Ziziphus jujuba Mill.) in appetite regulation based on network pharmacology.  To investigate the mechanism of jujube  network was constructed. Important protein nodes and subnets were predicted based on the cytoHubba plug-in, and the hub gene was screened. Additionally, GO and KEGG pathway analyses were performed to obtain potential biological processes and signaling pathways of key targets. And the active ingredient-target-action pathway diagram was constructed.  A total of 16 active components were screened from jujube, including 131 action targets related to appetite and appetite regulation. Three key targets  were screened out by MCODE analysis. KEGG enrichment analysis was mainly enriched in neuroactive ligand-receptor interaction, serotonin-containing synapse, gap junction, cAMP signaling pathway, and dopaminergic synapse. Molecular docking results showed that the components coclaurine, (−)-catenin, (+)-stepholidine, berberine, cianidanol, coclaurine, and moupinamide in jujube had strong binding activity to the main targets .  Based on network pharmacology, the appetite modulating effects of jujube on multiple components, targets, and channels were explored, and the main active components of jujube were predicted to act on multiple signaling pathways to regulate appetite. The molecular docking results showed that the components in jujube had strong binding activity to the main targets, which provided new ideas and methods to further investigate the mechanisms of appetite regulation by jujube. Hyperphagia is one of the most common and intractable symptoms in diabetic patients and is an important culprit in disrupting glucose homeostasis . StudiesZiziphus jujuba Mill.) decoction can produce the symptoms of gastric fullness in patients and then achieve good results in appetite control without producing more obvious blood glucose fluctuations, but can also assist other glucose-lowering treatment options to maintain blood glucose homeostasis [α-glucosidase activity [Fatty and sweet foods can trap the spleen and stomach, which will lead to spleen deficiency for a long time and eventually become diabetic, manifesting as easy hunger. Supplementing spleen deficiency and responding to the sweet nature of the spleen is one of the main tools in the clinical treatment of hyperphagia in diabetes in Chinese medicine , 5. Spleeostasis , 5. Someeostasis , improvieostasis , inducineostasis , inhibitactivity , and sedactivity . HoweverNetwork pharmacology is a research method that uses high-throughput screening in databases, network visualization, and data analysis techniques to reveal the complex biological network relationships among drugs, targets, and diseases and to analyze and predict the pharmacological mechanisms of drugs . Moleculhttps://tcmspw.com/tcmsp.php) [https://www.megabionet.org/tcmid/) [https://pubchem.ncbi.nlm.nih.gov/) [The Chinese medicine name jujube was used to obtain drug composition information using the TCMSP (msp.php)  database/tcmid/)  databaseih.gov/)  for searADMET  is the ahttps://go.drugbank.com/) [https://db.idrblab.net/ttd/) [https://www.swisstargetprediction.ch/) [The predictive screening of the active ingredient target proteins of jujube was performed using the following two steps: the smile structures of the screened active ingredients were entered into DrugBank (nk.com/) , Therapeet/ttd/) , and Swiion.ch/)  to prediThe target prediction was carried out according to the method of Fu et al. , and allhttps://www.GeneCards.org/) [https://www.disgenet.org/) [https://www.omim.org) [https://ctdbase.org/)-human [The search term “Appetite Depressants/Appetite/Appetite Regulation” was set and the GeneCards database (ds.org/) , the Diset.org/) , the Themim.org)  were use/)-human  databasehttps://string-db.org/). The information of the constructed PPI network was imported into Cytoscape 3.8.2 software [STRING database  is a datsoftware , based osoftware  plug-in software  to find https://david.ncifcrf.gov/) [P < 0.05) to analyze the key targets involved in the relevant biological processes. Signaling pathways were analyzed and visualized using Cytoscape software.GO functional annotation analysis is a common approach to perform large-scale functional enrichment studies of genes, including biological process (BP), molecular function (MF), and cellular component (CC). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway is a widely used database for storing information about genomes, biological pathways, diseases, and drugs. The screened hub genes were imported into the DAVID 6.8 database (rf.gov/) , and theCytoscape 3.8.2 software  was usedhttps://pubchem.ncbi.nlm.nih.gov/) according to their PubChem_ID numbers and imported into Discovery Studio 2017 R2. The high-resolution crystal structures of the targets were downloaded from the PDB [https://www.rcsb.org/pdb/home/home.do) protein database, and the active sites of the proteins were centered on the active amino acid sites of the original ligand action labeled in the crystal structure itself, and the corresponding “active pockets” were constructed, so that the system searches for “active pockets” near the active site and finally locates the “active pocket” information to the target “active pocket.”Molecular docking was performed using CDOCK in the Receptor-Ligand International module of the software Discovery Studio 2017 R2  to preci the PDB  , with the main structural types being flavonoids, phenylpropanoids, alkaloids, terpenoids, etc. Chinese medicine contains a large number of chemical components, and the DS software was used to predict the ADMET parameters of the chemical components contained in the compound based on their chemical structures, which helps to find the possible active components quickly. Finally, a total of 16 active ingredients were screened, and the corresponding information about the screened active ingredients is detailed in p value >0.9 were screened from the prediction results of the target database as active ingredient targets, and a total of 194 active ingredient targets were obtained from the Chinese herbal compound. A larger number of targets were screened in the GeneCards database according to the search term “Appetite,” and 3330 targets were selected based on the criterion of score >2.3. 224 targets were obtained from OMIM database, and 275 targets were obtained from DisGeNEt. A total of 3552 targets were obtained after combining and deweighting, with 275 targets in OMIM database, 275 targets in DisGeNEt database, and 1 target in TTD database. The 194 potential targets of Chinese herbal ingredients were intersected with 3552 targets of disease targets, and a Wayne diagram was drawn  is shown in Figures The KEGG pathway enrichment analysis of potential targets by the DAVID 6.8 data platform -catenin, (+)-stepholidine, berberine, cianidanol, coclaurine, and moupinamide were identified as potential active ingredients of jujube. These active ingredients include 131 targets of action related to appetite and appetite regulation. Through PPI network analysis of jujube in appetite regulation, we identified 11 key targets: MAPK3, EGFR, SRC, HSP90AA1, and so on. For further screening by MCODE analysis, three key targets  were identified. The molecular docking results showed that the main components of jujube had strong binding activity to the main targets .MAOA can metabolize monoamine neurotransmitters . StudiesFurther, GO enrichment and KEGG pathway enrichment analysis showed that key genes act on signaling pathways such as neuroactive ligand-receptor interaction, serotonergic  synapse, cAMP signaling pathway, dopaminergic synapse, calcium signaling pathway, and hypoxia-inducible factor pathway. Among them, the key pathway of 5-HT synapses was closely related to appetite control and the treatment of related diseases. The key role of 5-HT in appetite control was formally proposed almost 30 years ago . StudiesHowever, it should be noted that network pharmacology is based on existing databases and results for network modeling, and there is a certain false-positive rate of predicted results due to the differences in raw experimental data under different experimental conditions.This paper presents a predictive analysis of the appetite regulation mechanism of jujube based on the theoretical level, and we hope that the results of this analysis can provide new ideas for the next in-depth research, and we also expect better research basis at the level of new drug development. Our research team will continue to focus on the progress of pharmacological research on jujube and will use the results of this paper as a reference to conduct relevant animal and cellular experiments to further investigate the effects and regulatory mechanisms of jujube on the appetite center of the hypothalamus.


