305 were collected during either proliferative or secretory phase, while cycle phases of OE samples in GSE7307 were unknown. Comparing the gene expression in each subdivided cycle phase respectively would provide an improved perspective. Additionally, the contamination of normal tissue sometimes was unavoidable during the sampling process, which in a way would lead to potentially controversial results. For example, in our results, the significant overexpression of ovarian steroidogenesis related genes in OE while not in PE and DIE ectopic tissues may be explained by the intrinsic differences among subtypes and/or the mixture of normal ovarian tissues. This needs further laboratory investigation in the future.However, our analysis still has some limitations. Firstly, due to the lack of RNA-sequencing datasets for PE and DIE, validation analysis was only performed in subtype OE. Secondly, since the uterine endometrium undergoes molecular signature changes throughout the menstrual cycle , differeThrough integrated bioinformatics analysis, we found that inflammation, especially arachidonic acid (AA) metabolism-related inflammatory process was the most common pathogenesis of OE, PE, and DIE. Besides, a slow and controlled proliferation in ectopic lesions was commonplace in these three EMs subtypes. Meanwhile, abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. All these findings could deepen our understanding of the common and specific molecular events in different subtypes in endometriosis.10.7717/peerj.8730/supp-1Table S1Left to right: Patient I.D. (n=19), age in years (mean = 34.32 ± 7.6 years); stage of endometriosis peritoneal lesion according to the American Fertility Society [AFS] classification; menstrual cycle phase according to Materials and Methods in original article DOI: 10.1177/1933719112451147; tissues analyzed; .Click here for additional data file.10.7717/peerj.8730/supp-2Table S2Left to right: Patient I.D. (n=8), age in years (mean = 31.5 ± 5.2 years); stage of endometriosis deep infiltrating lesion according to the American Fertility Society [AFS] classification; menstrual cycle phase according to Materials and Methods in original article DOI: 10.1002/jcp.24358; tissues analyzed; .Click here for additional data file.10.7717/peerj.8730/supp-3Table S3Abbreviations: Log2 FC, Log2 fold change; OE, ovarian endometriosis.Click here for additional data file.10.7717/peerj.8730/supp-4Table S4Abbreviations: Log2 FC, Log2 fold change; OE, ovarian endometriosis.Click here for additional data file.10.7717/peerj.8730/supp-5Figure S1GSE7305 and GSE7307, the individual PE dataset E-MTAB-694 and DIE dataset GSE25628. Boxplots represent the mean ± interquartile range, with whiskers extending to the minimum and maximum value. The Abscissa axis represents samples; the vertical axis represents gene expression value; the black horizontal line represents the median of the gene expression value for each sample; the green boxes represent EU samples; the red boxes represent EC samples. OE, ovarian endometriosis; PE, peritoneal endometriosis; DIE, deep infiltrating endometriosis; EC, ectopic lesions; EU, eutopic endometrium.(A), (B), (C), Boxplot of gene expression values after normalization in the merged OE dataset of Click here for additional data file.


