Material and Methods. The active ingredients of Puerariae, Scutellariae, Coptis, and Glycyrrhiza were screened using the TCMSP platform with drug‐like properties (DL) ≥ 0.18 and oral availability (OB) ≥ 30%. To find the intersection genes and construct the TCM compound-disease regulatory network, the molecular targets were determined in the UniProt database and then compared with the UC disease differential genes with P value < 0.005 and ∣log2 (fold change) | >1 obtained in the GEO database. The intersection genes were subjected to protein-protein interaction (PPI) construction and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. After screening the key active ingredients and target genes, the AutoDock software was used for molecular docking, and the best binding target was selected for molecular docking to verify the binding activity.  Gegen Qinlian (GGQL) decoction is a common Chinese herbal compound for the treatment of ulcerative colitis (UC). In this study, we aimed to identify its molecular target and the mechanism involved in UC treatment by network pharmacology and molecular docking.  A total of 146 active compounds were screened, and quercetin, kaempferol, wogonin, and stigmasterol were identified as the active ingredients with the highest associated targets, and NOS2, PPARG, and MMP1 were the targets associated with the maximum number of active ingredients. Through topological analysis, 32 strongly associated proteins were found, of which EGFR, PPARG, ESR1, HSP90AA1, MYC, HSPA5, AR, AKT1, and RELA were predicted targets of the traditional Chinese medicine, and PPARG was also an intersection gene. It was speculated that these targets were the key to the use of GGQL in UC treatment. GO enrichment results showed significant enrichment of biological processes, such as oxygen levels, leukocyte migration, collagen metabolic processes, and nutritional coping. KEGG enrichment showed that genes were particularly enriched in the IL-17 signaling pathway, AGE-RAGE signaling pathway, toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, transcriptional deregulation in cancer, and other pathways. Molecular docking results showed that key components in GGQL had good potential to bind to the target genes MMP3, IL1B, NOS2, HMOX1, PPARG, and PLAU.  GGQL may play a role in the treatment of ulcerative colitis by anti-inflammation, antioxidation, and inhibition of cancer gene transcription. Ulcerative colitis (UC) is chronic nonspecific enteritis with typical clinical manifestations of abdominal pain, diarrhea, and mucopurulent bloody stools; it mostly manifests as superficial ulcers occurring in the rectum and sigmoid colon but can also spread to the proximal colon and even the entire colon, causing permanent fibrosis and tissue damage. Despite improvements in treatment prospects, the incidence of long-term colectomy has not decreased over the last 10 years, and achieving mucosal healing early may be strongly associated with a reduced risk of future colectomy [Treatise on Febrile and Miscellaneous Disease, and the monarch drug Pueraria has antipyretic and antidiarrheal properties; meanwhile, the subject drugs Scutellaria and Coptis have a function to eliminate dampness and heat and Glycyrrhiza can replenish qi.Gegen Qinlian (GGQL) decoction is from A meta-analysis including 2028 patients defined that GGQL could improve clinical symptoms and reduce an endoscopic severity index (UCEIS) and recurrence rate; further, it also led to fewer adverse events whether it was used alone or in combination with Western medicine [Network pharmacology combines pharmacology, bioinformatics, and several other sciences with system network analysis and explains the multicomponent and multitarget drug treatment mechanism from the direction of gene distribution, molecular function, and signaling pathways by constructing a network related to “disease-phenotype-gene-drug,” which is suitable for the study of traditional Chinese medicine (TCM) compounds.Puerariae, Scutellariae, Coptis, and Glycyrrhiza, were screened using the TCMSP platform (http://lsp.nwu.edu.cn/tcmsp.php) with drug‐like properties (DL) ≥ 0.18 and oral bioavailability (OB) ≥ 30% as conditions. The predicted targets of the screened compounds were acquired from the DrugBank (https://www.drugbank.ca/) database and verified literature. Meanwhile, the UniProt database (https://www.uniprot.org/) was used for comparison of target information and gene name standardization.The active ingredients of the GGQL herbs, including https://www.ncbi.nlm.nih.gov/geo/). The script was run in Strawberry Perl-5.30.2.1 (Perl) software, and the gene probe names were annotated as gene symbols and grouped. The “limma” package was installed in the Perl software, and the sample values were corrected and subjected to log2 (logFC) transformation. Samples with P value < 0.005 and ∣log2 (fold change) | >1 were screened and considered to have statistically significant differential genes. The gene volcano map of the samples was generated, and the top 20 genes with the most significant up- and downregulation were selected to draw the heat map.The genetic samples—Series: GSE38713—of UC patients and healthy people were obtained from the GEO database ; the “enrichKEGG” function was applied for KEGG enrichment analysis, and the database was the KEGG database (https://www.kegg.jp/). As for the parameters of the two functions, species was set to “has,” and the filter values  were set to 0.05. The first 20 enrichment results were visualized as a bar graph, and the KEGG regulatory network was generated by the Cytoscape 3.8.0 software.The R package including “colorspace,” “stringi,” and “ggplot2” was installed in software R 4.0.0, and a bioconductor package that includes “DOSE,” “clusterProfiler,” and “enrichplot” was used for GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The function “enrichGO” was used for GO enrichment analysis. The database was org. Hs .eg. db (DOI: 10.18129/https://www.rcsb.org), of which the 3D protein conformations with a crystal resolution of lower than 3 Å as determined by X crystal diffraction were acquired. The Mol2 format files of GGQL key active ingredients were downloaded from the TCMSP platform. The AutoDockTools 1.5.6 software was applied to process proteins as follows: separate proteins, add nonpolar hydrogen, calculate the Gasteiger charge, and assign the AD4 type, and set all the flexible bonds of small molecule ligands to be rotatable. According to the original ligand coordinates, the docking box was adjusted to include all protein structures. Meanwhile, the receptor protein was set to rigid docking, the genetic algorithm was selected, and the maximum number of evals was set as the medium. The docking results were obtained by running autogrid4 and autodock4, by which the binding energies were revealed. The partial diagram of molecular docking was then generated using the PyMol software.The target genes involved in the first 10 pathways of the KEGG enrichment results were searched in the PDB database   | >1, a total of 305 upregulated genes and 263 downregulated genes were obtained. As indicated by the gene volcano map  of a node is simply the number of edges it has. The higher the degree, the more central the node is. The betweenness centrality (BC) captures how much a given node is in between others. Specifically, it is the ratio of the number of the shortest paths passing through the node to the total number of the shortest paths in the network. DC and BC reflect the influence of the corresponding node in the entire network. They describe the topological centrality based on the connectivity and controllability of the network. The combination of DC and BC values has been confirmed to be effective for screening reliable important proteins . As showGO enrichment analysis illustrates gene function on three levels: biological process (BP), cellular component (CC), and molecular function (MF). BP mainly involves aspects of response to oxygen levels, leukocyte migration, collagen metabolic process, and response to nutrients. CC is mainly related to the extracellular matrix, collagen-containing extracellular matrix, fibrillar collagen trimer, and banded collagen fibril. MF is mainly involved in serine-type endopeptidase activity, serine-type peptidase activity, serine hydrolase activity, CXCR chemokine receptor binding, platelet-derived growth factor binding, and cytokine activity . AccordiMolecular docking is a technique that mimics the interaction between small ligand molecules and receptor protein macromolecules, and the binding energy between the two counterparts can be calculated to predict their affinity. A binding energy lower than 0 indicates that the two molecules combine spontaneously and that smaller binding energies lead to more stable conformations. Most ingredients in GGQL can bind well with target genes, among which stigmasterol, coptisine, and berberine have the best binding properties . The genPuerariae, Scutellariae, Coptis, and Glycyrrhiza) in GGQL and UC disease targets. Quercetin, kaempferol, and wogonin were identified as the active ingredients associated with the most targets. The results of molecular docking also verified that they have good binding properties with most target genes. Quercetin is a common flavonoid compound in nature. It is considered the most effective reactive oxygen species (ROS) scavenger and inhibits the production of several proinflammatory factors, such as TNF-α and NO. The antioxidant and anti-inflammatory advantages of quercetin in UC treatment have been confirmed by various in vivo and in vitro experiments [Scutellariae; it is quickly converted into metabolites, such as baicalin, after entering the bloodstream. Baicalin has been confirmed to significantly inhibit TLR4-induced increase in NF-κBp65 levels, reduce the activity of enzymes, such as MPO and COX-2, lower the production of factors, such as THF-α, IL-1β, IL-12, and IFN-γ, and regulate the Th17/Treg cell balance [κB signaling pathway. Meanwhile, it effectively enhances the expression of SIgA and lowers the expression of iNOS, MPO, and MDA [In this study, a network pharmacological analysis was conducted on the medicinal ingredients of the four TCMs  are the predicted targets of TCMs. PPARG is also an intersection gene, and these 9 targets were speculated to be the key targets of GGQL in the treatment of UC.κB pathway, particularly the gene RELA, which encodes NF-κB p65. It is suggested by the results that both the treatment mechanism of GGQL and the pathogenesis of UC are related to inflammatory regulation. A meta-analysis showed that in allelic and dominant models, the genetic polymorphisms of IL-17A and IL-17F may increase the risk of UC occurrence. In addition, IL-17 levels in serum are significantly associated with the severity of UC [κB induces cytokine expression and neutrophil aggregation. It is often regarded as a sign and the central pathway of inflammatory response, while being involved in cancer development through various pathways [κB signaling pathway, TNF-α and IL-1 activate toll-like receptors (TLRs), followed by the activation of the IκB kinase complex, which can phosphorylate IκBα [κB pathway activation [κB, indicating that GGQL plays its therapeutic role by inhibiting the inflammatory response mediated by the NF-κB signaling pathway. It has also been confirmed by other studies that baicalin and berberine exert inflammatory inhibition effects by downregulating the expression of proinflammatory cytokines  and inhibiting the NF-κBp65 signal transduction pathway [UC is an inflammatory disease related to intestinal immune recognition disorders. The KEGG enrichment results indicate four inflammation-related pathways: IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, toll-like receptor signaling pathway, and TNF signaling pathway. Meanwhile, further analysis revealed inflammation-related genes, such as IL1B, CXCL10, CXCL11, MMP9, MMP3, SPP1, NFKB1, IKBKG, and RELA. The first six genes are widely involved in IL-17, toll-like receptors, and TNF signaling pathways, while the latter three are participants in the NF-ty of UC . Tumor nty of UC . Anti-TNty of UC . NF-κB ipathways . In the ate IκBα . Advancetivation , 17. In  pathway , 19.κB, TNF-α, and AP-1). The inflammatory responses then lead to the generation of more ROS, forming a vicious circle between oxidative stress and inflammation [κB to exert anti-inflammatory effects. PPAR-γ has been proven to be a key receptor for 5-ASA to exert anti-inflammatory and antioxidant effects [γ messenger RNA in the colonic mucosa of UC patients is impaired, accompanied by enhanced expression of the corresponding inflammatory factors . Partial PPAR-γ agonists may be a new target for UC treatment [The excessive ROS in the intestinal mucosa can trigger inflammatory responses by inducing redox-sensitive signaling pathways and transcription factors (e.g., NF-ammation . The KEG effects . Relevanreatment . Accordireatment . NOS actreatment . DUOX2 preatment . In GGQLAs suggested by the KEGG enrichment results, GGQL may prevent the occurrence of cancer in the following ways: transcriptional misregulation in cancer and proteoglycans in cancer. Persistent inflammatory response and oxidative stress state can promote the mutation of cancer genes. The abnormal expressions of NOS2, DUOX2/DUOXA2, ESR1, EGFR, MYC, and AKT1, which are being discussed in this study, have been confirmed to be related to cancer –28. The α and IL-1β levels to take effect on the treatment of UC rats [κB activation along with several inflammatory cytokines such as TNF-α, IL-6, IL-1β, and IL-4 and NO [Many other scholars' studies are consistent with our findings. Xu et al. found that GGQL decoction could increase the SOD activity and decrease the MDA and iNOS activities along with the reduction in TNF- UC rats . Li et a4 and NO .This study shows that the mechanism of GGQL in UC treatment is related to its anti-inflammatory and antioxidative properties and inhibition of oncogene transcription. Meanwhile, the action targets are related to NOS2, PPARG, and MMP1. This study has not demonstrated the relationship between the mechanism of GGQL treatment and signaling pathways of relaxin, amoebiasis, and prostate cancer. Relevant studies on this are based on existing database information and lack experimental verification. Therefore, it is worthy of subsequent experimental analysis to confirm the reliability of the results.