This study aimed to determine the active ingredients of Huanglian Jiedu decoction (HLJDD) and the targets for treating dyslipidemia through network pharmacology to facilitate further application of HJJDD in the treatment of dyslipidemia.  Potential drug targets for dyslipidemia were identified with a protein-protein interaction network. Gene ontology (GO) enrichment analysis and KEGG pathway analysis were performed to elucidate the biological function and major pathways involved in the HLJDD-mediated treatment of dyslipidemia. κB, HIF-1, IL-17, TNF, FoxO, and PPAR signalling pathways were enriched in the antidyslipidemic action of HLJDD.  This approach revealed 22 components, 234 targets of HLJDD, and 221 targets of dyslipidemia. There were 14 components and 31 common targets between HLJDD and dyslipidemia treatment. GO enrichment analysis showed that these targets were mainly associated with the response to DNA-binding transcription factor activity, lipid localization and storage, reactive oxygen species metabolic process, and inflammatory response. The results of KEGG analysis indicated that the AGE-RAGE, NF- This study expounded the pharmacological actions and molecular mechanisms of HLJDD in treating dyslipidemia from a holistic perspective, which may provide a scientific basis for the clinical application of HLJDD. Dyslipidemia is a major risk factor for atherosclerotic cardiovascular disease (CVD), which accounts for more than half of all noncommunicable diseases and has become the leading cause of death worldwide . It has The therapeutic strategy for patients with dyslipidemia depends on their coronary artery disease risk. For patients at high risk, drug therapy combined with specific healthy lifestyle changes should be administered immediately. For those at moderate or low risk, specific healthy lifestyle changes should be promoted, and if the lipid levels are not targeted, drug therapy should be added. The choice of dyslipidemia-regulating medications depends on the major lipid abnormality and includes statins, resins, fibrates, and niacin .Rheum officinale (Dahuang), red yeast rice (Hongqu), and Fructus Crataegi (Shanzha) are frequently used in the treatment of hyperlipidemia and achieve significant effects in regulating blood lipids [As an important aspect of the complementary and alternative medical system, traditional Chinese medicine (TCM) has been widely used in treating dyslipidemia for centuries. In TCM, dyslipidemia can be classified as “phlegm,” “wet,” or “blood turbidity,” which is caused by overeating greasy food, dysfunction of transportation, transformation of the spleen and stomach, stagnation of qi, and phlegm dampness . Many hed lipids . Many hed lipids , Erchen d lipids , Gegen Qd lipids , and Wend lipids  also regHuanglian Jiedu decoction (HLJDD) is an herbal formula that originates from Zhou Hou Bei Ji Fang and was developed by Ge Hong and consists of 4 herbs: Fructus Gardeniae , Radix Scutellariae , Rhizoma Coptidis , and Cortex Phellodendri . Based on the TCM theory, multiple herbs in one formula should operate cooperatively. It has been reported that one of the aetiologies of dyslipidemia is heat toxicity, and HLJDD is able to clear heat and remove toxicity. Several basic studies have also shown that HLJDD has a certain effect on dyslipidemia , 13. TheThe study strategy is illustrated in https://tcmspw.com/tcmsp.php, 2021/06/01), with oral bioavailability (OB) ≥ 30%, drug likeness (DL) ≥ 0.18, and drug half-life (HL) ≥ 4 h used as screening criteria to screen for targets of corresponding ingredients. Then, the UniProt database  was used for target correction, with species restricted to “Homo sapiens,” and the target proteins were transformed into corresponding genes.The components of HLJDD, namely, Fructus Gardeniae, Radix Scutellariae, Rhizoma Coptidis, and Cortex Phellodendri, were searched in the TCMSP database , OMIM , PharmGKB  , and DrugBank  databases, as well as the therapeutic target database (TTD).The targets of dyslipidemia treatment were collected from the GeneCards  network, with the organism set to http://jvenn.toulouse.inra.fr/app/example.html, 2021/09/20). A Venn diagram with the common targets of HLJDD and dyslipidemia treatment was obtained.The targets of HLJDD and dyslipidemia treatment were imported into jvenn  to obtain the HLJDD-dyslipidemia PPI network, with the organism set to Homo sapiens, and the minimum required interaction score was set to medium confidence (0.400). Text mining, experiments, and databases were selected as the interaction sources.The common targets were then imported into the STRING database  algorithm in the CytoHubba plug-in.R package “ClusterProfiler”  was used to carry out gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of common targets of HLJDD and dyslipidemia treatment. The GO enrichment analysis items were the biological process (BP), cellular component (CC), and molecular function (MF). The organism was set to Homo sapiens, and the P value was set to ≤0.05.The The affiliation between herbs, components, common targets, and the top 30 enriched KEGG pathways were further imported to Cytoscape 3.9 software to construct the herb-component-target-KEGG pathway network. The network was analysed using the network analysis function.Four herbs, 22 components, and 234 targets of HLJDD were retrieved from TCMSP. The herb-component-target network  showed tA total of 221 targets of dyslipidemia treatment were retrieved from the GeneCards, OMIM, PharmGKB, TTD, and DrugBank databases. The dyslipidemia PPI network  showed tThirty-one common targets of HLJDD and dyslipidemia treatment were obtained and are shown in a Venn diagram . The HLJR package “ClusterProfiler.”GO enrichment and KEGG pathway enrichment analyses of the common targets of HLJDD and dyslipidemia treatment were performed using the Bioinformatics annotation of these genes identified 1058 GO terms. The analysis results of the GO functions including the top 10 enriched biological processes, CCs, and MFs are shown in a bubble chart Figures . RegardiYersinia infection, alcoholic liver disease, the intestinal immune network for IgA production, legionellosis, inflammatory bowel disease, chemical carcinogenesis-DNA adducts, the PPAR signalling pathway, and leishmaniasis.The KEGG pathway analysis revealed the presence of 64 enriched KEGG pathways, which are shown in a bubble chart . The patThese GO functions and pathways may be the potential mechanisms of HLJDD in dyslipidemia treatment.κB, HIF-1, IL-17, TNF, FoxO, and PPAR signalling pathways are closely related to the treatment of dyslipidemia with HLJDD.The relationships between herbs, components, common targets of HLJDD and dyslipidemia treatment, and enriched KEGG pathways are shown in Dyslipidemia is a major risk factor for atherosclerotic CVD, which has become the leading cause of death worldwide. TCM recipes exert therapeutic effects on several incurable diseases, including dyslipidemia . HoweverIn the present study, several network-based computational methods and algorithm-based approaches were used to predict targets and construct networks to assess the molecular interactions associated with HLJDD when used as a dyslipidemia therapy. Our research showed that some of the compounds in HLJDD such as quercetin, berberine, and kaempferol played a dyslipidemia-regulating role. We also showed that the biological functions of HLJDD-regulating dyslipidemia were related to DNA-binding transcription factor activity, lipid localization and storage, reactive oxygen species metabolic process, and inflammatory response.α and IL-6 concentrations, and inflammation slowly induces an increase in myeloperoxidase concentration, which decreases APOAI and HDL-C levels and disturbs HDL function, which confirms that inflammation plays an important role in dyslipidemia [Many genes such as APOB, LPL, APOA1, LEP, CD36, IL-6, and APOE may play an important role in dyslipidemia. Analysis of the functions of these genes may expand our understanding of the fundamental mechanisms leading to dyslipidemia. Apolipoprotein B (APOB) is associated with dyslipidemia and atherosclerosis development . Even inipidemia . LEP is ipidemia , 21. In α activation. Furthermore, other compounds of HLJDD also have the function of regulating dyslipidemia, such as crocetin [It has been reported that many compounds of HLJDD regulate dyslipidemia. The analysis of the network revealed that quercetin, kaempferol, berberine, acetic acid, and wogonin are predicted to be the major compounds in HLJDD for treating dyslipidemia. Several studies have shown that quercetin, an effective antioxidant and free radical scavenger against oxidative stress, has a hypolipidemic effect and antioxidative role by inhibiting ROS overgeneration and lipid peroxidation in mitochondria –24. In acrocetin , baicalecrocetin , and gencrocetin . TogetheκB, HIF-1, IL-17, TNF, FoxO, and PPAR signalling pathways were enriched in the antidyslipidemic action of HLJDD. It has been reported that the AGE-RAGE-oxidative stress axis is involved in the onset and progression of metabolic syndrome induced by a high-fructose diet [κB signalling pathway [The analysis of the network showed that the biological functions of HLJDD were linked to DNA-binding transcription factor activity, lipid localization and storage, reactive oxygen species metabolic process, and inflammatory response, which are closely related to the pathological mechanism of lipid metabolism. The results of KEGG analysis indicated that the AGE-RAGE, NF-ose diet . Quercet pathway . Animal  pathway . This reκB, HIF-1, IL-17, and PPAR signalling pathways to regulate lipid localization and storage, reactive oxygen species metabolic processes, and inflammatory responses, thereby achieving the treatment of dyslipidemia.In this study, we found that the active ingredients of HLJDD including quercetin, kaempferol, berberine, acetic acid, and wogonin may act on multiple targets such as TNF, IL-6, IL-8, PTGS2, and IL-10 and regulate multiple signalling pathways such as the AGE-RAGE, NF-This study employed the method of network pharmacology and clearly revealed the “multicomponent, multitarget, and multipathway” mechanism of HLJDD in treating dyslipidemia through network construction. In the future, clinical trials and animal experiments will be conducted based on previous reports , 36 to f


Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods.Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples . Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS.Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively.The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS.The online version contains supplementary material available at 10.1186/s12933-022-01716-0. Metabolic syndrome (MetS) is a combination of cardiometabolic risk determinants, including central obesity, elevated blood pressure, hyperglycemia, and dyslipidemia . It is aMetS has a multifaceted etiology, involving complex interactions between genetic and environmental factors . The patTo comprehensively investigate the metabolic characterization of MetS and its role in the development of consequent cardiometabolic diseases, several attempts have been made to screen biomarkers using various omics technologies, including metabolomics . MetabolThe analysis of metabolomics big data is complicated due to its complex structure, such as high dimensionality, high noise levels, and missing values. Conventional statistics-based models are usually not suitable for the analysis of metabolomics big data. Therefore, machine learning methods have become popular for the analysis of metabolomics data, especially for the construction of prediction models based on potential biomarkers for the diagnosis of diseases . Notably1H-NMR)-based untargeted metabolome profiling. Univariate analysis and multivariate analysis were applied to identify potential metabolic biomarkers for the diagnosis of MetS. Metabolic pathway enrichment analysis was performed to discover which metabolic pathways and metabolites are crucial to the physiopathology of MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS based on potential metabolic biomarkers. The protein-metabolite-disease interaction network was also explored, so that novel insights or hypotheses regarding the progression of MetS towards its consequent cardiometabolic diseases might be obtained.Taking into account these necessities, the aim of the present study was to comprehensively investigate the plasma metabolic characteristics of MetS in a large well-established Chinese cohort—China Suboptimal Health Cohort Study (COACS), and to screen potential metabolic biomarkers for MetS using proton nuclear magnetic resonance  and vortexed for 10 s. The mixture was centrifuged at 13,000 rpm for 15 min at 4 °C. Then 550 μL of supernatant was transferred into 5 mm NMR tubes for further analyses.Plasma samples were thawed at 4 °C. Once thawed, 200 μL of plasma was added to 400 μL of 0.045 M phosphate-buffered saline (PBS) prepared in deuterium oxide  operating at a 1H frequency of 599.77 MHz. One-domensional (1D) 1H-NMR spectra were recorded using the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. Each spectrum was acquired with 128 scans per sample using a spectral window of 16.4 ppm. The temperature was kept constant at 25 °C. Water suppression was achieved by using gated irradiation focused on the water frequency. All raw spectra files were obtained using VnmrJ software .All The study design and data analysis workflow are shown in Fig. Student t-test or Wilcoxon rank-sum test. Categorical variables are represented as frequencies and percentages. The Chi-square test or Fisher’s exact test was used to examine the differences in categorical variables between the two groups. The multiple testing corrections were controlled by using the false discovery rate (FDR).The data are presented as the means and standard deviations (SDs) if the continuous variables conformed to normal distribution. Otherwise, medians and interquartile ranges (IQRs) were used in descriptive statistics. The differences in continuous variables between the MetS and control groups were tested by Spearman’s rank correlation was performed and visualized using the “corrplot” R package. Metabolic pathway analysis and protein-metabolite-disease interaction network analysis were performed by using MetaboAnalyst [e1071” R package), RF (“randomForest” R package), KNN ( “kknn” R package), and logistic regression (“glm” R package). The receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the models. The area under the curve (AUC) and 95% bootstrap confidence intervals (CI) were also estimated.The orthogonal partial least squares projection-discriminant analysis (OPLS-DA) model was performed to identify the metabolic biomarkers using SIMCA, version 14.1 . To estimate the association between metabolic biomarkers and cardiometabolic risk factors, oAnalyst , and Cyt P < 0.05 was considered statistically significant.Statistical analyses were performed using R, version 4.1.2  and SPSS 25.0 . Two-tailedP < 0.05). Aside from these, significantly different frequencies of abdominal obesity, elevated blood pressure, elevated FPG, elevated TG, and reduced HDL-C phenotypes were observed between the two groups . The details about the demographic, biochemical, and anthropometric characteristics of the MetS patients and healthy controls are presented in Table In total, 205 MetS patients and 806 healthy controls were analysed in the present study. The average ages of the MetS and control groups were 57.21 ± 10.00 and 47.05 ± 12.93 years, respectively. The levels of body mass index (BMI), SBP, DBP, hip circumference (HC), WC, waist-to-hip ratio (WHR), FPG, TG, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), blood urea nitrogen (BUN), and creatinine (Cr) were significantly higher in the MetS group than those in the control group, whereas a significantly lower level of HDL-C was observed in the MetS group  were measured in this study. The glutathione metabolism pathway included 28 metabolites in total, while 2 metabolites (pyroglutamic acid and L-ornithine) were measured in the present study . There were five metabolites correlated with age , and the correlation coefficients ranged from − 0.238 to 0.188, which were relatively low. D-Maltose and Deoxyadenosine were associated with all the 14 cardiometabolic risk factors included in this study  database . There wAfter comprehensively profiling the metabolic biomarkers, four machine learning algorithms, including SVM, RF, KNN, and logistic regression, were performed to construct diagnostic models based on 13 metabolic biomarkers. The parameters of different models were tuned using ten-fold cross-validation on the whole dataset. Then, the parameters were applied to the whole dataset to provide final metrics of the suitability of the models for classifying individuals with MetS and healthy controls. Eventually, the kernel used in the SVM model was the radial kernel. The number of trees in the RF model was 500. The number of neighbours in the KNN model was 19. Then the diagnostic models based on 14 cardiometabolic risk factors were also built to compare the predictive ability with models based on metabolic biomarkers. The diagnostic performance of these eight models was shown in Table 1H-NMR-based metabolome profiling. Our findings unveiled that metabolome provides a valuable resource of biomarkers for the diagnosis and prevention of MetS and its consequent cardiometabolic diseases. These metabolomic biomarkers also provide a better insight into the critical metabolic pathways associated with MetS and a deeper understanding of its progression towards cardiometabolic diseases. Thus, the MetS-related metabolites and the metabolic patterns of metabolites can be used as potential diagnostic models for population risk stratification and targeted intervention of MetS towards chronic diseases, including CVD and T2DM.Identifying key metabolic biomarkers and pathways relevant to MetS and its progression towards cardiometabolic diseases is considered a viable strategy for the predictive diagnosis and targeted prevention of cardiometabolic diseases. In the present study, we comprehensively described the metabolomic biosignatures of MetS, and the metabolic biosignatures revealed significant differences between MetS patients and healthy participants. Based on the 13 potential metabolic biomarkers for MetS, the pathway analysis suggested that arginine and proline metabolism, and glutathione metabolism pathways were disturbed in MetS patients. Four machine learning algorithms, including SVM, RF, KNN, and logistic regression were used to build diagnostic models for MetS. ROC curve analysis showed that the AUCs of four models based on metabolic biomarkers ranged from 0.755 to 0.993. To our knowledge, the present study is the first to comprehensively provide metabolomic biosignatures of MetS based on a large well-established Chinese cohort by using We identified significant differences between MetS patients and healthy controls in cardiovascular risk factors, including BMI, SBP, DBP, HC, WC, WHR, FPG, TG, TC, HDL-C, LDL-C, BUN, and Cr Table . We addiPathway analysis revealed that the arginine and proline metabolism pathways are associated with MetS. Guanidinoacetate, hydroxyproline, and L-ornithine are the measured metabolites that participate in arginine and proline metabolism. Arginine, a semi-essential amino acid, is one of the most metabolically versatile amino acids. It serves as a precursor for the synthesis of urea, polyamines, proline, nitric oxide, creatine, glutamate, and agmatine . NumerouHydroxyproline, a nonessential amino acid, is a structurally and physiologically important amino acid in humans. Emerging evidence proves that the oxidation of hydroxyproline plays a significant role in regulating oxidative defense, apoptosis, and angiogenesis . StudiesPyroglutamic acid and L-ornithine are the measured metabolites that participate in glutathione metabolism. Glutathione is a low-molecular-weight tripeptide composed of the amino acid glutamine, cysteine, and glycine . It playThe metabolite-protein interaction network enables the visualization and exploration of interactions between metabolites and functionally related proteins. This visual network can be used to acquire innovative insights into the pathophysiology of MetS and its progression towards cardiometabolic diseases. According to the association between metabolites and diseases obtained from the HMDB database, a metabolite-disease interaction network was also produced to explore the disease-related metabolites. In the present study, MetS-related metabolic biomarkers were found to be associated with 23 diseases, such as Parkinson’s disease, Alzheimer’s disease, lung cancer, and schizophrenia. Some of these diseases were reported to be associated with MetS. The lower levels of L-ornithine, hydroxyproline, carnosine, and L-asparagine were observed in the individuals with MetS. All these four potential metabolic biomarkers for MetS were also found to be associated with Alzheimer's disease. Previous studies supported that MetS and T2DM are risk factors for Alzheimer's disease . The undSeveral limitations in the present study need to be addressed. Firstly, the causal effect was difficult to infer in data from a cross-sectional study design. The observed MetS-related metabolites may be the consequences rather than causes of MetS and its phenotypes. To investigate the causations between metabolic biomarkers and cardiometabolic risk factors, Mendelian randomization studies in the same cohort of participants are also needed. Secondly, given the semi-quantitative nature of untargeted metabolomics profiling, a targeted metabolomics study is underway against the same cohort to validate the potential biomarkers and pathways based on the findings in the present study. Despite the limitations, the present study has provided a novel strategy that plasma metabolomics offers an innovative alternative for the recognition of MetS. Building on the findings, further studies from diverse populations and geographical areas are warranted.The early diagnosis of MetS has the potential to identify the patients who are at high risk of developing CVD and T2DM at early stages, and evidence-based intervention for MetS may be a cost-effective method for targeted prevention, and personalized intervention for cardiometabolic diseases, such as CVD and T2DM. A total of 13 metabolites, including trans-acotinic acid, methanol, guanidinoacetate, hydroxyproline, pyroglutamic acid, glutaconic acid, D-maltose, D-fucose, taurine, deoxyadenosine, L-ornithine, L-asparagine, and carnosine, were selected as candidate biomarkers for MetS. The present study revealed the potential value of metabolomic biomarkers for the predictive diagnosis of MetS. MetS patients have a universal metabolic disturbance. The significantly higher level of guanidinoacetate and significantly lower level of L-ornithine in MetS patients indicated that the disturbance of arginine metabolism plays a significant role in the pathophysiologic mechanism of MetS and its phenotypes. Hydroxyproline and glutathione metabolism also play potential roles in the pathophysiologic mechanism of MetS. These findings determined the potential utility of MetS-related metabolic biomarkers and pathways for targeted prevention and personalized therapy of cardiometabolic diseases.Additional file 1: Figure S1. The stacked NMR spectra of plasma samples from 205 MetS patients and 806 healthy controls.Additional file 2: Table S1. Statistical analysis of identified metabolites.Additional file 3: Table S2. Pathway enrichment analysis of potential metabolic biomarkers.Additional file 4: Table S3. Spearman’s correlation coefficients between the 13 metabolomic biomarkers and 14 cardiometabolic risk factors.