Venenum Bufonis (VB), also called Chan Su in China, has been extensively used as a traditional Chinese medicine (TCM) for treating heart failure (HF) since ancient time. However, the active components and the potential anti-HF mechanism of VB remain unclear. In the current study, the major absorbed components and metabolites of VB after oral administration in rats were first collected from literatures. A total of 17 prototypes and 25 metabolites were gathered. Next, a feasible network-based pharmacological approach was developed and employed to explore the therapeutic mechanism of VB on HF based on the collected constituents. In total, 158 main targets were screened out and considered as effective players in ameliorating HF. Then, the VB components–main HF putative targets–main pathways network was established, clarifying the underlying biological process of VB on HF. More importantly, the main hubs were found to be highly enriched in adrenergic signalling in cardio-myocytes. After verified by molecular docking studies, four key targets  and three potential active leading compounds  were identified, which may play critical roles in cardiac muscle contraction. This study demonstrated that the integrated strategy based on network pharmacology and molecular docking was helpful to uncover the synergistic mechanism of multiple constituents in TCM. HF affes for HF . Howevers for HF . TherefoVenenum Bufonis (VB) is the dried white secretion of the auricular and skin glands of Bufo bufo gargarizans Cantor or Bufo melanostictus Schneider  and were saved in mol format . This software could efficiently capture the FDA-approved drugs that have similar chemical structures to the components in TCM . We pickhttp://www.drugbank.ca/, version 5.1.1), which could provide detailed information on the drug targets and their links with human diseases , a constantly updated database of human genetic diseases and genes . This database could provide a global perspective of proteins and their functional interactions and associations  database . This software is an efficient open source bioinformatics tool for visualizing and analysing the complex biological networks . Three ihttp://david.abcc.ncifcrf.gov/home.jsp/, v6.8)  pathways analysis and Gene Ontology (GO) enrichment analysis were undertaken to explore the potential functions of the pivotal target proteins involved in the VB-mediated treatment of Hf , by use /, v6.8) . Relevanhttp://www.rcsb.org/pdb/home/home.do), which was then decorated by removing the ligands, adding hydrogen, removing water, optimizing and patching amino acids. The binding site was defined by the ligand atoms, and the radius range was automatically generated. After each compound was docked, the 10 best conformations were obtained . Finally, CDOCKER interaction energies (CIEs) were used to assess the binding affinities between the core targets and the corresponding compounds. And the conformation corresponding to the lowest CIE was selected as the most probable binding conformation. All parameters used in calculation were default except for explained . CDOCKER is a semi-flexible molecular docking analysis method based on the CHARMm force field, which can produce high-precision docking results, and it provides information on the interaction binding energy and ligand–receptor docking mode. The three-dimensional (3D) structures of the candidate compounds were generated using Chem3D Pro 12.0 and the crystallographic structures of the proteins encoded by the candidate target genes were obtained from the PDB database  were selected on the basis of P value.To elucidate the biological process (BP), molecular function (MF), cellular components (CC) of these major hubs involved in, GO enrichment analysis was conducted on the major hubs. As shown in P < 0.05). As shown in To deeply determine the function and systemic association of the main hubs, KEGG pathway enrichment analysis were conducted. The results indicated that these hubs were enriched in 101 significant pathways , mitogen-activated protein kinase 1 (MAPK1), protein kinase C alpha . As shown in β-adrenergic receptor (β-AR)–Gs–adenylate cyclase (AC) pathway  and AC (ADCY2 and ADCY5). The regulation of Ca2+ homeostasis is also important for the cardio-myocyte excitation and cardiac electrical activity (+/K+-ATPase, a ubiquitous membrane protein composed of two subunits denoted as α and β, is also a critical regulator in maintaining the balance of Ca2+ in cardio-myocytes (+/K+-ATPase (+/K+-ATPase. Inhibition of the Ca2+/PKC α/ERK1/2 signal pathway plays a significant role in attenuating the progression of heart failure (α and ERK, such as bufalin (Many pathways are involved in adrenergic signalling for the regulation of cardiac contractile function. Among them, the best described is the mechanism mediated by  pathway . Activattraction . Accordiactivity . Accordimyocytes . An incr+-ATPase , such as+-ATPase , cinobuf+-ATPase , marinob+-ATPase , arenobu+-ATPase , and hel+-ATPase . Therefo failure . Several bufalin , marinob bufalin  and cino bufalin .+/K +-ATPase has been demonstrated in many basic researches and clinical practices. The molecular docking results indicated that the representative compounds could connect with the active-site residues via various noncovalent interactions, including the hydrogen bonding, pi-alkyl, pi-anion and pi-cation, etc, which was valuable for understanding of the action mechanisms of VB. In addition, according to -CIE values, bufotalin, cinobufaginol and 19-oxo-bufalin showed the best performance and thus were considered as the potential active leading compounds of the corresponding targets. Further researches on other potential targets or pathways are required to validate the predicted results.Based on the data analysis above, the bufadienolides may be the main active components in VB, which exert anti-HF effects via synergistically acting on multiple targets in multiple pathways. Among them, the positive inotropic effect of VB produced through the inhibition of NaIn summary, the active components of VB and their synergistic mechanisms for alleviating HF were successfully unveiled by network pharmacology coupled with molecular docking approach. The adrenergic signaling involved in cardiac muscle contraction process was found to be mainly responsible for the anti-HF effect of VB in silico. Four core targets and their corresponding leading compounds were identified, which may provide valuable information for further experimental validations and drug discovery.10.7717/peerj.10107/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10107/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10107/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10107/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10107/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.10107/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.10107/supp-7Supplemental Information 7Click here for additional data file.