A hiccup is myoclonus of a sudden involuntary contraction of the diaphragm. Hiccups have various causes, and brain stem stroke is one of the causes of central hiccups. Certain types of hiccups are caused by diseases that can be fatal. Therefore, it is beneficial for physicians to be familiar with the various cases of unusual hiccups. We report a case of hiccups triggered by urinary bladder filling in a brain stem stroke patient. To the best of our knowledge, previous reports have not described a similar case.We describe the case of a 54-year-old patient who had acute bilateral pontine hemorrhage. The patient had intermittent hiccups in the early stages of the stroke onset. The hiccups ceased by the administration of medication or stimulation of the pharyngeal or tracheal wall. Two months after the onset, the Foley catheter was removed to check if the patient could void the bladder voluntarily. Hiccups occurred whenever the bladder was filled with some amount of urine.Pontine hemorrhage, neurogenic bladder, and quadriplegia.When the hiccups occurred, the amount of urine in the bladder was checked using a transabdominal bladder ultrasonography scanner. After clean intermittent catheterization for bladder emptying, the hiccups subsided.The hiccups occurred 5 or 6 times a day, as often as the bladder was filling. He was unable to void the urine voluntarily for 5 days after the removal of the Foley catheter. Percutaneous suprapubic cystostomy was performed finally to remove the stimulation of bladder filling and the hiccups disappeared.Bladder filling is suspected to increase the sympathetic tone and cause a hiccup reflex. Bladder filling could be a factor triggering hiccups in pontine hemorrhage. To date, no report has discussed that bladder filling causes hiccups.Hiccup is usually a self-limited disorder; hence, many episodes of hiccups may subside spontaneously without any clinical significance. Most hiccups are brief self-limiting episodes of no clinical significance. The most frequent benign causes are gastric distension , a sudden change in temperature , drinking alcohol, excessive smoking, or psychogenic .The pathophysiology of hiccupping is poorly understood, but it may be mediated through a hiccup reflex arc. A reflex arc is involved with phrenic nerve, vagus nerve, and sympathetic pathways and central modulation are likely responsible for hiccups. Accordingly, any irritant, such as physical/chemical factors, inflammation, and neoplasia invading the arc leads to hiccups. The central causes of hiccups include stroke, space-occupying lesions, injury, and others. Among the types of stroke, brain stem stroke is the most common cause of hiccups. We present the case of a middle-aged man who had hiccups repeatedly every time his bladder was filling, after bilateral pontine hemorrhage. We performed a literature review of the previous cases and aimed to elucidate a possible mechanism for our case. The patient's guardian provided informed consent for participation in the study.2A 54-year-old man without any premedical history had sudden-onset left-sided weakness at home. A few minutes later, right-sided weakness also developed, and he could not speak. He was admitted to the emergency department in a stuporous state. The initial Glasgow coma scale score was 4 . The initial brain computed tomography scan revealed acute pontine hemorrhage with intraventricular hemorrhage at the fourth ventricle Fig. .In the intensive care unit, he remained in a semicoma, and central high fever and apnea developed, which could occur in pontine hemorrhage. A Foley catheter, Levin tube, and endotracheal tube were inserted. After 7 days in the hospital, he opened his eyes spontaneously but was not aware of his surroundings. Making eye contact was impossible, and he could not move his extremities at all. Hiccups occurred once every few days. In addition, the hiccups subsided after administration of metoclopramide HCl or stimulation of the pharyngeal and tracheal walls with a suction catheter.On the 70th day of hospitalization, the Foley catheter was removed to check if he could urinate voluntarily. Six hours after the removal, hiccups began again. Stimulation of the pharyngeal and tracheal walls was not successful to cease the hiccups. At that time, urine volume was assessed using a transabdominal ultrasonography bladder scan. The urine volume was 260 mL. Two hours later, although the urine volume increased up to 360 mL, he could not void voluntarily. Bladder emptying was performed through clean intermittent catheterization (CIC). Within a few minutes after the CIC, the hiccups that lasted for 2 hours spontaneously subsided. Ever since, whenever his bladder was filled with urine at a volume of as much as 175 to 350 mL, hiccups occurred. After the CIC, the hiccups were terminated. He could not void urine voluntarily for 5 days after the removal of the Foley catheter. Thus, the physician decided to reinsert a Foley catheter for continuous drainage of urine. Hiccups did not occur thereafter. In consultation with a urologist, percutaneous suprapubic cystostomy was the final decision to remove the stimulation of bladder filling. Three months after a stroke, suprapubic cystostomy was performed. He discharged from hospital 5 days after suprapubic cystostomy procedure. One and a half years after a stroke, motor power did not recover and urinary drainage maintained through the suprapubic cystostomy without hiccups.3 Hiccups are more common in central nervous system and gastrointestinal diseases. Persistent or intractable hiccup is a common disorder that can occur due to brain stem lesions. Keane's analysis revealed that 56% of his experience with central hiccups were secondary to lateral medullary infarcts. However, any ischemic insult to the brainstem or pons will place an individual at risk of developing intractable hiccups. If hiccups persist, aspiration, during episodes of regurgitation secondary to hiccups, and loss of airway control would occur. It is more fatal to stroke patients. Therefore, prompt and appropriate management is required.We report a case of a hiccup induced by bladder filling after pontine hemorrhage. Approximately 4000 hospital admissions due to hiccups are reported every year in the United States.,5,6 Brain stem infarction may cause disintegration of inhibitory connections with a higher central pathway, which could make hiccups persist.The production of hiccups is a complex mechanism that involves multiple neurotransmitters and anatomical structures within the central and peripheral nervous systems. The reflex arc has afferents from the phrenic, vagus, and sympathetic nerves (T6–T12) to the central processing area in the brain stem, with efferents in the motor fibers of the phrenic nerve to the diaphragm and accessory nerve to the intercostal musculature. In an intact central circuit, excitation of the PMC activates the descending pathways that control urethral sphincter relaxation and activation of the sacral parasympathetic outflow. This results in bladder contraction and increased intravesical pressure and urinary flow.Considering the bladder neurophysiology, connections between various brain areas and extensive tracts in the spinal cord including sympathetic, parasympathetic, and somatic systems are necessary for the neural control of micturition. In the brain, the pontine micturition center  is specific for the micturition center.During the bladder filling phase, the sympathetic afferent fibers are activated with somatic afferent fibers owing to the mechanical distension of the bladder wall. In this case, we postulated that the hiccups were caused by the irritative sympathetic fibers of the hiccup reflex arc that were full-blown at the end of the filling phase. If the PMC was intact and the bladder emptying phase was initiated, the parasympathetic tone would be activated, and the sympathetic tone would be turned off. As the PMC was injured due to the stroke, the sympathetic tone subsided, and in turn, the hiccups subsided after artificial bladder emptying.If hiccups in pontine stroke patients are persistent, physicians must check for intra-abdominal stimuli, especially bladder distension.Conceptualization: Jinmann Chon, Seung Ah Lee, Seung Don Yoo.Data curation: Jinmann Chon, Seung Ah Lee.Investigation: Seung Ah Lee.Methodology: Seung Ah Lee.Supervision: Seung Ah Lee.Writing – original draft: Jinmann Chon, Seung Ah Lee, Seung Don Yoo.Writing – review & editing: Jinmann Chon, Seung Ah Lee, Seung Don Yoo.Seung Ah Lee orcid: 0000-0002-3426-6259.