Convolvulus pluricaulis (CP), a Medhya Rasayana (nootropic) herb, is a major ingredient in Ayurvedic and Traditional Chinese formulae indicated for neurological conditions, namely, dementia, anxiety, depression, insanity, and epilepsy. Experimental evidence suggests various neuroactive potentials of CP such as memory-enhancing, neuroprotective, and antiepileptic. However, precise mechanisms underlying the neuropharmacological effects of CP remain unclear. The study, therefore, aimed at deciphering the molecular basis of neuroprotective effects of CP phytochemicals against the pathology of dementia disorders such as Alzheimer's (AD) and Parkinson's (PD) disease. The study exploited bioinformatics tools and resources, such as Cytoscape, DAVID , NetworkAnalyst, and KEGG (Kyoto Encyclopedia of Genes and Genomes) database to investigate the interaction between CP compounds and molecular targets. An in silico analysis was also employed to screen druglike compounds and validate some selective interactions. ADME  analysis predicted a total of five druglike phytochemicals from CP constituents, namely, scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin. In network analysis, these compounds were found to interact with some molecular targets such as prostaglandin G/H synthase 1 and 2 (PTGS1 and PTGS2), endothelial nitric oxide synthase (NOS3), insulin receptor (INSR), heme oxygenase 1 (HMOX1), acetylcholinesterase (ACHE), peroxisome proliferator-activated receptor-gamma (PPARG), and monoamine oxidase A and B (MAOA and MAOB) that are associated with neuronal growth, survival, and activity. Docking simulation further confirmed interaction patterns and binding affinity of selected CP compounds with those molecular targets. Notably, scopoletin showed the highest binding affinity with PTGS1, NOS3, PPARG, ACHE, MAOA, MAOB, and TRKB, quercetin with PTGS2, 4-hydroxycinnamic acid with INSR, and ayapanin with HMOX1. The findings indicate that scopoletin, kaempferol, quercetin, 4-hydroxycinnamic acid, and ayapanin are the main active constituents of CP which might account for its memory enhancement and neuroprotective effects and that target proteins such as PTGS1, PTGS2, NOS3, PPARG, ACHE, MAOA, MAOB, INSR, HMOX1, and TRKB could be druggable targets against dementia. Dementia is a leading cause of disability and dependency among the elderly. Dementia patients may have difficulty remembering, thinking critically, behaving normally, and even performing normal daily activities. Neurodegenerative diseases (NDD) such as Alzheimer's (AD) and Parkinson's (PD) disease account for 60–80% of all dementia cases. The pathobiology of NDD is still unclear, however, pathogenic events such as oxidative stress, inflammation, apoptosis, and mitochondrial dysfunction play a critical role in the onset and progression of NDD . TargetiConvolvulus pluricaulis Choisy  is a perennial herb native to the Indian subcontinent. Commonly termed as Shankhpushpi in Ayurveda, C. pluricaulis (CP) has been indicated for various human ailments, including those affecting the central nervous system, namely, anxiety, depression, epilepsy, and dementia [β-sitosterol) that are related to its pharmacological effects [dementia , 4. The dementia . CP has  effects .A growing body of preclinical evidence has emerged supporting the ethnopharmacological uses of CP for neurological problems . In healThe neuropharmacological effects highlighted above are mostly cumulative effects of CP phytochemicals. The existing literature, however, can hardly explain precise mechanisms that underlie the neuroactive functions of CP. Understanding the underlying molecular mechanisms through an experimental approach requires intensive endeavors. Alternatively, network pharmacology is a promising bioinformatics tool that can predict the active phytochemicals and the molecular targets that are associated with the pharmacological actions of plant extracts , 31. TheCP compounds were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database . We alsoDrug-likeness of CP compounds were predicted by QikProp . The screening was carried out based on #stars (0–5), which indicates the number of properties that fall outside the 95% range of similar values for known drugs. A compound with fewer stars is more druglike than compounds with large stars.http://www.uniprot.org/) [Target information for the individual compound was retrieved from TCMSP database . The proot.org/) .https://bioinfogp.cnb.csic.es/tools/venny/index.html). An interaction network among compounds, targets, and diseases was established by Cytoscape v3.8.2 [First, the individual list of AD, PD, and dementia-related genes was retrieved from DisGeNET database v6.0 . Targetse v3.8.2 . The nodhttps://david.ncifcrf.gov/home.jsp). GO terms with a Pvalue of <0.01 were considered significant. Target proteins were categorized by the Panther classification system [http://pantherdb.org/).Functional enrichment analysis of Gene ontology (GO) for biological process, molecular function, and cellular components was carried out using DAVID 6.8 Gene Functional Classification Tool  . An interactive network connecting molecular targets and associated cellular pathways was also constructed by NetworkAnalyst. Signaling and disease pathways highlighting the targets of CP compounds were retrieved through the KEGG pathway mapper [https://www.genome.jp/kegg/tool/map_pathway2.html).A protein-protein interaction network was constructed by NetworkAnalyst  (https:/y mapper  (https:/https://pubchem.ncbi.nlm.nih.gov/) and then using ligand preparation by applying ligand preparation in Schrodinger 2017–1 with an OPLS-3 force field [For virtual screening, five compounds of 2D structure with SDF format were retrieved from the PubChem database  had the highest degree of connectivity with the compounds, followed by NOS3, INSR, NR1I3, NR1I2, HMOX1, ACHE, PPARG, MAOA, and MAOB (each with degree ≥3) suggesting the implication of these gene products as a prospective drug-target for CP compounds in the dementia management. The protein-protein interaction network illustrates the target proteins, some of which are direct targets of CP compounds and others are interacting proteins (Supplementary Compound-target-disease (C-T-D) network established through Cytoscape could explain the multitarget effects of CP, which are used to treat brain disorders associated with cognitive deficits. C-T-D network represents the interaction of CP compounds with the targets that are linked with AD, PD, and dementia . FocusinP < 0.05, Pvalues were adjusted using the Benjamini‒Hochberg procedure) are shown in GO analysis was carried out only with the disease-associated genes  that are common to AD, PD, and dementia as retrieved by employing Venny 2.1.0 online software . The topAn interactive network illustrates top cellular pathways that involved targets of CP compounds . CellulaPvalue <0.05) in the “signal transduction” module  were highly connected by CP compounds, and their interaction was further verified by molecular docking. Several signaling pathways related to inflammation including TNF pathway, HIF-1 pathway, and NF-κB pathway were enriched (PTGS1 and PTGS2) catalyzing the production of inflammatory mediators were targeted by CP compounds with the highest degree of connectivity , and the obtained docked complex was further subjected for MM-GBSA analysis. As shown in In any docking analysis of protein-ligand, it is ascertained that if the predicted complex obtained docking scores less than zero, indicating binding affinity of the ligand toward the receptor. However, molecular docking usually used approximated scoring functions to calculate binding energies, which are not correlated with experimental values , 48. In C. pluricaulis, alone or in combination, can enhance memory and protect against cognitive impairment [in silico approach to provide an in-depth insight into the neuropharmacological effects of CP phytochemicals and their protective potential against dementia. Virtual ADME screening identified a total of five active compounds from CP, such as scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin showing drug-likeness and blood-brain barrier permeability. Growing evidence suggest neurorestorative and memory protective potentials of these compounds. Quercetin, a natural polyphenolic of many plants, fruits, and vegetables, is found to be effective in protecting neurons from various injuries and ameliorating cognitive deficits [β-amyloidosis, tauopathy, astrogliosis and microgliosis in the hippocampus and the amygdala) and recover cognitive deficits in triple transgenic Alzheimer's disease model mice [β25-35-induced apoptosis of PC-12 cells via the ER/ERK/MAPK signaling pathway [P-coumaric acid) promotes hippocampal neurogenesis, improves cognitive functions, and reduces anxiety in post-ischemic stroke rats by activating BDNF/TrkB/AKT signaling pathway [β aggregation, and lipid peroxidation [Traditional knowledge and experimental evidence suggest that pairment , 6, 50. deficits . Quercetdel mice , 53. Anodel mice . The fladel mice , 56. Kae pathway . Other c pathway , 58, 59. pathway . Scopolexidation . Anotherxidation .