The purpose of this study was to investigate the mechanism of sanguinarine (SAN) against nasopharyngeal carcinoma (NPC) by means of network pharmacology, molecular docking technique, and experimental verification.The SAN action targets were predicted using the Swiss Target Prediction database, the related NPC targets were determined using the GEO database, and the intersection of drug and disease pathway targets were considered to be the potential targets of SAN against NPC. The target-protein interaction network map was constructed using the STRING database, and the core target genes of SAN against NPC were obtained via topological network analysis. “R” language gene ontology (GO) function and Kyoto encyclopedia of genes and genome (KEGG) pathway enrichment analyses were used to dock the core target genes with SAN with the help of AutodockVina. Cell proliferation was detected using MTT and xCELLigence real-time cell analysis. Apoptosis was identified via Hoechst 33342 staining, JC-1 mitochondrial membrane staining, and annexin V-FITC/PI double fluorescence staining, while protein expression was quantified using western blotting.A total of 95 SAN against NPC targets were obtained using target intersection, and 8 core targets were obtained by topological analysis and included EGFR, TP53, F2, FN1, PLAU, MMP9, SERPINE1, and CDK1. Gene ontology enrichment analysis identified 530 items, and 42 items were obtained by Kyoto encyclopedia of genes and genome pathway enrichment analysis and were mainly related to the PI3K/AKT, MAPK, and p53 signaling pathways. Molecular docking results showed that SAN had good binding activity to the core target. SAN inhibited the proliferation of NPC cells, induced apoptosis, reduced the expression levels of survivin and Bcl2, and increased the expression levels of Bax and cleaved caspase-8. It also decreased the expression levels of the key proteins p-c-Raf, p-MEK, and p-ERK1/2 in the MAPK/ERK signaling pathway in NPC cells.SAN inhibits the proliferation and induces the apoptosis of NPC cells through the MAPK/ERK signaling pathway. Scholars believe that the occurrence of NPC is the result of a combination of factors, which is related to family genetic susceptibility, EB virus infection, environmental factors, and eating habits. The most common site of NPC is in the pharyngeal recess. Because of its hidden location and the lack of specificity of early symptoms, NPC is not diagnosed in time, its metastasis rate is high, and most of NPC cases are poorly differentiated carcinomas, making its prevention and treatment more difficult. NPC is sensitive to radiation and radiotherapy is the first choice for its treatment. Simultaneous chemotherapy can enhance the antitumor effect and reduce the metastasis and recurrence rates.,3 However, radiotherapy and chemotherapy have limitations. The side effects caused by this treatment, such as dry mouth, mucosal injury, and nerve injury, result in significant levels of pain and lead to a decline in quality of life. Local recurrence and metastasis are still the main causes of failure in the treatment of NPC. More and more studies have shown that natural small molecular compounds demonstrate efficacy in the treatment of tumors with low levels of side effects and high safety. The discovery and application of these drugs are expected to provide a new treatment plan and perspective for tumor treatment. Sanguinarine (SAN) is a phenanthrene alkaloid extracted from plants, such as Macleaya corydalis and Corydalis. Studies have found that SAN has a wide range of pharmacological effects, which can activate the AMPK/Smad3 signaling pathway to promote MC3T1-E1 cell differentiation, stimulate bone growth, treat osteoporosis, inhibit activation of the P38 MAPK signaling pathway, relieve neuropathic pain, reduce the pathogenicity of Magnaporthe oryzae, inhibit the MAPK signaling pathway, and affect the release of inflammatory mediators.At present, more and more attention has been paid to the antitumor research on SAN, which covers many aspects, such as signaling pathways and microRNAs.,11 SAN has therapeutic effects on lung adenocarcinoma, breast cancer,,14 ovarian cancer, thyroid cancer, colorectal cancer, and gastric cancer. It also inhibits the proliferation of non-small cell lung cancer, blocks the cell cycle, and induces apoptosis by changing the content of STAT3. In addition, its mechanism is related to ferroptosis. Sarkhosh-Inanlou et al have reported that SAN reverses the sensitivity of cisplatin-resistant ovarian cancer cells to cisplatin by