Shen Zhu San (SZS) was regarded as one of the basic prescriptions.  Since December 2019, coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 infection has emerged in Wuhan and rapidly spread throughout China and even to other countries. Combined therapy with modern medicine and traditional Chinese medicine has been proposed, in which  Network pharmacological approaches along with candidate compound screening, target prediction, target tissue location, protein-protein interaction network, gene ontology (GO), KEGG enrichment analyses, and gene microarray analyses were applied.  A total of 627 targets of the 116 active ingredients of SZS were identified. Targets in immune cells and tissues were much more abundant than those in other tissues. A total of 597 targets were enriched in the GO biological cellular process, while 153 signaling pathways were enriched according to the KEGG analysis. A total of 450 SARS-related targets were integrated and intersected with the targets of SZS to identify 40 common targets that were significantly enriched in five immune function aspects of the immune system process during GO analysis. Several inflammation-related pathways were found to be significantly enriched throughout the study.  The therapeutic mechanisms of the effects of SZS on COVID-19 potentially involve four effects: suppressing cytokine storms, protecting the pulmonary alveolar-capillary barrier, regulating the immune response, and mediating cell death and survival. In early December 2019, a series of pneumonia cases of unknown cause were first confirmed in Wuhan, Hubei Province, China . BecauseChinese scientists rapidly isolated and confirmed a novel type of coronavirus named SARS-CoV-2 by the International Committee on the Taxonomy of Viruses on February 11, 2020 , 7. Coro5thedition) issued by China's National Health Commission integrated therapy with traditional Chinese medicine (TCM) and modern medicine [Shen Zhu San  was regarded as one of the basic prescriptions for the treatment of COVID-19 [As there are no specific antiviral therapies for COVID-19 and the main treatments are supportive at present, the Proposed Diagnosis and Treatment , MF (gray bar), and CC (blue bar) . The maiet genes . There wYersinia infection, Epstein-Barr virus infection, C-type lectin receptor signaling pathway, toxoplasmosis, NOD-like receptor signaling pathway, influenza A, malaria, Measles, and inflammatory bowel disease. Beyond that, we noticed that several inflammation-related terms were also significantly enriched, such as the JAK-STAT signaling pathway, Th17 cell differentiation, NF-kappa B signaling pathway, RIG-I-like receptor signaling pathway, and HIF-1 signaling pathway  and SymMap (https://www.symmap.org/) [https://pubchem.ncbi.nlm.nih.gov/).The constituents of the compounds in SZS were retrieved from the online public databases Traditional Chinese Medicine Systems Pharmacology (TCMSP) (ap.org/) –98. Eachap.org/) , 100. Alhttp://www.lilab-ecust.cn/chemmapper/index.html), a web server for predicting potential drug targets based on the 3D structure similarity [https://www.uniprot.org/) database with “Homo sapiens” as the species [The validated targets, which were confirmed by experiments, of the active ingredients of SZS were obtained from the TCMSP database . Then, tmilarity –105. Themilarity –107. To  species .http://biogps.org/#goto=welcome). First, the mRNA expression patterns of each target gene were determined in 84 different organ tissues. Second, the average values for each gene were analyzed. Third, the genes were located in the relevant organ tissues where the mRNA expression level was higher than the mean. Finally, a target-organ location network was constructed using Cytoscape 3.7.2 (https://cytoscape.org/), a well-known tool for network pharmacology research, to visualize the biological pathways and intermolecular interaction networks, among others. Furthermore, this supplied a basic set of features for data integration, analysis, and visualization for complex network analysis [BioGPS is a database for querying and organizing gene annotation resources. It provides gene expression data for cells or tissues obtained by microarray analysis . The taranalysis .https://string-db.org/) to obtain the relevant information on protein interaction, for which the genes were determined as nodes and the interactions as lines in a network [Since the chances of proteins performing their assigned functions individually are small, proteins involved in the biochemical process in the same cell tend to form macromolecular complexes through interactions to perform biological functions. Therefore, the exploration of protein interactions and their interaction networks are vital to understanding cellular organization, bioprocesses, and functions. In order to better understand protein interactions systematically, the associated targets were inputted into Search Tool for the Retrieval of Interacting Genes/Proteins  ( network . STRING  network . To ensuhttp://www.geneontology.org/), the gene numbers were calculated for every term, and significantly enriched GO terms in targets compared to the genome background were defined by a hypergeometric test. The calculated p value was then subjected to FDR correction. In this study, GO terms with an FDR ≤0.01, as the threshold, and the data were collected by ClueGO and CluePedia (Cytoscape plugins) [Gene ontology (GO) is an international standardized gene functional classification system that offers a dynamically updated controlled vocabulary and strictly defined concepts to comprehensively describe the properties of genes and their products in any organism . GO has plugins) –113. Morplugins) –40.http://www.genome.jp/kegg/). The calculated p value was subjected to FDR correction by taking FDR ≤0.01 as a threshold and the data were collected by the ClueGO and CluePedia plugins [http://www.r-project.org) with several R packages, including clusterProfiler, org.Hs.eg.db, enrichplot, and ggplot2, was applied to draw graphs during the GO and pathway enrichment analyses, such as bar plots, bubble diagrams, and a KEGG pathway map [https://www.bioconductor.org/) [Genes usually interact with each other to play roles in certain biological functions. The pathway-based analysis helps to further understand gene biological functions. KEGG is the major public pathway-related database . Pathway plugins , 116. Mohway map . The R por.org/) .https://www.malacards.org/pages/info), a database of human genes and genetic disorders. The proteins acquired from MalaCards were used as hub proteins and submitted to STRING and Human Protein Reference Database (HPRD) (http://www.hprd.org/) to determine the proteins interacting with these hub proteins. HPRD is a resource for experimentally derived information about the human proteome including protein-protein interactions, posttranslational modifications, and tissue expression [http://www.ncbi.nlm.nih.gov/geo/), an international public repository that archives and freely distributes high-throughput gene expression and other functional genomic datasets. The GEO dataset numbered GSE1739 is comprised of gene expression data obtained using the GPL201 Affymetrix Human HG-Focus Target Array from 14 samples, including 10 peripheral blood samples from adult patients with SARS and 4 controls. The workflow has been wrapped into the R packages “limma” and “edgeR” to identify the differentially expressed genes (DEGs) between the case and control samples [p value <0.05 and |LogFC|>1.0 were regarded as DEGs. Meanwhile, the R package “pheatmap” was used to draw the heatmap plot and volcano plot, with the up- and downregulated genes presented.Genes related to SARS were derived from MalaCards , and edges indicate intermolecular interactions, namely, the connections between nodes.


Motivation: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computational advances, interpreting collected measurements and determining their biological role remains a challenge. Results: To interpret measurements, we present an inference-based approach, termed Probabilistic modeling for Untargeted Metabolomics Analysis (PUMA). Our approach captures metabolomics measurements and the biological network for the biological sample under study in a generative model and uses stochastic sampling to compute posterior probability distributions. PUMA predicts the likelihood of pathways being active, and then derives probabilistic annotations, which assign chemical identities to measurements. Unlike prior pathway analysis tools that analyze differentially active pathways, PUMA defines a pathway as active if the likelihood that the path generated the observed measurements is above a particular (user-defined) threshold. Due to the lack of “ground truth” metabolomics datasets, where all measurements are annotated and pathway activities are known, PUMA is validated on synthetic datasets that are designed to mimic cellular processes. PUMA, on average, outperforms pathway enrichment analysis by 8%. PUMA is applied to two case studies. PUMA suggests many biological meaningful pathways as active. Annotation results were in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures. Importantly, PUMA annotates many measurements, suggesting 23 chemical identities for metabolites that were previously only identified as isomers, and a significant number of additional putative annotations over spectral database lookups. For an experimentally validated 50-compound dataset, annotations using PUMA yielded 0.833 precision and 0.676 recall.  Metabolomics has emerged as the new ‘omics’, providing a readout of cellular activity that is most predictive of phenotype. Metabolomics, so far, has played a critical role in advancing applications spanning biomarker discovery , drug diMetabolite annotation concerns associating measured masses with their chemical identities. This problem is challenging, as a particular mass may be associated with multiple chemical formulas . There are several techniques for annotating measurements. Database lookups rely on comparing the measured spectral signature against experimentally-generated fragmentation patterns cataloged in reference spectral databases  score t, METLIN  and BioC, METLIN  exploit , METLIN .pathway enrichment analysis, concerns interpreting measurements within their biological context to study coordinated changes arising in response to cellular perturbations. Overrepresentation Analysis (ORA) tools  employ g., MESA . Pathwayg., MESA  and IMPag., MESA ). Metabog., MESA .We present a novel inference-based probabilistic approach, Probabilistic modeling for Untargeted Metabolomics Analysis (PUMA), for interpreting metabolomics measurements. One input to PUMA is the set of measurements that are already processed through metabolomics, e.g., MZmine , XCMS 2, CAMERA A small example is provided to illustrate challenges in mapping measurements to metabolites and pathways, and to show inference’s ability to address these issues. 