Qing Guang An Granule (QGAG), a Chinese patent medicine, has been used clinically to treat glaucoma for more than 20 years.  To explore the possible mechanism of treatment of QGAG in glaucoma by using network pharmacology and molecular docking in this study.  Active compounds and targets of each herb in QGAG were retrieved via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Glaucoma-related targets were acquired from OMIM and DisGeNET database. Key targets of QGAG against glaucoma were acquired by overlapping the above targets via the Venn diagram. Using the DAVID, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the key targets were performed. The docking process was performed using the AutoDock 4.2.6 and AutoDock Vina 1.1.2.  The 55 active compounds and 173 targets were obtained and constructed a compound-target network. The 20 key targets of QGAG in treating glaucoma were acquired, and these targets are involved in the apoptotic process, cellular response to hypoxia, negative regulation of cell growth, and ovarian follicle development. The main pathways are p53, HIF-1, PI3K-Akt, and neurotrophin signaling pathway.  QGAG may exert a protective effect by acting on the optic nerve at a molecular and systemic level. This study can provide a certain basis for future researches on exploring the QGAG in treating glaucoma and provide new ideas for developing new drugs. Glaucoma is the second leading cause of blindness in the world characterized by optic atrophy and visual field defects . It is eTraditional Chinese medicine (TCM) has been used to treat glaucoma for thousands of years. Shennong's Classic of Materia Medica (Shennong Bencao Jing), one of the four classics of TCM, has first recorded glaucoma. In TCM, glaucoma is considered as a “five wind cataract”, which is caused by emotional depression, qi movement stagnation, and liver fire flaming upward . In receAstragalus membranaceus , Plantago , Safflower , Poria cocos , Radix Paeoniae Rubra , Atractylodes macrocephala , Rehmannia glutinosa , and Earthworm . QGAG, first proposed by Professor Qinghua Peng, has been used clinically to treat glaucoma for more than 20 years. Huangqi (HQ) and Shengdihuang (SDH) benefit qi and nourish yin, Dilong (DL), Honghua (HH) and Chishao (CS) activate blood circulation, and Fuling (FL), Baizhu (BZ), and Cheqianzi (CQZ) remove dampness [β1 and Smad3 and reduced scar tissue hyperplasia [Qing Guang An Granule (QGAG), a Chinese patent medicine, consists of eight medicine herbs, including dampness . Previoudampness . The mecerplasia . HoweverNetwork pharmacology is an approach for exploring the potential pharmacological mechanism of TCM through constructing biological networks. TCM has the features of multi-ingredients and multitargets, while network pharmacology has the features of multipathways and multilevels. Therefore, the systematic evaluation method of network pharmacology has a good predictive effect in the study of TCM . Moleculhttp://tcmspw.com/tcmsp.php); duplicate compounds were removed. Oral bioavailability (OB) ≥ 30% and drug-likeness (DL) index ≥ 0.18 are considered as important pharmacokinetic parameters to filter active compounds [https://string-db.org/) to transform gene symbols.Active compounds of each herb in QGAG were retrieved via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  and DisGeNET database (http://www.disgenet.org/). The obtained targets were entered into STRING (https://string-db.org/) to build Protein-Protein Interaction (PPI) networks.Glaucoma-related targets were acquired from the OMIM database (http://bioinfogp.cnb.csic.es/ tools/venny/index.html).The key targets of QGAG against glaucoma were acquired by overlapping the above targets via Venn diagram , the GO enrichment analysis and KEGG pathway analysis of the key targets were performed to obtain the related functions and pathways.GO enrichment analysis is normally classified into cell component (CC), biological process (BP), and molecular functions (MF) categories . Using thttps://pubchem.ncbi.nlm.nih.gov/), and the receptor proteins of key targets were obtained from Protein Data Bank (http://www.rcsb.org/pdb). The receptor proteins removed ligands and water by using the protein visualization software PyMOL 2.3.4. The docking process was performed using the AutoDock 4.2.6 and AutoDock Vina 1.1.2. From the docking results, docked conformation with the best scoring was selected