reducing the glutathione content. S. Zhang et al have shown that SAN has an anti-ovarian cancer effect via the long noncoding RNA CASC2/EIF4A3 axis, NF-κB, and PI3K/AKT/mTOR signaling pathways. It also affects the viability of triple negative breast cancer cells through CCL2 and the IKBKE/NF-κB/ERK1/2 signaling pathway. In hypoxic conditions, SAN inhibits the expression of HIF-1α in hepatoma cells, further inhibiting the secretion of TGF-β and epithelial stromal transformation induced by hypoxia. It has been reported that SAN can inhibit the proliferation, apoptosis, and invasion of NPC cells through the mTOR signaling pathway. However, research on its mechanism is still not comprehensive enough, which hinders its in-depth study and clinical application.Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors in the head and neck. Its incidence has obvious regional characteristics, mostly in southeast Asia, and a high incidence in southern China, affecting more males than females.,24 Based on this, the purpose of the present study was to use network pharmacology and molecular docking technology to explore the anti-NPC mechanism of SAN and to verify its antitumor activity and mechanism against NPC through experiments.Network pharmacology is a new strategy that integrates systems biology, high-throughput screening, and computer technology to study the complex relationship between “drug-target-disease” and accelerate the identification of drug targets. Molecular docking technique is a method to evaluate the interaction between organic small molecular ligands and target proteins, which can be used to determine the binding sites and binding forces of small molecules in natural products and target proteins.http://bionet.ncpsb.org.cn/ batman-tcm/index.php/Home/ Index/ index) as ID: 5154 and the target with score of > 20 was selected. The molecular structure was uploaded to the PharmMapper database (http://lilab-ecust.cn/pharmmapper/). Results with Norm Fit of >0.5 and z-score of >0 were selected, and the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) was searched using “Sanguinarine” in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (https://tcmsp-e.com/tcmsp. php) database and the corresponding formula for its molecular structure. The above results were combined to obtain the SAN targets. The results were corrected using the reviewed target from Swiss-Prot and Human in Uniprot data.SAN was uploaded to the pubchem BATMAN database  to obtain the intersection target of SAN in the treatment of NPC.Transcriptome datasets related to NPC were selected from the GEO database and included GSE68799, GSE118719, and GSE134884 (Table Interactions between target–target function-related proteins were obtained from the mapped intersection targets for NPC SAN treatment in the STRING (version 11.5) database. The selected minimum required interaction score was >0.700 and the PPI network map of target interaction and its tsv.data were obtained. The topological parameters, such as the median degree of freedom and the maximum degree of freedom in the network, were analyzed using NetworkAnalyzer in Cytoscape_v3.7.1. The core targets were selected based on 2 times the degree.P-value cutoff for enrichment was .05.R packages including “ClusterProfiler” (version 4.1.3) were used to analyze and visualize the GO and KEGG pathway enrichment of the module and core targets. Gene annotation information came from “org.Hs.e.g..Db” and https://pubchem.ncbi.nlm.nih.gov/), and the corresponding protein structure was extracted from the PDB database (https://www.rcsb.org/). The SAN structure was optimized using minimum energy MM2 position 3D Draw module in Chem Bio Office software and transformed into PDBQT using Raccoon software. Autodock Vina software-supporting tool MGLTools 1.5.6 was used to evaluate the proteins, hydrogenation, and Gasteiger charge, as well as merge nonpolar hydrogen and to perform other operations. The results were saved in pdbqt format to prepare for ligand docking.Molecular docking can predict the binding ability of proteins to small molecules. The SAN structure was obtained from PubChem database  twice. Then, 1 mL of Hoechst 33342 staining solution was added and incubated for 20 minutes in the dark. Next, the staining solution was removed, and the excess dye was washed with PBS. Finally, the cells were imaged using a multi-functional cell imaging system.The cells were digested, resuspended at a concentration of 1 × 105 cells/mL, and 1 mL/well of the cell suspension was added to a six-well plate. After the