3 is assigned to metabolite j5. Because j5 is a metabolite common to both Pathways 1 and 2, there is an uncertainty in assignment of the metabolite to the pathways: j5 can be the product of activity in either Pathway 1, Pathway 2, or both. The other uncertainty relates to assignment of masses to metabolites, when a mass can map to multiple metabolites , while, if w4 is associated with j7, then it clearly ought to contribute to the activity of Pathways 2 (and/or other pathways with which j7 is associated). Not all measurements contribute to these uncertainties. For example, measurement w5 is unique to metabolite j13. In turn, j13 is unique to Pathway 2. Some measurements (such as w5) clearly contribute more significantly than others (such as w3 and w4) in determining pathway activities.There are two types of uncertainties in interpreting measurements from untargeted metabolomics. One type of uncertainty relates to assignment of metabolites to pathways  of pathways are assumed active, and a random portion  of metabolites within each active pathway are generated. For each portion of active pathways and for each portion of active metabolites, 100 metabolomics datasets reflecting the masses of the active metabolites were generated. The observed accuracy We compared the likelihood of pathway activities computed by PUMA against the enrichment ratios for the synthetic datasets. The enrichment ratio for a particular pathway is defined as the ratio of measured masses that map to metabolites within the pathway to its size. We use AUC, the area under the Receiver Operating Characteristic (ROC) curve to report the results. A ROC curve plots TPR (True Positive Rate) vs. FPR  at different classification thresholds. The AUC considers the performance of a classifier across all possible classification thresholds. Lowering the threshold for classifying a pathway as active results in classifying more pathways as active, thus increasing both false positives and true positives. The AUC effectively reports on the probability that the method ranks an active pathway that is selected randomly more highly than an inactive pathway that is selected randomly. The ROC curves are plotted for PUMA and for enrichment ratios . The AUCWe then applied PUMA to each dataset and averaged PUMA’s precision, recall and accuracy on identifying the presumed active pathways. At a pathway activity of 0.3 , as we hWe investigate how uncertainty in metabolite annotation impacts inference regarding pathway activity. Before running PUMA, each mass measurement is attributed to a presumed active metabolite, thus removing annotation uncertainty. Results  show a sWe further investigated the robustness of the model to its parameters. While prior runs assumed that the probability of observing a metabolite due to a particular pathway activity was 0.5, we varied the corresponding model parameter  results  show thacge. The model had 86 pathways, 1534 metabolites, and 722 unique mass measurements. The m/z of the precursor ions in the measurement datasets were adjusted based on the ionization mode by adding or subtracting the mass of one proton. Adjusted measurements ± a 15 ppm are then used to initialize the observation vector We apply PUMA to LC-MS (Liquid-Chromatography Mass Spectrometry) metabolomics data for CHO cell cultures belonging to a low growth cell line  Supplem. LC-MS dDetailed results for each dataset and for the combined data set is provided in Many of the 42 pathways identified active by PUMA are biologically relevant. The biological activity of most pathways such as TCA cycle, essential for energy metabolism, Biotin (vitamin B7) metabolism, amino acid synthesis, and many others, is expected. However, the activity of some pathways, including caffeine and drug pathways, is biologically unlikely active in the CHO cell samples. Based on our experiments using the synthetic datasets, we expect some PUMA predictions to be false.statistically enriched based on statistical significance of their ratios using Fisher’s Exact Test (FET). The null hypothesis is that there is no difference between the enrichment ratios of pathways in the sample. A p-value equal to or less than 0.05 is considered significant. Eight pathways are designated statistically enriched. These pathways are galactose metabolism, fatty acid degradation, purine metabolism, N-glycan biosynthesis, amino sugar and nucleotide sugar metabolism, glycosaminoglycan degradation, glycerophospholipid metabolism, lipoic acid metabolism. Among them, six pathways were predicted by PUMA to be active with probability equal to 1 while the N-Glycan biosynthesis pathway had a 0.53 likelihood of being active. Fatty acid degradation is predicted to be inactive. There were many pathways that had low enrichment ratios and low PUMA-predicted activity.Pathway activities predicted by PUMA are contrasted against pathway enrichment ratios . Pathway5 in our illustrative example While there was consensus in some cases, there were also differences. PUMA designates some pathways as active despite low enrichment ratios. For example, the enrichment ratios of the TCA cycle, fatty acid biosynthesis, ubiquinone and terpenoid-quinone biosynthesis are 0.15, 0.29, and 0.13, respectively. Meanwhile, PUMA predicted these pathways active with a likelihood of 1. There are three pathways with enrichment ratio equal to 0.5. Of them, one pathway, biotin metabolism, is assigned active by PUMA with probability 1.0. The biotin metabolism pathway has a measured mass that is unique and cannot be generated by other pathways. However, the other two pathways, both glycosphingolipid biosynthesis pathways, are predicted active with probability less than 0.5 (0.47 and 0.48). The reason was as follows: the observed mass measurements in the glycosphingolipid biosynthesis pathways could be mapped to galactose metabolism and glycosaminoglycan degradation pathways that are associated with a unique measurement that cannot be attributed to any other pathway in model  for each mass is considered as the spectral database candidate set.We assess the accuracy of PUMA annotations by comparing the level of agreement of PUMA annotations with those using two other techniques, spectral database searches and BioCAn . SpectraFor each measurement, the PUMA candidate set was compared against the spectral database candidate set. The comparison leads to four different scenarios: “agreement”, “semi-agreement”, “disagreement”, and “only PUMA”. An “agreement” scenario is where the PUMA candidate set exactly matches the spectral database candidate set. Such agreement occurs in 60 cases. A “semi-agreement” is when the spectral database candidate set is a subset of the PUMA candidate set. That is, unlike the “agreement” scenario, there is not complete consensus regarding the top candidate set, and hence the “semi-agreement” label. There are 15 cases of “semi-agreement”. A “disagreement” scenario is when the spectral database candidate set does not overlap with the PUMA candidate set. There were 10 such cases of “disagreement”. In 7 such cases, the spectral database candidate set is the second likely putative annotation identified by PUMA. These putative annotations, which were not part of the PUMA candidate set, had high PUMA activity scores that were close in value to scores of the metabolite(s) in the PUMA candidate set. In the remaining 3 cases, however, the spectral database candidate is assigned a low score by PUMA. Clearly PUMA and METLIN/HMDB disagree in regard to these three cases. An “only PUMA” scenario is when the spectral database candidate set was empty, but the PUMA candidate set was not. There were 326 such cases. This large number of “only PUMA” cases reflects the low coverage of spectral databases.PUMA annotations are compared against those obtained using BioCAn . BioCAn To validate annotations, BioCAn experimentally validated 50 of their predicted annotations against chemical standards. A subset of 37 compounds were confirmed present based on the standards. Thus, BioCAn’s precision on this 50-compound dataset is 0.740 (37/50). PUMA correctly calculated the likelihood for 33 of the 50 compounds in the sample: 25 were true positives, and 8 were true negatives. PUMA miscalculated the likelihood for 17 annotations: 5 were false positives and 12 were false negatives. PUMA’s precision for this 50-compound dataset is 0.833, thus achieving a significant additional 0.093 improvement in precision over BioCAn. Recall for PUMA for this 50-compound dataset was 0.676.In summary, comparing PUMA annotations against those obtained through spectral database and BioCAn shows significant levels of agreement. METLIN, HMDB and BioCAn incorporate spectra signatures during annotation while PUMA relies solely on pathway organization and mass measurements. Importantly, for the CHO cell, PUMA increased annotation by 383% over spectral databases and by 21% over BioCAn.J rows and K columns, maps metabolites in the model to their corresponding masses. We investigate if PUMA benefits from reducing the mapping uncertainty by adjusting the k to precisely a single metabolite j, which is identified based on the METLIN and HMDB annotations. Column entries other than k uniquely maps to metabolite As a measured mass may be attributed to more than one metabolite, there is uncertainty inherent in mapping measurements to metabolites. Matrix We apply PUMA to untargeted metabolomics datasets collected for human urinary samples analyzed by Roux et al. . DetailePUMA designated 41 pathways as active in human urinary sample . We invePUMA candidate set. We compared our annotation against those provided by Roux et al. [Roux candidate. When comparing the PUMA candidate set to the Roux candidate, there were four scenarios: “agreement”, “clarification”, “disagreement”, and “model incompleteness issues”. An “agreement” scenario is where the PUMA candidate set exactly matches the Roux candidate. Such agreement occurs in 85 cases. A “clarification” scenario is when the PUMA candidate set provided a specific chemical annotation while the Roux candidate annotated the measurement as an isomer. There were 23 cases of “clarification”. A “disagreement” scenario is when the Roux candidate was available in the model but was not predicted as match by PUMA. There was one case of “disagreement”. A “model incompleteness issues” scenario is when the Roux candidate was not in the model, reflecting that PUMA provides the best match within the scope of model metabolites. There were 14 “model incompleteness issue”. More comprehensive metabolic model could address such issues.The PUMA probabilities for each metabolite being present in the sample are used to score and rank metabolites. Only the top ranked metabolite(s) for each mass are considered as the x et al.   metabolites are not independent because they share the same set of hidden random variables. This relationship is evident in the graphical representation of the generative model . However


Potentilla freyniana Bornm. (P. freyniana), belonging to the family Rosaceae, has been used as a folk medicine in China. However, as we know, the constituents and the systematic elucidation of the mechanism were not fully investigated. Therefore, it is necessary to develop a rapid method using LC-MS and network pharmacology for the detection and identification of constituents and the systematic mechanism of P. freyniana. Firstly, the flavonoids were detected and identified based on ultra-high-performance liquid chromatography coupled with Quadrupole-Exactive Focus Orbitrap MS (UHPLC-Q-Exactive Orbitrap MS). After that, the potential targets of those constituents were obtained by database mining. Then, the core targets were predicted by protein-protein interaction network and network analysis. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out via DAVID. This finding revealed that P. freyniana possessed 43 flavonoids (40 of them were first reported) with 23 core target genes, which are associated with PI3K-Akt, MAPK, TNF signaling pathway, and pathway in cancer. This study demonstrated the multicompound, multitarget, and multimechanism of P. freyniana, which are very beneficial to develop the further study and utilization of this plant including the material basis and quality control research. Potentilla freyniana Bornm. (P. freyniana), a genus Potentilla of the family Rosaceae, named Difengzi, is a perennial plant with branched and tufted roots widely distributed and cultivated all-around of China, especially in Hunan, Hubei, Jiangxi. Their roots have been used as a folk medicine in clearing away heat and toxic materials for treating canker, bone tuberculosis, external bleeding iography . Thus, p− at m/z 463.088 (C21H19O12), 176.032 Da more than that of eriodictyol, suggesting they are eriodictyol-glucuronide, which were further identified by the presence of fragmentation ion at m/z 287.056 (C15H11O6). In a similar way, peaks 9, 28, and 30 were tentatively identified as taxifolin-glucuronide, quercetin-glucuronide, and quercetin-glucuronide, respectively.Peaks 4 and 26 were eluted at 4.13 and 11.18 min, respectively. All of them showed the same deprotonated ion [M−H]21H21O11). Peaks 19 and 34 possessed the fragment ions at m/z 167.034 (C8H7O4) and m/z 123.044 (C7H7O2), which are the diagnosis fragmentation ions of phloretin, suggesting they were phloretin derivatives. Thus, Peaks 19 and 34 were tentatively inferred as phloretin-glucuronide. Peaks 5, 7, 10, 16, and 22 yielded the same fragmentation ion at m/z 287.056 (C15H11O6), suggesting they were eriodictyol derivatives. The ion at m/z 287.056 was yielded by the neutral loss of 162.053 , suggesting the presence of glucose moiety. Therefore, they were tentatively characterized as eriodictyol-glucoside.Peaks 5, 7, 10, 16, 19, 22, and 34 were eluted at 4.19, 4.56, 5.88, 6.90, 7.99, 9.61, and 13.57 min, with the same deprotonated ion [M−H]− at m/z 449.109 (C27H29O15) was eluted at 4.41 min. It yielded fragment ions at m/z 353.0667 , 383.0774 , 473.1092, and 413.0874 , resulting from the loss of C4H8O4 + C4H8O4, C4H8O4 + C3H6O3, C4H8O4, and C3H6O3 + C3H6O3, respectively, suggesting the presence of two carbon-glucoside. According to the published paper [Peak 6 with the deprotonated ion [M−H]− at m/z 593.1535 , 162 Da more than that of naringenin (peak 38), suggesting they were naringenin-glucoside, which were further confirmed by the presence of m/z 271.061 and 151.003 in MS2 spectrum.Peaks 11, 14, 24, and 31 generated the same quasimolecular ion [M−H]− at m/z 433.114 (C27H29O17), 176.032 Da more than