for further mapping analysis. PyMOL was performed to show the interactions between receptor protein and ligand.The three-dimensional structures of active compounds were retrieved from PubChem  was performed. The binding energies of these 9 active compounds with ADRB2 are −7.8, −7.9, −8.0, −7.6, −7.9, −9.2, −9.5, −7.5, and −7.9 kcal/mol, respectively. The more negative the binding energy, the more stable the compound binds to the target. The Stigmasterol binding with the ADRB2 showed the highest binding energy . The efficacy of QGAG has been verified through more than 20 years of clinical application . HoweverTCMSP database was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 herbal medicines registered in the Chinese pharmacopeia with 29,384 ingredients, 3,311 targets, and 837 related diseases . Glaucomβ-adrenergic receptor family genes, is widely distributed in eye tissue [β-adrenergic receptor antagonist that has been widely used as eye drops for the treatment of high intraocular pressure in patients with glaucoma for many years. The result of molecular docking has shown that the binding energies of selected active compounds with ADRB2 lower than −7 kcal/mol indicate that these active compounds might act directly on ADRB2 to treat glaucoma.In our study, there are 20 key targets of QGAG in treating glaucoma. RHO has a close relationship with glaucoma, and the inhibition of RHO expression can lower intraocular pressure, change retinal vasculature perfusion, and promote regeneration of the optic nerve . BCL ande tissue . TimololGO enrichment analysis and KEGG pathway mapping are efficient methods to analyze gene function. GO enrichment analysis found that the effects of QGAG in the treatment of glaucoma are mainly reflected in the apoptotic process, cellular response to hypoxia, negative regulation of cell growth, and ovarian follicle development. Enriched KEGG pathways were predominantly associated with some signaling pathways. The PI3K-Akt and P53 signaling pathway are related to apoptosis, and downregulating the expression of P53 can reduce ganglion cell apoptosis . A studyIn summary, we used the network pharmacology and molecular docking approach to illuminate the multiple underlying mechanisms of QGAG in glaucoma treatment. The results indicate that QGAG may exert a protective effect by acting on retinal ganglion cell and optic nerve at a molecular level. However, the results require further experimental verification. This study can provide a certain basis for future research on exploring the QGAG in treating glaucoma and provide new ideas for developing new drugs.


Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus. Owing to its complicated pathogenesis, no satisfactory treatment strategies for DN are available. Milkvetch Root is a common traditional Chinese medicine (TCM) and has been extensively used to treat DN in clinical practice in China for many years. However, due to the complexity of botanical ingredients, the exact pharmacological mechanism of Milkvetch Root in treating DN has not been completely elucidated. The aim of this study was to explore the active components and potential mechanism of Milkvetch Root by using a systems pharmacology approach. First, the components and targets of Milkvetch Root were analyzed by using the Traditional Chinese Medicine Systems Pharmacology database. We found the common targets of Milkvetch Root and DN constructed a protein-protein interaction (PPI) network using STRING and screened the key targets via topological analysis. Enrichment of Gene Ontology (GO) pathways and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed. Subsequently, major hubs were identified and imported to the Database for Annotation, Visualization and Integrated Discovery for pathway enrichment analysis. The binding activity and targets of the active components of Milkvetch Root were verified by using the molecular docking software SYBYL. Finally, we found 20 active components in Milkvetch Root. Moreover, the enrichment analysis of GO and KEGG pathways suggested that AGE-RAGE signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway might be the key pathways for the treatment of DN; more importantly, 10 putative targets of Milkvetch Root  were identified to be of great significance in regulating these biological processes and pathways. This study provides an important scientific basis for further elucidating the mechanism of