κB, and p53). Bcl-2 is a prosurvival protein whereas Bax is a proapoptotic protein. IKK, NF-κB, and p53 are involved in inflammatory response [The C-T-D network illustrates that the selected CP metabolites were linked to the target proteins of dementia-associated cellular pathways. GO analysis revealed several enriched biological processes such as inflammatory response, response to drug, and aging that are implicated in the pathobiology of NDD. Network pathway analysis also shows that CP metabolites target several markers of the top enriched pathways. PI3K/Akt signaling is at the top of the enriched pathways associated with the development, survival, and activity of neurons. This pathway has multiple downstream effector targets including those associated with cell survival  inhibitors such as donepezil, rivastigmine, and galantamine are currently in use to compensate for memory deficits due to cholinergic dysfunction . MoleculINSR) although in network pharmacology quercetin and kaempferol interact with this target.Among the endocrine pathways, the dominant pathway is the insulin signaling pathway, which plays an essential role in ensuring neuronal survival and homeostasis, promoting synaptic plasticity and thereby supporting learning and memory function , 71. EviκB pathway, suggesting that anti-inflammatory effects mediated by CP compounds would play a pivotal role in preventing inflammatory cascade during pathobiological progression of NDD. Cyclooxygenase enzymes, namely, COX-1 (PTGS1) and COX-2 (PTGS2) catalyze the biosynthesis of inflammatory mediators such as prostaglandins and thromboxane. In the brain, COX-2 is activated by excitatory synaptic activity in neurons and by inflammation in the glia. COX-1/COX-2 pathway has pathogenic relevance in preclinical stages of Alzheimer's disease development [in silico reports on interaction of COX-2 with quercetin and kaempferol also support our data [There was an enrichment of inflammation-related pathways, including TNF pathway, HIF-1 pathway, and NF-elopment . Patholoelopment . Cyclooxelopment . Severalelopment  and indoelopment  have shoour data .In addition to the above cellular pathways, CP compounds target some other pathways, namely, autophagy, mitophagy, apoptosis, necroptosis, and some specific molecular markers of AD and PD pathways. Endothelial nitric oxide synthase or eNOS (NOS3) is known for its outstanding role in regulating cerebral blood flow and is associated with synaptic plasticity such as long-term potentiation . eNOS atOther important targets are monoamine oxidases (MAOs) that catalyze the oxidative deamination of monoamines and contribute to the metabolism of dopamine, a neurotransmitter of dopaminergic neurons. Drugs that inhibit MAO, particularly MAOB, such as selegiline and rasagiline are currently in clinical use in patients with PD –85. DockHMOX1) is a stress-sensitive enzyme that catalyzes the breakdown of heme into iron, carbon monoxide, and biliverdin/bilirubin and is involved in the pathobiology of AD and other brain disorders. Astroglial induction of the HMOX1 by β-amyloid and cytokines leads to mitochondrial iron sequestration and may thereby contribute to pathological iron deposition and bioenergy failure [Heme oxygenase-1 or HO-1 ( failure . Pharmac failure . All CP γ (PPARG), a ligand-activated nuclear transcription factor, regulates the expression of multiple genes that encode proteins involved in the regulation of lipid metabolism, improvement of insulin sensitivity, and inhibition of inflammation [γ agonists counteract oxidative stress, neuroinflammation, and Aβ clearance [γ agonists such as fenofibrate, icariin, and naringenin are known to be neuroprotective, supporting neuronal development, synaptic plasticity, and ameliorating cognitive deficits [γ, suggesting that these compounds can ameliorate cognitive deficits through activating PPARγ signaling.Peroxisome proliferator-activated receptor-gamma or PPARammation . PPARγ alearance , 88. PPAdeficits , 89, 90.in silico analysis predicts that CP metabolites, namely, scopoletin, 4-hydroxycinnamic acid, kaempferol, quercetin, and ayapanin are the major bioactive leads that showed interaction with various molecular targets and cellular pathways crucial to neuronal growth, survival, and activity. The signaling pathways that CP compounds primarily target include the PI3K/Akt signaling pathway, the neurotrophin signaling pathway, and the insulin signaling pathway. In addition, top targets of CP compounds including PTGS1, PTGS2, NOS3, INSR, HMOX1, ACHE, PPARG, MAOA, MAOB, and TRKB may be potential druggable targets for future drug designing to address dementia disorders. Together with the previous reports, the combined network pharmacology and in-silico observations form a scientific basis that supports the ethnomedical application of CP for memory enhancement and against aging/pathological cognitive deficits. However, further investigation of memory-enhancing and neuroprotective effects of CP and its metabolites is essential to extrapolate the findings from preclinical and in silico models into clinical subjects.The


Buyang Huanwu Decoction, a traditional Chinese medicine decoction, is widely used to treat spinal cord injury in China. However, the underlying mechanism of this decoction in treating spinal cord injury is unclear. This study used network pharmacology and molecular docking to examine the pharmacological mechanism of Buyang Huanwu Decoction in prevention and treatment of spinal cord injury. The active compounds and target genes of Buyang Huanwu Decoction were collected from the Traditional Chinese Medicine Systems Pharmacology and the SwissTargetPrediction Database. The network diagram of ”traditional Chinese medicine compound target“ was constructed by Cytoscape software. Genetic data of spinal cord injury were obtained by GeneCards database. According to the intersection of Buyang Huanwu Decoction’s targets and disease targets, the core targets were searched. The protein-protein interaction network were constructed using the STRING and BisoGenet platforms. Meanwhile, gene ontology enrichment and Kyoto encyclopedia of genes, and genome pathway were performed on the intersection targets by Metascape. Molecular docking technology was adopted to verify the combination of main components and core targets. A total of 109 active compounds and 5440 prediction targets were screened from 7 Chinese herbal medicines of Buyang Huanwu Decoction, with 98 active components and 49 related prediction targets being strongly linked to Spinal Cord Injury. By studying protein-protein interaction network, a total of 8 core proteins were identified, primarily interleukin-6, tumor protein P53, epidermal growth factor receptor, and others. Positive regulation of kinase activity regulation of reaction to inorganic chemicals are the basic biological processes. Buyang Huanwu Decoction cures Spinal Cord Injury primarily by moderating immunological inflammation, apoptosis, and oxidative stress, which involves the cancer pathway, the HIF-1 signaling pathway, the p53 signaling pathway, the MAPK signaling pathway, and so on. The results of molecular docking demonstrated that the primary components could attach to the target protein effectively. Finally, the mechanism of Buyang Huanwu Decoction in the treatment of spinal cord injury through multicomponent, multitarget, and multichannel was deeply explored. And it offers new ideas and directions for future research on the mechanism of the treatment of spinal cord injury. The primary pathogenic mechanisms of SCI include axonal separation, neuronal death, and, eventually, lifelong neurological impairment. Acute spinal cord injury (ASCI) is caused by two mechanisms: primary and subsequent injury. The term “primary injury” refers to the first mechanical lesion to the spinal cord, whereas “ subsequent injury “ refers to a set of biochemical and cellular processes involved in SCI, such as oxygen free radical generation and the inflammatory response after trauma. The literature reports that the global incidence of SCI is 10.4 to 83 cases/million/year. According to the latest SCI epidemiological data in China, the incidence rate has risen quickly in the last decade, with the elderly (65–74 years old) having the greatest incidence rate, with an average annual incidence rate of 127.1 cases/million people. SCI can result in major consequences, in China, the proportion of quadriplegia and complete damage is as high as 37.4% to 82.0% and 14.1% to 73.9%, respectively. SCI will not only bring considerable bodily and psychological impairment to the sufferer, but will also impose a significant economic burden on society as a whole. As a result, its treatment is a critical issue.Spinal cord injury (SCI) is a common and devastating neurological injury that can result in significant neurological damage and possibly paralysis around the world. The latter is critical in the treatment of SCI patients, yet current physical and rehabilitative exercises are ineffective. Thus, finding a more effective treatment strategy is critical. SCI is classified as “low back pain,” “atrophy syndrome,” and “long bi” in traditional Chinese medicine (TCM). According to studies, TCM has obvious therapeutic effects on it, particularly when used later in the recovery process. Buyang Huanwu Decoction (BYHWD), which is composed of Astragalus membranaceus, Angelica tail, Red Peony Root, Earthworm, Sichuan lovase rhizome, Carthamus tinctorious, Persicae Semen, has been shown to improve the ischemic and inflammatory response of post-injury tissues to reduce post-injury tissue edema, inhibit lipid peroxidation, neural cell apoptosis.–13 However, because TCM prescriptions have complicated components and targets, network pharmacology methodologies must be used to describe their distinct pharmacological mechanisms.The goal of SCI treatment is to promote spinal cord tissue regeneration and functional recovery. SCI treatment is currently separated into two stages: anti-inflammatory symptomatic treatment in the acute stage and rehabilitation function exercise in the latter stage. Molecular docking may anticipate the link between the active component and the target protein based on receptor features, receptor interaction mode with the drug molecule, and intermolecular binding mode and affinity, making it more powerful in proving the drug’s mechanism of action.,16 Finally, the objective of this study is to systematically reveal the internal mechanism of BYHWD in the treatment of SCI from multiple levels and perspectives using network pharmacology in conjunction with systems, bioinformatics, network science, and other disciplines, in order to provide better clinical guidance.Data mining may be fully utilized in network pharmacology to disclose the association between TCM chemicals and diseases, as well as to clarify the mechanism of TCM to treat diseases.  