that of eriodictyol-glucoside, suggesting it is eriodictyol-glucoside-glucuronide, which was confirmed by the presence of the base peak at m/z 287.0558 (eriodictyol).Peak 12 eluted at 6.22 min and showed a pseudomolecular ion at m/z 625.1408  and generated the same fragment ions at m/z 269.044 (C15H9O5) by loss of the glucose moiety (C6H10O5), which suggested the presence of glucose moiety. The base peak at m/z 268.037 [Y0–H] ions in the MS2 spectrum of peaks 25 and 29 was a characteristic of apigenin aglycone. According to the published paper − at m/z 431.099  and exhibited the MS2 fragmentation ions at m/z 273.0771 , resulting from the loss of glucose moiety and pentoside moiety (294.096). Thus, peak 32 was tentatively characterized as phloretin-pentoside-glucoside. In a similar way, peak 23 was tentatively identified as naringenin-pentoside-glucoside.Peak 32 was detected at 13.39 min. It presented a pseudomolecular ion at m/z 567.1730  data. The PPI network with 206 nodes and 3980 edges was established by Cytoscape . A totalAmong the 23 key target genes and their interacting genes, it was found that 42.75 % had coexpression characteristics, 41.10 % displayed physical interactions characteristic. Other characteristics, including pathway, genetic interactions, colocalization, and shared protein domains, are displayed in p-value less than 0.05 , MAPK (10/23), TNF (9/23), ErbB (8/23), HIF-1 (8/23), Estrogen (8/23), FoxO (8/23), and cancer in the pathway. The top 20 KEGG pathways are shown in In order to further study the 23 core target genes, GO and KEGG pathway analysis were performed by DAVID. GO term enrichment analysis results were divided into the biological process , cell compound , and molecular function . A total of 158 BP, 16 CC, and 33 MF has a han 0.05 . In GO than 0.05  were enrBased on the target and KEGG pathway analysis, the entire compounds, targets, and pathway network were established by Cytoscape. The network with 122 nodes and 595 edges is shown in P. freyniana possessed 43 flavonoids (40 of them was first reported) with 23 core target genes, which were associated with PI3K-Akt, MAPK, TNF signaling pathway, and pathway in cancer. This study demonstrated the multicompound, multitarget, and multimechanism of P. freyniana, which are very beneficial for the further study and utilization of this plant including the material basis and quality control research.In the present investigation, this finding revealed that


Aims: The dysregulation and essential role of WNTs in glioma have been widely implicated. However, there is a paucity of literature on the expression status of all the 19 WNTs in glioma. Our study was aimed to evaluate the expression and prognostic values of the 19 WNTs in glioma. Methods: mRNA expression and clinical data were retrieved from the Cancer Genome Atlas (TCGA) database, Chinese Glioma Genome Atlas (CGGA), GTEx and ONCOMINE databases. The 50 frequent neighbor genes of WNT5A and WNT10B were shown with PPI network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Results: We found that the mRNA expression of WNT5A was significantly higher in glioma; however, the WNT10B expression was significantly lower in glioma. Furthermore, the expression of WNT5A and WNT10B was associated with the clinicopathology of glioma. The survival analysis revealed that the higher expressions of WNT5A and WNT16 were associated poor overall survival (OS) in patients with glioma. Conversely, overexpression of WNT3, WNT5B, and WNT10B was associated with better OS. Finally, Go and KEGG analysis revealed WNT5A was associated with multiple signal translations, and crucial oncogenes (EGFR and MDM2) and 2 important tumor suppressors (PTEN and IKN4a/ARF) were found closely correlated with WNT5A in glioma. Conclusion: Among 19WNTs, WNT5A can serve as a candidate to diagnose and therapy glioma, while WNT10B might be valuable for anti-glioma research. The presumed direction was provided to explore the relation of WNTs signal and multiple pathways in glioma. Glioma represents the most frequent primary intracranial neoplasms and is characterized by aggressive growth and poor prognosis . The WHOin vitro and in vivo [In the hundreds of previous studies, 8 members of 19 WNTs, WNT1, 2, 2B, 3A, 5A, 6, 7A and 7B, were showed being related with glioma development ,6–9. Now in vivo . Further in vivo . In addi in vivo ,14. WNT6Homo sapiens are numbered in the order of discovery: WNT1, 2, 2B, 3, 3A, 4, 5A, 5B, 6, 7A, 7B, 8A, 8B, 9A, 9B, 10A, 10B, 11 and 16 [2+ pathways. Accumulating evidences indicate that 11 members of WNT1, 2, 2B, 3, 3A, 7A, 7B, 9A, 9B, 10A and 10B are predominant ligands of canonical pathway [2+pathway and take part in the canonical pathway as well [Nineteen WNTs of 1 and 16 ,16. WNT 1 and 16 . Abnorma pathway ; WNT7A a pathway . WNT5A a as well ,27. Besi as well ,28,29. HIn the present study, we extensively used bioinformatic approaches to analyze the expression profiles and evaluate the prognostic significance of all the 19 members of the WNT family in glioma with publicly available data from TCGA, CGGA, CCLE and other databases. The PPI network, GO and KEGG analyses were performed to analyze the biofunction and molecular mechanism of several essential WNTs in glioma. Therefore, we could find out the most reliable prognostic markers and therapeutic strategies against glioma in 19 WNTs.www.oncomine.org) is currently the world’s largest online data-mining platform including data of over 20 cancers from TCGA and GEO databases [P-value was set up at 0.01.ONCOMINE (atabases . The trahttps://www.broadinstitute.org/ccle) database was used to retrieve the mRNA expression of WNTs in various cancer cell lines, and the data are verified and illustrated as box plots [The CCLE  were downloaded for our study. The CGGA database is a free used platform to investigate glioma from Chinese cohorts including over 600 glioma samples of patients. The mRNA microarray of 301 patients and matched clinical data were retrieved in the present study. The clinicopathological correlation and prognostic value of WNTs’ mRNA expression in glioma were accepted only when the results are statistically significant in both databases.In our study, the mRNA expression data of 163 GBM samples and 518 LGG samples were retrieved from TCGA database, and the mRNA expression data of 207 NB samples was retrieved form GTEx database. For analysis, we utilized GEPIA to compare mRNA expression of WNTs between glioma and NB tissues. Furthermore, we performed the survival analysis of 19 WNTs in patients with glioma .www.cbioportal.org) is an online open-access website to easily explore, visualize and analyze the multidimensional genomic data of cancers. Following the c-BioPortal’s online instruction, 50 frequent neighbor genes of WNT5A and WNT10B were determined as summarized in c-BioPortal (https://string-db.org/) was used to make PPI network of WNT5A/10B and 50 frequent neighbor genes. Then, Cytoscape was applied to examine the potential correlation between these genes. In addition, the MCODE app and BONGO app in Cytoscape were used to check modules of the PPI network .STRING (https://david.ncifcrf.gov/summary.jsp).GO analysis predicted the functions of WNT5A and 10B and in glioma on the basis of three aspects, including molecular function, biological processes and biological pathway. KEGG analysis showed the important role of WNT5A, visualized with the online website DAVID  followed by Dunnett test. The Kaplan–Meier method was used to plot survival curves and compared using the log-rank test.IMB SPSS 22.0 and GraphPad Prism 7.0 software were used to analyze statistical data and plot the graphs. All values were expressed as means ± standard deviations (SD). Homo sapiens. By analyzing the ONCOMINE database, the expression profiles of WNTs were analyzed across 20 cancers and compared with corresponding normal tissues. The higher level of WNT5A mRNA expression is elevated in most cancers. For CNS tumor, higher expression of WNT5A, lower expression of WNT7A and lower expression of WNT10B were found in glioma compared with NB tissues  was the most malignant of all glioma, the others were low grade glioma . We analyzed 19 WNTs expression in GBM and LGG compared with NB tissues using GEPIA website. The result suggested that only higher expression of WNT5A and lower expression of WNT10B were detected with statistical significance A. In mulThen, we analyzed the mRNA expression of WNTs in glioma cell lines by assembling the CCLE database. We expanded the procedure of detailed annotation in preclinical human glioma models. WNT5A was differentially higher expressed in glioma cell lines among 19 WNTs, implying that WNT5A might play distinct functions in glioma A. CCLE aTo investigate the prognostics significance of WNTs in glioma, we further analyzed mRNA expression data of WNTs with clinical data using Kaplan–Meier survival analysis. Higher mRNA levels of WNT5A and WNT16 were significantly associated with poor OS in patients with glioma A,B. ConvAnalyzing the mRNA data of 19 WNTs from TCGA and CGGA database, we found that WNT5A mRNA expression level was significantly correlated with the grade of glioma in both TCGA and CGGA databases A; howeveAs WNT5A and WNT10B were the most correlated with glioma prognosis and clinicopathology, we analyzed the 50 most frequently altered neighbor genes of WNT5A and WNT10B in glioma  and consThe GO enrichment analysis was then performed to determine the function of WNT5A and WNT10B with neighbor genes in glioma using DAVID. Go analysis predicted that WNT5A regulated molecular functions including GTPase activity, biological processes such as signal translation, and biological pathways such as EGFR-dependent signaling events in glioma A. In add2+pathway and the canonical pathway, however, WNT10B was only ligands of canonical pathway  and two important tumor suppressors (PTEN and IKN4a/ARF) were found to be closely correlated with WNT5A in glioma  and 2 important tumor suppressors (PTEN and IKN4a/ARF) were closely correlated with WNT5A.To understand the function and molecular mechanism of WNT5A and WNT10B in glioma, we performed PPI network, GO and KEGG analyses. First, the cluster of WNT5A was correlated with signaling pathway and the cluster of WNT10B was correlated with RNA polymerase II transcription mediator activity in PPI network. Then, GO analysis predicted that WNT5A regulated molecular functions including GTPase activity, biological processes such as signal translation, and biological pathways such as EGFR-dependent signaling events in glioma; differently, WNT10B regulated molecular functions including GTPase activity, biological processes such as signal translation, and biological pathways such as development biology in glioma. Finally, KEGG pathway analysis showed that WNT5A is primarily through the WNT/Ca+ pathway, also activates the canonical pathway ,48–50 anDespite the down-regulation of WNT10B expression in glioma, the most important molecule in the classical pathway, β-catenin is up-regulated in glioma and also reported accumulation in the nucleus of glioma ,56. ClasIn the present study, we also revealed that increased mRNA expressions of WNT16 was associated with shorter OS in patients with glioma, while the increased mRNA expressions of WNT3 and WNT5B were associated with longer OS. However, there was not enough evidence for their different expression between glioma and NB tissues.Although our study provided important insights into the prognostic and research values of WNT5A and WNT10B in glioma patients at the mRNA level, there still are some limitations to the present study. On the one hand, all the data analyzed in our study retrieved from the online databases were RNA expression data, thus, a larger quantity of protein data such as immunohistochemistry is required to validate our findings. On the other hand, though we performed analysis to predict the mechanism and biofunction of WNT5A and WNT10B in glioma, a remarkable amount of work is urgent to investigate the distinct role of WNTs in glioma.We systemically investigated the expression profiles and prognostic significance of all the 19 members of the WNT family in glioma. Our results indicated that mRNA expressions of WNT5A and WNT10B were significantly differentially regulated in glioma compared with NB tissues, and associated with pathology and grade of glioma. The study also indicated that WNT5A can serve as a candidate to diagnose and therapy glioma, while WNT10B might be valuable for anti-glioma research. Our data also provided presumed directions to explore interactions in canonical WNT pathway, non-canonical WNT pathway and multiple other signal pathways of interaction in glioma occurrence and development.Click here for additional data file.