cells were attached, 2 mL of the drug-containing culture medium was added. The culture medium was removed after 24 hours and washed twice with PBS. Then, 1 mL of the JC1 staining solution was added and incubated for 20 minutes in the dark. The staining solution was then removed and the excess dye was washed out with PBS. The cells were imaged using a multi-functional cell imaging system.The cells were digested, resuspended at a concentration of 1 × 10After the intervention with SAN, the supernatant was collected in an Eppendorf (EP) tube and the adherent cells were digested with trypsin without EDTA. Then, 2 mL of the whole culture was added to terminate the digestion, the cell suspension was collected in an EP tube containing the supernatant, and the supernatant was discarded and transferred to a new 1.5-mL EP tube with PBS solution. The supernatant was removed by centrifugation and 100 μL of binding buffer was gently added, followed by 5 μL of annexin V-FITC and 5 μL of propidium iodide (PI) staining solution while avoiding light exposure for 15 minutes. Then, 100 μL of binding buffer was added to stop the staining reaction. The apoptosis rate was detected using a double fluorescence cell analyzer.After SAN treatment for 24 hours, the total protein was extracted and quantified. The protein samples were then added to the gel electrophoresis system, concentrated at a voltage of 40 V in the gel, and pressurized to 80 V after entering the separation gel at 200 mA of constant current. 2.5 hours transfer film and 5% skimmed milk powder closed for 1 hour. The primary antibody was incubated overnight at 4 ºC, and the secondary antibody was incubated at room temperature for 1.5 hours. The film was scanned using a two-color infrared fluorescence imaging system, and the band signal value was analyzed using ImageStudio.P < .05 was considered statistically significant. Results were graphically presented using GraphPad prism 8.0 software.SPSS 26.0 statistical software was used to process all of the data in the present study. The experimental data followed a normal distribution and were expressed as the mean ± standard deviation. Univariate analysis of variance was used for univariate design and comparisons of measurement data among multiple groups. The LSD method was used for multiple comparisons. A total of 7852 disease genes were extracted from the GEO database. Drug targets were searched in the SwissTargetPrediction database. A total of 449 SAN targets were obtained by removing duplicates and corrected using the Uniprot database. As a result, 95 intersection targets were identified Fig. A. At theThe mapped intersection protein was introduced into the Cytoscape 3.7.1, and the topological parameters of SAN anti-NPC target and function-related protein interaction network were calculated. The median degree of freedom for the target was 4.588, and the maximum degree of freedom was 19. Therefore, the range of core target screening conditions was set to 10–19, and 8 core targets were obtained, including EGFR, TP53, F2, FN1, PLAU, MMP9, SERPINE1, and CDK1  of SAN and Cis (4 μg·mL−1) for 12, 24, 36, and 48 hours, the proliferation of CNE2 cells was significantly inhibited , MMP9, and SERPINE1 was positively correlated with NPC metastasis and promoted the proliferation and invasion of NPC cells.–34 CDK1 plays an important role in the development of NPC. The results of molecular docking showed that there is good binding between SAN and the core targets, suggesting that the anti-NPC effect of SAN is related to the regulation of these core targets. The present study experiments showed that SAN can inhibit the proliferation and induce apoptosis of NPC cells. Bax and Bcl2 are the key factors determining apoptosis in tumor cells. The present results showed that SAN can reduce the expression of antiapoptotic protein Bcl2 and increase the expression level of proapoptotic protein Bax. Caspase-8 mainly mediates the mitochondrial apoptosis pathway. Prabhu et al and others have shown that SAN can promote the expression of cleaved caspase-8 and stimulate mitochondrial apoptosis in non-small cell lung cancer. Similarly, the present study experiments showed that SAN can increase the expression of cleaved caspase-8 in NPC cells, suggesting that SAN-induced apoptosis in NPC cells may be related to the mitochondrial pathway.In this study, 95 SAN targets against NPC were obtained based on the reverse targeting strategy. Eight core targets were identified via topological analysis, including EGFR, TP53, F2, FN1, PLAU, MMP9, SERPINE1, and