was used to collect the relevant chemical components of BYHWD -Chi Shao, Chuanxiong, Angelica, Di Long, and Huang Qi. To acquire the active ingredients of BYHWD, the active ingredients of each medicine were screened based on oral bioavailability (OB) http://www.swisstargetprediction.ch/) were used, and the Uniprot database (http://www.uniprot.org/) was used. “Protein names” were universally adjusted to official names.The TCM Systematic Pharmacology Database to create a drug-component-predicted target network diagram, and then use Cytoscape 3.7.2 software’s Merge function to create the final drug-component-target network. The nodes represent TCMs, active substances, and targets, while the edges reflect the relationships between TCMs and ingredients, as well as between ingredients and targets. Analyze the node degree value of the aforementioned network using the software’s “ Networkanalyzer” plug-in. The greater the value, the more significant the node in the network. BYHWD appears to play a major role in the treatment of SCI based on the components with higher node degree values.Import the active ingredients and predicted target information from BYHWD and “1.1” into Cytoscape 3.7.2 softwarehttps://www.genecards.org/) and enter keywords such as “spinal cord injury” to retrieve SCI-related targets. Upload the anticipated targets and SCI-related targets from “1.1” to the WeChat platform (http://www.bioinformatics.com.cn/), choose “Venn Diagram,” and get BYHWD in the treatment of potential SCI targets.To screen disease targets, use the human gene database GeneCards (https://stringdb.org). To obtain protein-protein interactions, the possible therapeutic targets were imported into the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) database, the species was set to human, and a moderate interaction value of “0.4” was used. The data was displayed and analyzed using Cytoscape 3.7.2 software, and the topological features of prospective treatment targets were investigated using Cytoscape’s cytoNCA function.The protein-protein interaction (PPI) network of putative therapeutic targets of SCI was examined in this study utilizing the STRING database (https://metascape.org/), and the species was restricted to “H. sapiens.” For GO enrichment analysis of the targets of BYHWD in the treatment of SCI, the biological process (BP), cellular component (CC), and molecular function were chosen; pathway enrichment analysis of target genes was performed using the KEGG database. To screen out biologically transgenic target pathways with substantial differences, the threshold was set to P < .05, and the P value was ordered in increasing order. Create bar and bubble charts from the results.The BYHWD junction genes in the therapy of SCI were imported into the biological information annotation database Metascape (http://www.rcsb.org/pdb/) database, find the PDBID of the corresponding protein, and save it in pdb format. Next, search the TCMSP database for the compound component corresponding to the target protein, download and save it as mol2 format, import the target protein data information and its corresponding active ingredient information into AutoDockTools-1.5.6 software, dewater and hydrogenate the protein, dock the small molecule following hydrogenation to acquire the docking binding energy of the drug and the target protein, and then do visual analysis with PyMOL-2.5.0 software.This study used AutoDockTools-1.5 to see if the beneficial chemicals in BYHWD could attach to target proteins in the body and exert their curative effect after entering the body. Molecular docking between the core compound and the core target can successfully determine the small molecule compound that matches the spatial and electrical properties of the target receptor’s active site, as well as whether the compound can bind to the target after entering the body. Check the precision of compound target predictions. Enter the name of the potential target protein into the PDB  after deleting duplicate entries and using 0B To fully understand the molecular mechanism of BYHWD in the treatment of SCI, a drug-component-target network was built. The drug-component-target network was created and visualized using Cytoscape 3.7.2’s Merge function, as shown in Figure The Venn diagram is presented in Figure The top 8 potential targets were identified after twice screening the median of the values greater than or equal to Degree: AKT1, TP53, IL6, VEGFA, EGFR, CASP3, STAT3, and TNF, as shown in Table In order to determine the top 10 core targets of Degree value, the intersection of the active ingredient targets of Tonic Yang Returning Five Soup and SCI disease targets were taken and visualized by Cytoscape 3.7.2 software Fig. . The topP value. This pathway is followed by GO:0051091, which enriches 16 genes. Positive control of kinase activity, positive regulation of DNA-binding transcription factor activity, and regulation of inorganic substance response were the key functions of the top-ranked genes. After hierarchical clustering, the pathway GO:0045121 in the cellular fraction, which had a total of 74 enrichment findings, had the lowest P value and enriched 12 genes, followed by GO:0031983, which enriched 9 genes. The top ranking mostly involves (nerve direction) membrane rafts, sacs, and dendrites. There are a total of 73 enrichment findings in the molecular functions. GO:0019903 has the lowest P value after hierarchical clustering, enriching 8 genes, followed by GO:0001085, enriching 7 genes. Protein phosphatase binding, RNA polymerase II transcription factor binding, and protein domain specific binding are among the top ranks.Figure P value after hierarchical clustering, enriching 8 genes, followed by GO:0001085, enriching 7 genes. Protein phosphatase binding, RNA polymerase II transcription factor binding, and protein domain specific binding are among the top ranks.The top ranking mostly involves (nerve direction) membrane rafts, sacs, and dendrites. There are a total of 73 enrichment findings in the molecular functions. GO:0019903 has the lowest The results of KEGG analysis showed that 49 targets of Buyang Huanwu Decoction in the treatment of SCI were enriched in 243 channels  are a part of the TP53 signaling pathway. According to research, miR-429-5p controls the expression of Bcl-2, which controls the apoptosis of spinal cord cells and contributes to the healing process after SCI. The caspase family, which is classified into apoptosis-related caspases and inflammation-related caspases, is a collection of highly homologous and structurally identical proteases. Specifically, caspase3 controls apoptosis. According to studies, BYHWD can help rats recover their neurological function after a SCI by suppressing the caspase cells’ apoptotic process. The STAT protein family, which includes STAT3, is activated by phosphorylation in response to a number of cytokines and growth factors.,37 The JAK2/STAT3 signaling system controls the production of several cytokines and growth factors as well as cell proliferation, differentiation, and death. It is also linked to the incidence and progression of acute SCI. We propose that the inflammatory response, cellular autophagy, and the apoptotic process can control neurological impairment and recovery in tonic yang and rejuvenation soup.Through network analysis, it was shown that AKT1, IL6, TP53, VEGFA, EGFR, CASP3, TNF, STAT3, CTNNB1, and HRAS may be the primary targets of BYHWD in the treatment of SCI. Three members of the serine/threonine protein kinase family, known as AKT , are closely connected to one another and are involved in a number of biological activities. The PI3K/Akt system is strongly associated with both pro-inflammatory and anti-inflammatory responses, and the Akt signaling pathway has the ability to regulate activities including inflammatory cytokines and macrophage phagocytosis. More research is required to more conclusively demonstrate the mechanism of action of BYHWD, as there are currently only a small number of studies on its use in the treatment of SCI.BYHWD can also cure SCI by controlling inflammation, antioxidant stress, and apoptosis via the cancer route, HIF-1 signaling pathway, MAPK signaling pathway, and Wnt signaling pathway, among other pathways, according to the KEGG enrichment study. We discovered that BYHWD can help neural stem cell transplantation, which is mostly controlled by wnt signaling, to cure spinal cord damage in experimental rats, boost the growth of mitotic cells in the spinal cord, and thereafter, to some extent, support the restoration of neurological function. Additionally, several researchers have discovered that BYHWD can lessen secondary damage to the spinal cord brought on by ischemia and hypoxia, ameliorate ischemia and hypoxia, and stimulate the production of HIF-1 and VEGF in the SCI section of SD rats following SCI. support the restoration of nerve function after sexual damage.BYHWD, a tonic, possesses anti-inflammatory and antioxidant properties, suppresses apoptosis, and aids in the healing of nerve cells. Several targets that are important for treating SCI can be affected by a single chemical, and each target may be connected to a number of pathways. Clinically, BYHWD is successful in treating SCI, although it’s unknown how it works. Through the use of TCM network pharmacology and molecular docking tools, we investigated the mechanism of multi-component, multi-target, and multi-channel therapy of SCI in this research. It draws attention to the legitimacy and efficacy of BYHWD, which offers clinical practitioners direction in the treatment of SCI and fresh thoughts for further examining the mechanism of action of BYHWD.The authors would like to thank the Sichuan Vocational College of Health and Rehabilitation for helpful discussions on topics relevant to this work.Conceptualization: Lan Chen.Data curation: Ying Wang.Formal analysis: Ying Wang and Lan Chen.Investigation: Hai-Xu Chen.Methodology: Ying Wang and Hai-Xu Chen.Project administration: Lan Chen, Ying Wang, and Hai-Xu Chen.Resources: Jun-Wei Wang, Xin Chen.Software: Jun-Wei Wang.Supervision: Ying Wang and Lan Chen.Validation: Jun-Wei Wang.Writing – original draft: Ying Wang.Writing – review & editing: Lan Chen.