Globally, more than 170 million people are infected with hepatitis C virus, a major cause of cirrhosis and hepatocellular carcinoma. The Yinchenhao Decoction (YCHD) is a classic formula comprising three herbal medicines. This decoction have long been used in China for clinically treating acute and chronic infectious hepatitis and other liver and gallbladder damp heat-accumulation disorders.In this study, we identified 32 active ingredients and 200 hepatitis C proteins and established a compound-predicted target network and a hepatitis C protein–protein interaction network by using Cytoscape 3.6.1. Then, we systematically analyzed the potential targets of the YCHD for the treatment of hepatitis C. Finally, molecular docking was applied to verify the key targets. In addition, we analyzed the mechanism of action of the predicted targets by the Kyoto Encyclopedia of Genes and Genomes and gene ontology analyses.This study adopted a network pharmacology approach, mainly comprising target prediction, network construction, module detection, functional enrichment analysis, and molecular docking to systematically investigate the mechanisms of action of the YCHD in hepatitis C. The targets of the YCHD in the treatment of hepatitis C mainly involved PIK3CG, CASP3, BCL2, CASP8, and MMP1. The module and pathway enrichment analyses showed that the YCHD had the potential to influence varieties of biological pathways, including the TNF signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, FoxO signaling pathway, and pathways in cancer, that play an important role in the pathogenesis of hepatitis C.The results of this study preliminarily verified the basic pharmacological effects and related mechanisms of the YCHD in the treatment of hepatitis C. Treatise on Exogenous Febrile Disease (Shanghan Lun), the Yinchenhao Decoction (YCHD) comprises Yinchen (Artemisiae Scopariae Herba), Zhizi (Gardeniae Fructus), and Dahuang (Rhei Radix et Rhizoma) and has found widespread application in the treatment of jaundice for more than 2000 years [In Asian countries, Traditional Chinese medicine (TCM), which holds an irreplaceable position, has a long history of application in the prevention and treatment of diseases . One amo00 years –5.Hepatitis C infection results in acute and chronic hepatitis. The hepatitis C virus (HCV) is an enveloped RNA virus that is spread as a sexually transmitted and blood-borne infection that infects humans only, and primarily targets liver cells. The HCV evades innate and adaptive immunity and establishes chronic infections in 70% of infected cases. Cirrhosis develops in 20% of untreated patients, and hepatocellular carcinoma (HCC) occurs in a fraction of these patients. Hepatitis C is a global health problem, and an estimated 170 million individuals are chronically infected with hepatitis C virus (HCV) –8. WorldNumerous studies on the treatment of viral hepatitis by the YCHD offer a good opportunity for effective data mining and network pharmacology research in this subject. Clinically, YCHD combined with IFN and ribavirin has shown significant clinical effects and a low incidence of adverse reactions in the treatment of patients with chronic HCV infection . Networkhttp://lsp.nwu.edu.cn/) – a unique system pharmacology platform devised for Chinese herbal medicines. All herbal compounds used in YCHD were retrieved on searches by using “Yinchen”, “Zhizi”, and “Dahuang” as the keywords. Oral bioavailability (OB) is an important indicator that determines whether oral drugs can overcome several obstacles, such as intestinal epithelium and wall, to reach the target site. Drugs with poor OB may show relatively low efficiency in entering the bloodstream and, thus, may have less beneficial therapeutic effects. To distinguish drugs from nondrugs, we adopted quantum mechanics-derived descriptors that depict the physical and partition properties of molecules to calculate the drug-likeness (DL) index; the DL index could help estimate the absorption, distribution, metabolism, and excretion (ADME) properties of the chemicals in the study drug [To collect the herbal compounds of YCHD, we utilized the Traditional Chinese Medicine Systems Pharmacology Database , 19 (TCMudy drug . Moleculhttps://db.idrblab.org/ttd/) – a database of the known and explored therapeutic protein and nucleic acid targets, target disease, pathway information, and the corresponding drugs directed at each of these targets; and (2) the Pharmacogenomics Knowledgebase [https://www.pharmgkb.org/) – a resource that collects, curates, and disseminates information about the impact of human genetic variations on drug responses.Details on the human genes associated with hepatitis C infection were acquired from two resources: (1) the Therapeutic Target Database  , which provides information on predicted and experimental protein interactions. The prediction method of this database derives from the experiments, databases, and text mining of the neighborhood, gene fusion, cooccurrence, and co-expression. Furthermore, the database defines PPI with confidence ranges for data scores . In the present study, PPIs with combined scores higher than 0.7 were selected for further research.The protein–protein interaction (PPI) data were obtained from the Search Tool for the Retrieval of Interacting Genes (STRING) database , 24 (httNetwork construction was done as follows: (1) the compound–compound target network was established by connecting the herbal compounds and their corresponding targets; (2) a PPI network of hepatitis C targets was constructed by linking the known hepatitis C-related targets and other human proteins that interacted with them; (3) a PPI network of targets for YCHD against hepatitis C was built by intersecting the two networks of (1) and (2); and (4) a drug–compound–target–pathway network was established.http://cytoscape.org/). Cytoscape is an open-source bioinformatics analysis software for constructing molecular interaction networks composed of proteins, genes, drugs, and other interactions for visual browsing and analysis. The topological features of interaction networks were evaluated by calculating three indices – degree, betweenness centrality, and closeness centrality – with a Cytoscape tool Network Analyzer [All the networks were visualized utilizing Cytoscape   – the US data center for the global PDB archive – serves thousands of Data Depositors in the Americas and Oceania and makes available 3D macromolecular structure data free of charge. SystemsDock [http://systemsdock.unit.oist.jp/iddp/home/index) is a web server for network pharmacology-based prediction and analysis, and it applies high-precision docking simulation and molecular pathway map to comprehensively characterize ligand selectivity and to illustrate how a ligand acts on a complex molecular network. The docking score of systemsDock is a negative logarithm of the experimental dissociation/inhibition constant (pKD/pKi), which can directly indicate the binding strength [https://pubchem.ncbi.nlm.nih.gov) contains validated chemical depiction information provided to describe the components in a PubChem Substance. The structures stored within PubChem Compounds are pre-clustered and cross-referenced by identity and similarity groups. The RCSB PDB database was used to retrieve and download the PDB ID of the potential target proteins, whereas the active compound was downloaded as a 2D structure file using the PubChem database. The abovementioned two files were jointly imported into the SystemsDock online platform for molecular docking verification.The Research Collaboratory for Structural Bioinformatics Protein Data Bank  (RCSB PDtemsDock  (http://strength . The Pubstrength  (https:/https://david.ncifcrf.gov). The David database integrates multiple types of database resources, uses the improved Fisher’s exact test algorithm to undertake enrichment analysis on gene sets, and provides P-value and false discovery rate (FDR) for enriched analysis results. GO enrichment analysis illustrates the role of target proteins of TCM compounds in gene function, including the functions of three modules of biological processes, molecular functions, and cellular components [The Cytoscape plugin Molecular Complex Detection (MCODE)  was applmponents . The KEGmponents . FinallyThe compounds were searched in the TCMSP and screened by OB of 30% or higher and DL of 0.18 or higher. In total, 44 compounds were searched from the YCHD; of these, 13 were from Yinchen, 15 from Zhizi, and 16 from Dahuang. 44 compounds were subjected to component target prediction and repeated component screening, and a total of 32 compounds and corresponding predicted targets were obtained. Additional file The details of the other 32 compounds in the YCHD are described in Fig. Additional file The combination of the YCHD compound–compound target network and the PPI network of hepatitis C can be used to obtain the potential target of the YCHD in the treatment of HCV infection. The principle of the connection between nodes is that when the predicted target of the active ingredient in the YCHD is the same as the target of HCV, the predicted target is linked to be a target of HCV. The same target is the potential target of active ingredients for the treatment of hepatitis C with the YCHD. By the Merge combined analysis, we obtained 12 targets: phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform (PIK3CG), caspase-3 (CASP3), apoptosis regulator Bcl-2 (BCL2), caspase-8 (CASP8), interstitial collagenase (MMP1), C-X-C motif chemokine 10 (CXCL10), RAF proto-oncogene serine/threonine-protein kinase (RAF1), interleukin-6 (IL-6), pro-epidermal growth factor (EGF), interleukin-10 (IL-10), Bcl-2-like protein 1 (BCL2L1), and epidermal growth factor receptor (EGFR). The above potential target data were uploaded into the STRING database for the PPI network , eight items to molecular function (MF), and four items to the cellular component (CC). The GO enrichment analysis of Module 2 obtained a total of 128 entries. With P < 0.01 and FDR < 0.01, we obtained 17 entries: 12 related to BP, four items to MF, and one entry related to the CC.The GO enrichment analysis of Module 1 obtained a total of 104 entries. With a P < 0.01 and FDR < 0.01; these mainly included the apoptotic pathway (hsa04210); TNF signaling pathway (hsa04668); p53 signaling pathway (hsa04115), etc. In Module 2, we screened 23 pathways according to P < 0.01 and FDR < 0.01; these mainly included the ErbB signaling pathway (hsa04012); glioma pathway (hsa05214); chronic myeloid leukemia pathway (hsa05220); endometrial cancer pathway (hsa05213), etc.In Module 1, we screened eight pathways according to Using the DAVID platform for GO functional enrichment analysis, we studied the role of 12 potential targets based on the combined results in gene function, and obtained 127 GO entries. Four GO entries were determined on the basis of the FDR  infection . The HCVBCL2 gene (Ala43Thr) single-nucleotide polymorphism and growth hormone (GH1) levels in Egyptian