Milkvetch Root in treating DN. According to an epidemiological survey, the number of Chinese adults of ages over 18 who suffered from diabetes mellitus (DM) was 10.4% in 2013, ranking the first in the world . The earTraditional Chinese medicine (TCM) has been used to treat various diseases for thousands of years. TCM also has unique advantages in diabetes and is widely used in clinical practice in China , 10. MilNetwork pharmacology, which is based on the interaction network of diseases, genes, target proteins, and drugs, is a systematic analytical method . In receIn summary, we used network pharmacology to analyze the active ingredients, drug targets, and key pathways of Milkvetch Root to treat DN. This study aimed to further elucidate the mechanism of Milkvetch Root in treating DN and present new ideas and theoretical basis. The workflow of the network pharmacology approach used in the present study is illustrated in http://lsp.nwu.edu.cn/tcmsp.php)  chromen-3-ol had 33, 28, 25, and 19 corresponding targets, respectively. The corresponding targets of the active ingredients include AKT1, VEGFA, IL-6, PPARG, and NOS3. Moreover, molecular docking showed that the binding strength of 16 active components of Milkvetch Root to their target proteins was as follows: PPARG > VEGFA > IL-6 > AKT1> NOS3. We infer that quercetin has good interactions with NOS3, VEGFA, and SERPINE1. Kaempferol has good interactions with PPARG and VEGFA. 7-O-Methylisomucronulatol has good interactions with IL-6, NOS3, and SERPINE1.γ (PPARγ) agonist, and PPARγ agonists have become common drugs in the treatment of diabetes and its complications [Quercetin, a flavonoid, is a potent antioxidant found in common medicinal herbs and possesses a wide spectrum of biologic activities . It alsoications . Kaempfeications . Studiesications . Formonoications . 7-O-Metications .κB signaling activity, decrease the production of inflammatory mediators, and attenuate oxidative stress in diabetic rats and glomerular mesangial cells (GMCs), thus reducing urinary albumin filtration and renal damage and improving renal pathological changes among other effects [Isorhamnetin can inhibit the NF- effects . This shIn addition, quercetin, kaempferol, formononetin, and isorhamnetin are all flavonoids. Studies on the mechanism of action have suggested that flavonoids can improve the metabolism of sugar and lipid, enhance insulin resistance, inhibit the activity of relevant glucose metabolic enzymes, and escape oxidative damage of DM , 47. We α), AKT2 (PKBβ), and AKT3 (PKBγ), which each have their own physiologic functions [The analysis of protein interaction showed that there was a correlation between AKT1, VEGFA, IL-6, PPARG, and NOS3. First of all, there are three isoforms of AKT: AKT1  . PreviouIL-6, in the pathogenesis of DN, is associated with insulin resistance. A study has suggested that IL-6 affects the dynamics of the extracellular matrix and may increase the glomerular basement membrane and endothelial permeability . Currentα, PPARβ, and PPARγ [PPARG is a transcription factor that is activated by ligands. Currently, it has three subtypes: PPARnd PPARγ . Some stnd PPARγ . Nitric nd PPARγ , 59. Thend PPARγ . The actnd PPARγ .α can stimulate the aggregation and adhesion of inflammatory cells, increase the permeability of small blood vessels, and damage the glomeruli through inflammatory reactions [In this study, molecular docking and network analyses showed that all protein-pathway pairs were distributed among oxidative stress, inflammation, metabolism, immune system, apoptosis, and multiple pathways. For instance, oxidative stress and inflammation prompted by hyperglycemia are key initiators that lead to renal damage and nephropathy , 63. AGEeactions .In this study, the mechanism of astragalus in treating DN was analyzed by using systems pharmacology approaches. We found six active ingredients that can directly affect diabetic nephropathy targets; we also found ten potential targets for the treatment of DN. We infer that the AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway in diabetic complications serve as the key points and principal pathways for DN treatment. Altogether, we systematically explored how Milkvetch Root may affect DN treatment. We found that Milkvetch Root has multiple targets and approaches for treating DN. Such data provide the basis for multi-ingredient synergies in future research.