Background: Glioblastoma multiforme (GBM) is the most aggressive primary nervous system brain tumor. There is still a lack of effective methods to control its progression and recurrence in clinical treatment. It is clinically found that Xiaoliu Decoction (XLD) has the effect of treating brain tumors and preventing tumor recurrence. However, its mechanism is still unclear.Methods: Search the Traditional Chinese Medicine System Pharmacology Database (TCSMP) for efficient substances for the treatment of XLD in the treatment of GBM, and target the targeted genes of the effective ingredients to construct a network. At the same time, download GBM-related gene expression data from the TCGA and GTEX databases, screen differential expression bases, and establish a drug target disease network. Through bioinformatics analysis, the target genes and shared genes of the selected Chinese medicines are analyzed. Finally, molecular docking was performed to further clarify the possibility of XLD in multiple GBMs.Results: We screened 894 differentially expressed genes in GBM, 230 XLD active ingredients and 169 predicted targets of its active compounds, of which 19 target genes are related to the differential expression of GBM. Bioinformatics analysis shows that these targets are closely related to cell proliferation, cell cycle regulation, and DNA synthesis. Finally, through molecular docking, it was further confirmed that Tanshinone IIA, the active ingredient of XLD, was tightly bound to key proteins.Conclusion: To sum up, the results of this study suggest that the mechanism of XLD in the treatment of GBM involves multiple targets and signal pathways related to tumorigenesis and development. This study not only provides a new theoretical basis for the treatment of glioblastoma multiforme with traditional Chinese medicine, but also provides a new idea for the research and development of targeted drugs for the treatment of glioblastoma multiforme. Human cancer has become a main cause of death, and glioma, which can be divided into low-grade glioma and high-grade glioma, is the most frequent main central nervous system malignant tumor. Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor, accounting for 14.5% of all tumors and 48.6% of malignant tumors. At present, there is no cure for GBM, and GBM patients have a median survival duration of fewer than 8 months. . DespiteTCM has unique tumor prevention and treatment effects, and it is an important part of comprehensive tumor treatment. A large number of clinical studies have demonstrated that TCM can be combined with surgery, radiotherapy and chemotherapy to achieve better results, prevent tumor progression, alleviate clinical symptoms and reduce adverse reactions of radiotherapy and chemotherapy, thereby improving the quality of life and prognosis of patients . For exaGlycyrrhiza uralensis) was first found in Shennong Materia Medica, the oldest pharmacopeia in China. Licorice is widely used in clinical prescriptions of TCM. Modern pharmacological studies have shown that Glycyrrhiza uralensis has a variety of biological activities, such as anti-tumor, anti-virus, anti-inflammatory, antioxidant, immune regulation, liver protection and nerve protection. The effects of several components in Glycyrrhiza uralensis have also been studied in glioblastoma multiforme. Glycyrrhizin (ALA) is a natural chalcone extracted from Glycyrrhiza uralensis. It induces mitochondrial dysfunction in glioblastoma multiforme stem cells (GSCs) and further activates the mitochondrial apoptosis signaling pathway, resulting in cell death in vitro. Tanshinone IIA is the main active component of Salvia miltiorrhiza. Recent studies have shown that it inhibits the proliferation, migration and invasion of GBM through miR-16-5p/Talin-1  from the traditional Chinese Medicine system Pharmacology (TCMSP) Database , a multi-functional Web server for exploring the relationship between pharmacological and chemical structures based on molecular 3D similarity methods (https://www.uniprot.Org) are used to standardize the prediction targets (https://cn.string-db.org/), which is commonly used to search and predict the interaction between the compound and the target protein, is also used to predict the target. Finally, the Venn diagram is made, and the intersection of the two databases is regarded as the final gene target of XLD. Among them, XLD includes 12 kinds of traditional Chinese medicine ingredients, and a total of 200 active ingredients can pass through the blood-brain barrier, of which gancao, Yanhusuo and danshen account for 64% of the total drugs.After screening the active components of XLD through the TCMSP database, the active components are uploaded to PubChem  database was launched by the National Cancer Institute (NCI) and the National HumanGenome Research Institute (NHGRI) of the United States in 2005. Through the application of genome analysis technology, especially large-scale genome sequencing, this paper attempts to draw and systematically analyze the genome variation map of all human cancers (the short-term target is 50 kinds of tumors including subtypes). We downloaded 169GBM tumor samples, Genotype-Tissue Expression . This database is different from TCGA and ICGC. On the other hand, GTEx collected tissues from normal people for sequencing, and 1,152 normal brain samples were obtained (https://bioconductor.org/pack ages/Limma/) to screen the differentially expressed genes between high risk group and low risk group , 894 differentially expressed genes were obtained.TCGA (obtained . Using LBy using Cytoscape (v.3.8.0) to construct the target network of 166target genes of active components in traditional Chinese medicine that can pass through the blood-brain barrier and 19 genes of drug-disease, and the (CTD) network of compound-target-disease, we can see the core relationship between XLD active component network and molecular targeting prediction target and compound-target-disease (CTD) network. The network analyzer plug-in is used to identify key active components and key candidate targets according to the following criteria: nodes whose values exceed the average of all nodes in the network. The degree value is the number of edges a node has in the network, indicating how many herbs/ingredients/targets a node is associated with. If the degree value of the node is large, it is considered that the node plays a more important role in the network.The 166 target genes of the above drugs and 19 genes of the same drug-disease were introduced into the STRING database to construct the PPI network. STRING database integrates many protein-protein association networks for biological quality control. We chose the core PPI target based on the above-average degree score and the confidence score above 0.9. Used to build PPI networks. Cytoscape is then applied to examine the potential correlation between these genes. The target-target (TT) network between XLD and GBM is constructed using the plug-in CytoNCA of Cytoscape, and the interaction network of gene protein intersection between XLD and GBM is constructed by the plug-in BisoGenet of Cytoscape, and visualized with Cytoscape .p-value filtering condition was pvalueFilter <0.05. The corrected p-value filtering condition was qvalueFilter <0.05i. In order to further clarify the biological effects of the active components of XLD and the potential mechanism of antagonizing the therapeutic effect of GBM, the selected targets were marked with signal pathways such as cell cycle, p53 and glioblastoma multiforme  to identify that the interacting genes and networks were mapped using KEGG parsers.Then R software was used to deal with the above 169genes and 19 intersecting genes, and clusterProfiler, org. Hs.eg.db, enrichplot and ggplot2, pathview plug-in packages (vision 3.6.2) were used to visualize GO and KEGG, respectively. The http://stitch.embl.de/) to accurately obtain the PDB number of these proteins in humans and obtain the two-dimensional structure of the eight components through the PDB number. The 2D structures of XLD active components and their main targetswere downloaded from Pubchem database and PDB database (http://www.rcsb.org/), respectively, and these molecules were dehydrated and hydrogenated. Then, autoDOCK 1.5.7 software was used to complete the molecular docking analysis. The combination of drug composition and target can be visualized by thermal map, which shows good binding activity, and the lower the binding energy is, the better the docking effect is. MOE software (v.2019.0102) was used to verify the molecular docking of the molecular pairs with the lowest binding energy between drug small molecules and protein macromolecules.First, the eight key genes were imported into STRING . These three types accounted for 71.11% of the total components.There are 12 components of XLD, including