HCV genotype-4 patients, both before and after treatment with PEG IFN plus ribavirin. The results showed that patients with HCV genotype-4 with normal GH1 concentration and the BCL-2/43Ala genotype could successfully achieve a response to IFN therapy [The top five degrees of the potential targets for the YCHD in the treatment of hepatitis C include PIK3CG, CASP3, BCL2, CASP8, and MMP1. Apoptosis – a form of cell damage in various liver diseases – is worthy of study with regard to its role and significance in the pathogenesis and progression of viral hepatitis , 43. Stu therapy . We unde therapy . The ove therapy –55. AlthIn order to illustrate the role of the YCHD in the gene function and signaling pathway, this study undertook a GO functional enrichment and KEGG pathway analysis on the potential targets. The GO function enrichment analysis found that the YCHD is mainly reflected in biological processes, primarily in the negative regulation of the apoptotic process, active regulation of cell proliferation, response to glucocorticoids, and wound healing. In the enrichment of the KEGG pathway of the gene, 12 pathways were obtained; thus, we infer the YCHD is a treatment of hepatitis C through multicomponent action on these signaling pathways. HCV infection is the direct cause of CHC, and persistent inflammation can lead to liver fibrosis, sclerosis, and even liver cancer. Inflammation is usually associated with abnormal cellular signaling pathways. After viral infection, viral proteins interfere with the normal signal transduction pathways of host cells, causing abnormal expression of pro-inflammatory and inflammation-related molecules, leading to inflammation . Taking In the present study, we applied a network pharmacology approach to predict, elucidate, and confirm the potential mechanisms of the YCHD on hepatitis C by integrating target prediction, network construction, and module analysis. Firstly, a total of 200 targets affected by 32 bioactive compounds in the YCHD were obtained; these demonstrated a synergistic treatment strategy of TCM characterized by multicomponent, multitarget, and multipathway mechanisms. Secondly, the analysis of compound–compound target network and the PPI network of compound targets displayed that PIK3CG, CASP3, BCL2, CASP8, and MMP1 might be the key targets of the YCHD associated with hepatitis C. Thirdly, it can be seen by molecular docking that the active ingredient in the YCHD has strong binding activity to two potential targets and has good binding activity to most potential targets. The KEGG enrichment analysis of potential targets indicates that the therapeutic effect of the YCHD in hepatitis C may be related to various biological pathways; for example: pathways in cancer, PI3K-Akt signaling pathway, FoxO signaling pathway, tuberculosis, apoptosis, pancreatic cancer, prostate cancer, HIF-1 signaling pathway, TNF signaling pathway, toxoplasmosis, bladder cancer, etc. We confirmed the molecular mechanism of the YCHD in preventing and treating hepatitis C, providing the preliminary information and basis for further exploration of its mechanism of action; moreover, it provides a reference for studying the more complex mechanism of action of Chinese herbal compounds.Additional file 1:Table S1. Information on 44 active compounds of YCHD. Table S2. List of 39 proteins related to hepatitis C. Table S3. Module 1 GO enrichment entry. Table S4. Module 2 GO enrichment entry. Table S5. Module 1 KEGG enrichment entry. Table S6. Module 2 KEGG enrichment entry. Table S7. List of GO enrichment results to the YCHD potential target associated with hepatitis C. Table S8. List of pathway enrichment results to the YCHD potential target associated with hepatitis C


This study shows that RP probably acts by influencing the proliferation module, apoptosis module, inflammation module, and others when treating hypertension. This study provides novel insights for researchers to systematically explore the mechanism of action of TCM.Hypertension is a clinical cardiovascular syndrome characterized by elevated systemic arterial pressure with or without multiple cardiovascular risk factors. Radix Pueraria (RP) has the effects of anti-myocardial ischemia, anti-arrhythmia, vasodilatation, blood pressure reduction, anti-inflammation, and attenuating insulin resistance. Although RP can be effective for the treatment of hypertension, its active compounds, drug targets, and exact molecular mechanism are still unclear. In this study, systems pharmacology was used to analyze the active compounds, drug target genes, and key pathways of RP in the treatment of hypertension. Thirteen active compounds and related information on RP were obtained from the TCMSP database, and 140 overlapping genes related to hypertension and drugs were obtained from the GeneCards and OMIM databases. A PPI network and a traditional Chinese medicine (TCM) comprehensive network (Drug-Compounds-Genes-Disease network) were constructed, and 2,246 GO terms and 157 pathways were obtained by GO enrichment analysis and KEGG pathway enrichment analysis. Some important active compounds and targets were evaluated by  Hypertension is a clinical cardiovascular syndrome characterized by elevated systemic arterial pressure with or without multiple cardiovascular risk factors. It is also an important cause of and risk factor for a variety of cardiovascular and cerebrovascular diseases, affecting the structure and function of the heart, brain, kidney, and other important organs, eventually leading to organ failure .Traditional Chinese medicine (TCM) has been used in clinical treatment in China for more than 2,500 years . CompareAlthough RP can be effective for the treatment of hypertension, its active compounds, drug targets, and exact molecular mechanism are still unclear. Each formula consists of a variety of Chinese medicines, each containing dozens to hundreds of compounds. Due to a variety of compounds can play a role of synergistic treatment on multiple targets, so the analysis of the mechanism of the formula is a time consuming and resource intensive process. Fortunately, systems-pharmacology has become a recent academic field, which incorporates a variety of disciplines and technologies, including biological chemistry, physiology, genetics, and computer science system pharmacology, provides a holistic approach to explore and understand the nature of the traditional medicine and its formula. Systems-pharmacology is able to identify compound-compound, compound-target, and target-disease interactions in computer models and understand the effects of herbs on biological networks based on system theory. At present, there are many methods based on systematic pharmacology to study the effect of traditional Chinese medicine on hypertension. The study shows that the potential mechanism of the therapeutic effect of traditional Chinese medicine on hypertension can be predicted through systematic pharmacology, so as to verify its therapeutic potential. According to systems pharmacology studies, San Cao Decoction can lower hypertension through regulating the pathway of PI3K-Akt-eNOS. And Oryeong-san formula may suppress hypertension by controlling the renin-angiotensin-aldosterone system (RAAS) . By usinIn this study, systems pharmacology was used to analyze the active compounds, drug targets and key pathways of RP in the treatment of hypertension, as shown in http://tcmspw.com). TCMSP is a special systems pharmacology platform about Chinese traditional herbal medicines that includes the relationships between drugs, targets, and diseases. TCMSP has a wide range of 500 drugs and more than 12,000 compounds (All compounds contained in Radix Puerariae (RP) were searched from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  refers to the percentage of unmodified drugs that enter the circulatory system after oral administration . OB is ahttp://www.talete.mi.it/products/dragon_description.htm). β means the average molecular property for all drugs in the DrugBank database ; Based on the predictions of a receptor (molecular docking); Machine learning to predict ; and Combination forecast. But different technologies have their limitations. For example, the defect of molecular docking method is that it is difficult to obtain target proteins with three-dimensional structure through experimental means or homologous modeling. However, the modeling process of machine learning prediction is blind and implicit, so the binding pattern between target proteins and compounds cannot be found directly. The data set used in the training model must contain accurate annotation information, which means that the small molecule must have a clear correspondence with the target and the target naming needs to be standardized, so the common compound database is not applicable. In addition, such methods may only focus on the receptor space of some protein families or are limited to the compound space of specific drugs, which is not ideal for the prediction of drugs and targets that are not in this space. Considering the limitations of the experimental conditions of molecular docking and machine learning prediction and combining that with previous experience, we finally chose the prediction method based on ligands for subsequent research .http://lsp.nwu.edu.cn/tcmsp.php), removing redundant information and keeping only those that can interact directly with each of these compounds in RP as a presumption of targets. The target is then transformed using the UniProt knowledge database, and the selected species is Homo sapiens (Human). After deleting the redundant items, the data were merged to obtain the gene targets. UniProt is the most informative and well-resourced protein database. It is composed of data from three major databases: swiss-prot, TrEMBL, and pir-psd. The data come mainly from protein sequences obtained after the genome sequencing project was completed. It contains a wealth of information about the biological functions of proteins from the literature.All of the protein targets of the active compounds in RP were retrieved from TCMSP . Gene Cards is a searchable, comprehensive database that offers all comments and predicts human genetic information comprehensively in a user-friendly manner. It automatically integrates data from 150 web sources for the gene, including genome, transcriptome and proteome, genetics, and clinical and functional information   . It is aFirst, we intersected the obtained drug targets with the disease genes and obtained the Venn diagram of the intersected genes. Then, we built a network of complex information based on interactions between the drug (RP), compounds, genes and the disease (hypertension). Next, we used Cytoscape software   (Homo sapiens (Human). A PPI network of RP active compounds-targets and hypertension-related targets was then constructed.Protein-protein interaction data were obtained from the String database  . This ishttp://bioconductor.org/, version 3.8, released on October 31, 2018) for analysis.Gene