Baihuasheshecao, baizhi, Baizhu, banxia, Banzhilian, chenpi, Chuanxiong, Chuanxiong, Danshen, Fuling, Gancao, Gouteng and Yanhusuo. To reveal the mechanism of action of XLD in glioblastoma multiforme (the selected drugs can pass through the blood–brain barrier), we downloaded the drug ingredients of XLD from TCMIP and analyzed the ingredients to build a drug network. The initial network contained a total of 346 nodes and 2,626 edges. Then, the final active ingredients were screened using the PubChem database. Among them, 53 were licorice, 41 were Yanhusuo, and 34 were A compound regulatory network of the relationship between active components and molecules was established with the above-mentioned XLD TCM formula, and an initial network consisting of 346 nodes and 2,626 edges was obtained. Then, topology analysis was applied to filter out the required core network, which contained a total of 44 nodes and 168 edges. This network included nine drug nodes, and 35 molecular nodes . The core target network was mapped using Cytoscape .SPP1 (osteopontin) and HLA-DRA (HLA class II histocompatibility antigen) showed the greatest change, MBP (Myelin basic protein) and EEF1G (Elongation factor 1-gamma) showed the highest degree of upregulation, and the first two genes were the most downregulated (screening criteria: FDRFilter <0.05). A heat map (We downloaded the tissue data of GBM patients (169 samples) from the TCGA database and normal brain tissue data  from the GTEX database. The downloaded data were combined using the limma software package of R software, and differentially expressed genes were screened. Compared with normal brain tissue samples, there were 894 significantly differentially expressed genes in GBM, including 418 upregulated genes and 476 downregulated genes. According to the fold change, heat map  and a voheat map  were draTP53, JUN, MYC, MAPK1, STAT3, FOS and other genes formed the core of the network.First, a PPI network of the obtained XLD targets was constructed. Importantly, these components can pass through the blood–brain barrier and play an important role in the brain. We obtained a total of 169 gene targets of the effective components in XLD, as shown in Then, we used the above-mentioned XLD gene target network and the differentially expressed genes in glioblastoma multiforme to construct the drug-disease-target network. As shown in the network diagram in We analyzed the GO and KEGG enrichment of 169 targets of XLD and 19 targets common to glioblastoma multiformes using R software . GO analAmong the 19 common genes of drug and disease, the biological processes were mainly those such as regeneration, cell cycle arrest, negative regulation of G1/S transition of mitotic cell cycle, negative regulation of G1/S phase cell cycle transition, G1/S transformation of mitotic cell cycle, regulation of neuron apoptosis, regulation of cell cycle block, regulation of DNA biosynthesis, positive and negative regulation of pri-miRNA transcription by RNA polymerase II. These mainly involve, for example, the cyclin-dependent protein kinase holoenzyme complex, serine/threonine protein kinase complex, transferase complex, transfer phosphorus-containing groups, complex protein kinases, RNA polymerase II transcriptional regulatory complex, complex transcriptional regulatory agencies.CDK1, CXCL8, CDK4, MYC, TP53, DKN1A, PLAU, MMP9, HIF1A, CCL2, JUN, BIRC5, seven KEGG passages are: hsa05202, hsa04657, hsa05163, hsa04218, hsa05205 hsa01524, hsa04012, hsa04668, hsa04110, hsa04210.Finally, KEGG enrichment analysis was performed on these genes to determine the mechanism of HLT against GBM, and it was found that they are mainly involved in glioblastoma multiforme, cellular senescence, cell cycle and transcriptional misregulation in cancer pathways. To elucidate the overall mechanism of HLT in glioblastoma multiforme, the above steps were repeated during KEGG network construction, which provided a complete mechanism of XLD to understand the pathogenesis of glioblastoma multiforme . Twelve Jun was 1JNM, the PDB with the crystal structure of PCP4 was 2N77, the PDB with the crystal structure of MMP9 was 1L6J, the PDB with the crystal structure of TP53 was 2K8F, the PDB with the crystal structure of CDKN1A was 4RJF, and the PDB with the crystal structure of TnFaiP6 was 2PF5. Molecular docking was conducted for all pharmacodynamic components and targets in sequence, and the results were displayed in a heat map  through the MEK/ERK signaling pathway. Lu et al. found that LA effectively inhibited the growth of U87 GBM cells by inducing G0/G1 and G2/M cell cycle arrest, an effect that was attributed to reduced cyclin and cell cycle-dependent kinase mRNA and protein levels . LupiwigSalvia miltiorrhiza. Salvia miltiorrhiza dispels blood stasis and relieves pain, promotes blood circulation and relieves menstrual pain. Salvia miltiorrhiza contains fat-soluble and water-soluble components. Most of the fat-soluble components are conjugated quinones and ketones, such as tanshinone I, tanshinone IIA, tanshinone IIB and cryptotanshinone. The water-soluble components include Danshensu, Danshen acid A, protocatechuic acid and protocatechuic aldehyde. Among them, tanshinone, cryptotanshinone and tanshinone have all been shown to have effects on GBM cells  to high-grade (III–IV). Approximately 95% of low-grade GBM progress into high-grade tumors; less than 3% of these patients are still alive 5 years after diagnosis . At presp = 0.13)  . Further = 0.13) . These tThe discovery of plant-derived bioactive compounds as new therapies may provide a therapeutic advantage in GBM research. Approximately 60% of clinically approved anticancer drugs on the market come from medicinal plants . Their mTP53 and Jun were located in the core, indicating that they may be a key hub mediating the effects of XLD on a variety of pathological mechanisms in GBM. Mutations in the tumor suppressor gene TP53 occur in a variety of cancers, including GBM, which usually lead to a loss of TP53 function and several transcriptional changes, promoting the development of tumors. Approximately 30% of glioblastomas carry TP53 mutations. TP53-dependent cell cycle arrest has been shown to be involved in mediating the sensitivity of chemotherapy. Additionally, TP53 has been identified as the main regulator of CSC self-renewal, differentiation and tumorigenic potential in glioblastoma (In China, XLD is widely used in the treatment of tumors, including GBM. Therefore, comprehensively studying the specific mechanism of XLD in the treatment of tumors, especially GBM, is necessary. This information can help us better understand the regulatory mechanism of drugs in tumors, elucidate the mysteries of TCM and identify new research directions. To address the shortcomings of current gene therapy and immunotherapy regimens and provide novel and effective methods. Therefore, we used R software to study the biological process of related genes. The results showed that XLD regulates the proliferation of tumor cells by affecting the following biological processes: lipopolysaccharide response, phospholipase C-activated G protein-coupled receptor signaling pathway, radiation response, oxidative stress response, transcription initiation of RNA polymerase II promoter, DNA template transcription, hypoxia response and others. Because these regulatory mechanisms are closely related to the occurrence and development of GBM, we hypothesize that XLD may affect the above pathways to regulate GBM cell proliferation, metastasis and other tumor biological processes. To understand the mechanism by which XLD regulates genes in GBM, the expression data of GBM tumor tissues and normal brain tissues were downloaded from TCGA and GTEX databases, and the differentially expressed genes were obtained by R software. The drug-disease-target PPI network was constructed between the target genes of XLD and the differentially expressed genes in GBM. We found that blastoma . Howeverblastoma . To furtThere are still some shortcomings in this study that need to be investigated in depth in the next step. Firstly, only a computer-based exploration of the mechanism of XLD for the treatment of glioblastoma multiforme was used. Next, we need to further confirm the therapeutic effect of XLD on GBM through basic experiments using the core small molecule drugs in our conclusions. In addition, the optimal dose of XLD for treating patients with GBM needs to be determined. In conclusion, we have now obtained a possible mechanism for the treatment of GBM with XLD through a network pharmacological analysis and hope that drugs including tanshinone iia will become a new and promising targeted chemotherapeutic agent. It remains to be further elucidated whether it can be a new adjuvant therapy. In China, XLD has been used clinically with relatively few adverse effects, and therefore it is promising to be used in GBM-related clinical trials.