Ontology (GO) is an international standard classification system for gene function. It is a standard of language vocabulary that can be used in all species. It can define and describe the functions of genes and proteins, and it can be regularly updated with new research. It can help researchers to focus on the biological functions of different genes from the perspective of gene function . This stIn vivo, different gene products coordinate with each other to perform biological functions, and pathway annotation analysis of differentially expressed genes helps further interpret gene functions. Overrepresentation of differentially expressed proteins on a given pathway shows the pathway enrichment analysis of differentially expressed proteins. This study also used the Bioconductor (R) software  for analysis.The KEGG (Kyoto Encyclopedia of Genes and Genomes) database is a database systematically analyzing the metabolic pathways of gene products in cells and the functions of these gene products. The database is useful for studying genes and expressed information as a whole network. KEGG integrates data from the genome, chemical molecules and biochemical systems, including metabolic pathways, drugs, diseases, and gene sequences . In vivowww.rcsb.org); the PDB entry code for these proteins is 1ALU, 2NVH, 2D48, and 5F19, respectively. During molecular docking, we adopted the GOLD Score fitness function.We wished to ascertain the interaction between active compounds and their protein targets and explore their binding modes. Hence, we selected three active compounds and four targets, a total of six compounds-targets interactions for verification of molecular docking. We used GOLD v5.1 . We obtained the X-ray crystal structures of IL-6, IL-1β, IL-4, and Prostaglandin G/H synthase 2 (PTGS2) from the RCSB Protein Data Bank (PDB) (Puerarin (purity ≥ 98%) and Daidzein (purity ≥ 98%) was purchased from Must Biotechnology . A stock solution of 100 mM puerarin or daidzein in dimethyl sulfoxide was prepared and stored at 4°C. Enzyme-linked immunosorbent assay (ELISA) kits for IL-6 and IL-4 were purchased from MultiSciences Biotech .RAW264.7 cells were obtained from the cell bank of the Chinese Academy of Sciences . RAW264.7 cells were cultured in Dulbecco’s modified Eagle’s medium  with 10% fetal bovine serum (Gibco). Cells were cultured at 37°C in an atmosphere of 5% CO2 for all experiments.4 cells/well in 96-well culture plates. After incubation for 24 h, Raw264.7 cells were exposed to puerarin or daidzein . After treatment for 24 h, 20 μl of Cell Counting Kit (CCK-8) assay solution  were added to each well, and cells were incubated for 4 h at 37°C in an atmosphere of 5% CO2. The absorbance at 450 nm was measured by a microplate reader. Cell survival was calculated as: absorbance/absorbance of control ×100%.RAW264.7 cells in the logarithmic phase were seeded at 8 × 106 cells/well) were incubated with lipopolysaccharide  for 24 h and then treated with daidzein  for 24 h. Supernatants were harvested and the level of IL-6 and IL-4 determined by ELISA kits (Biosharp).RAW264.7 cells (1×106 cells/well) were incubated with lipopolysaccharide  for 24 h and then treated with puerarin  for 24 h.RAW264.7 cells , and reverse-transcribed with oligo-DT using HiScript™ Reverse Transcriptase  according to manufacturer instructions. The primers used were synthesized by Genscript . The sequences were  5′-TTGGTCAGGTGAAGGGAGAC-3′ and 5′-GGATCACAGCCAGCTTTCAG-3′ for NF-κBIA; 5′-TTGCGTGAAGGCTTGAGATG-3′ and 5′-CTGGACAGGATGGAGGGTTT-3′ for BCL2; 5′-AAGCCTTCTCCAACCTCTCC-3′ and 5′-GCTGGGCAAAGAATGCAAAC-3′ for PTGS2; 5′-AACGGATTTGGCCGTATTGG-3′ and 5′-CATTCTCGGCCTTGACTGTG-3′ for the internal control glyceraldehyde 3-phosphate dehydrogenase (GAPDH).Total RNA was extracted with TRIzol−ΔΔCT method was the basis for relative expression of genes.qRT-PCR was done using SYBR™ Green Master Mix (Vazyme) in the QuantStudio 6 Flex system . The PCR cycling profile was: one cycle at 50°C for 2 min and 95°C for 10 min, 40 cycles at 95°C and 60°C for 30s. Fluorescence signals were detected using the QuantStudio 6 Flex system. Gene-expression data were normalized to that of the endogenous control GAPDH. The 2Data are the mean ± SD. The significance of results was determined based on one-way analysis of variance using Prism 8.0.1 . p < 0.05 was considered significant. All experiments were repeated at least three times.To identify the active compounds of RP, two classical ADME parameters, OB and DL, were used for screening. Some did not agree with the standard of screening compounds that are also likely to produce therapeutic effects on the human body. To study this issue more comprehensively, although they do not meet the screening criteria, the compounds were still retained as active compounds in this study. In the TCMSP database, there are 18 listed compounds of RP. Among them, only 4 compounds had an OB greater than 30.0% and a DL greater than 0.18. The DL of 16 compounds was greater than 0.18 and the OB of 6 compounds was greater than 30.0%. However, we found that puerarin, with an OB of 24.03%, is a well-known active compound in RP . These sSimilarly, the DL of allantoin is 0.03, which is far less than 0.18. However, studies have shown that allantoin has potential as a novel antihypertensive drug in the future . In addiAfter being collected from the TCMSP database and converted into the UniProt database, as well as deleting the redundant items, 13 active compounds in RP, and 183 known targets related to them were finally obtained. The details are described in Additional file: A total of 6,255 known therapeutic targets for hypertension were collected from the Gene Cards database. In addition, 11 known therapeutic targets for hypertension were obtained based on the OMIM database. After eliminating redundant targets, a total of 6,109 known therapeutic. Targets for hypertension were collected in this study. The details are described in Additional file: To clarify the potential mechanism of RP acting on hypertension, we used Cytoscape software to build the D-C-G-D network, as shown in We conducted further network analysis by evaluating centralization and heterogeneity. The network centralization and heterogeneity are 0.885 and 2.442 and are different from each other, showing that some nodes are more concentrated in the network than others. This means that the compound-target space has a tendency for certain compounds and targets. Therefore, the network includes some compounds with multiple targets, such as genistein (degree=66), daidzein (degree=49), puerarin (degree=45), formononetin (degree=26), beta-sitosterol (degree=25), and scoparone (degree=14). Additionally, it means that RP can act on multiple targets through the same active compound. For example, puerarin can have an effect on 45 targets, such as VEGFA, PTGS2, AR, PPARG, and RELA, when treating hypertension. Studies have shown that puerarin can achieve anti-inflammatory effects by activating the NF-κB signaling pathway and antioxidant effects by inhibiting the Nrf2 pathway . DetaileIn this study, we constructed a PPI network of the 140 overlapping targets, which consisted of 140 nodes and 2,076 edges, as shown in As seen in To verify whether the 140 genes are related to hypertension, we conducted GO enrichment analysis to clarify the relevant biological processes, as shown in The KEGG pathway enrichment analysis was performed by using the Bioconductor (R) software, as shown in According to the results, an integrated “RP pathway” has been constructed by the KEGG enrichment analysis, which can mainly be roughly divided into a proliferation module, apoptosis module, inflammation module, and others, as shown in In these modules, hypertension is thought to be associated with excessive endothelial cell proliferation . StudiesChronic low-grade inflammation is one of the many putative mechanisms for hypertension, with elevated levels of inflammatory cytokines and activation of the immune system . Some stVascular endothelial growth factor receptor (VEGFR) can regulate the cardiovascular system . The effIn general, the number and strength of a ligand bound to a receptor is determined largely by the inhibitory efficiency . TherefoPuerarin has special pharmacologic effects and high content in RP. Hence, we first conducted molecular docking of puerarin with PTGS2. We can see clearly from In In As shown in Due to that genistein has the highest degree, the molecular docking of genistein with PTGS2 was performed, with results displayed in In The results of molecular docking were also consistent with our cell-experiment results, which demonstrated that puerarin and daidzein had a significant anti-inflammatory effect.Based on these data, we can consider that the interaction between these active compounds and targets is the basis of their biologic activity. It also means that RP has multiple compounds and multiple targets.First, we determined the effects of different doses of puerarin or daidzein on the viability of RAW264.7 cells using the CCK-8 assay -stimulated RAW264.7 cells. We undertook ELISAs for IL-6 and IL-4 and qRT-PCR for NF-κBIA and BCL2 and PTGS2 to confirm the predicted anti-inflammatory effects of the compounds. The production of IL-6 was suppressed obviously with the increase of the concentration of daidzein, whereas IL-4 was generated more  and 157 pathways were obtained by GO enrichment analysis and KEGG pathway enrichment analysis. We thought that the PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, FoxO signaling pathway, JAK-STAT signaling pathway, VEGF signaling pathway, MAPK signaling pathway, AMPK signaling pathway, NF-kappa B signaling pathway, calcium signaling pathway, mTOR signaling pathway, PPAR signaling pathway, cGMP-PKG signaling pathway, p53 signaling pathway, and Ras signaling pathway might be principle pathways involved in the process of the treatment. The JAK-STAT cascade (GO:0007259) is associated with hypertension, and hypertension can be reduced by negative regulation of this cascade .in vitro cellular experiments and molecular docking to prove the validity of the prediction to some extent through verifying the inflammation pathway. Our results demonstrated that puerarin and daidzein from RP may have a therapeutic effect against hypertension, and the inhibition of inflammation would probably one of their approaches to produce anti-hypertension effect, especially through the IL-17 and NF-κB signaling pathway. In vitro studies and molecular-docking results also provided additional information for the screened compounds with potential anti-inflammatory effects, and validated the reliability of this screening strategy based on systems pharmacology. Meanwhile, there are a series of reported studies on the effects of puerarin on anti-hypertension through in vivo models, such as cats and SHR, and all suggests that puerarin exerts its anti-hypertensive effect, which is coincident with our research .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.