CDK1. Recent studies have shown that EGFR plays an important role in promoting cell proliferation, cell protection, and apoptosis-mediated differentiation, and that EGFR expression is closely related to NPC prognosis. indicating that SAN can inhibit proliferation and induce apoptosis of NPC cells via the MAPK/ERK signaling pathway.GO enrichment analysis resulted in 530 items, and KEGG pathway enrichment analysis showed 42 items, which were mainly related to the PI3K/AKT, MAPK, and p53 signaling pathways. The MAPK signaling pathway is involved in the occurrence and development of NPC. The effect of SAN on the MAPK/ERK signaling pathway was verified and revealed that SAN can inhibit the expression level of the key proteins in the signaling pathway, including p-c-Raf, p-MEK, and ERK1/2, which was consistent with previous studies,The above analysis and argument indicate that sanguine is suitable for the preparation of anti-nasopharyngeal carcinoma drugs. Although the inhibitory effects of sanguine on nasopharyngeal carcinoma proliferation and induction of apoptosis have been clarified, the process of inducing apoptosis is still unclear. And this study has only completed network pharmacology and cell experiments, therefore, in further research, it is necessary to improve in vivo experiments and the entire regulatory network.SAN can inhibit the proliferation and induce the apoptosis of NPC cells via the MAPK/ERK signaling pathway. The core targets of this mechanism action include EGFR, TP53, F2, FN1, PLAU, MMP9, SERPINE1, and CDK1.Data curation: Wen-Qing Zhang, Ting Lin.Formal analysis: Fang-Liang Zhou.Funding acquisition: Jing-Ying Fan.Methodology: Ying-Chun He, Hong-Jian Shi.Project administration: Ying-Chun He.Resources: Xi-Ran Hu.Writing – original draft: Jing-Ying Fan, Jie Liu.Writing – review & editing: Le Tang, Hong-Jian Shi.


The pathophysiology of bone defects (BDs) is complex, and the treatment for bone defects, in particular massive bone defects, remains a major clinical challenge. Our study was conducted to explore the molecular events related to the progression of bone defects a common clinical condition.First, microarray data of GSE20980 were obtained from the Gene Expression Omnibus (GEO) database, where 33 samples in total were used to analyze the molecular biological processes related to bone defects. Next, the original data were normalized and differentially expressed genes (DEGs) were identified. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Finally, a protein–protein interaction (PPI) network was constructed and the trends of the different genes were confirmed.Compared with the samples of non-critical size defects (NCSD), the samples of critical size defects (CSD) had 2057, 827, and 1,024 DEGs at 7, 14, and 21 days post injury, respectively. At day 7, the DEGs were significantly enriched in metabolic pathways, at day 14 the DEGs were predominantly enriched in G-protein coupled signaling pathways and the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling pathway, and at day 21 the DEGs were mainly enriched in circadian entrainment and synaptic-related functions. The PPI network showed similar results. Quantitative real-time PCR (qRT-PCR) and western blot (WB) were performed to validate the partial results of sequencing.This study provides some clues about the molecular mechanism behind bone defects, which should contribute to scientific research and clinical treatment of this condition. Bone defects often occur as a result of trauma, the resection of tumors, infections, osteoporosis, and other factors , 2. MillPhysiologically, once the bone is damaged by mechanical injury, an inflammatory reaction is activated, and the repair cascade is initiated . AlthougThe transcriptome is known to reflect cellular pathophysiological information . In receThe transcriptome data of GSE20980 based on the GPL1335 platform (Affymetrix Rat Genome 230 2.0 Array) were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database.The raw data of the series were normalized using Robust Multichip Average (RMA) method and log2 transformed. Principal component analysis (PCA) was performed to visualize data variance. When different probes located the same gene symbol, we used the average to represent the gene expression level. In total, 31,042 gene chips were taken into consideration during the data processing.t-test was used to identify DEGs with an average fold-change of >2.0, and p < 0.05 was considered to indicate a statistically significant difference.We divided the data into six groups: CSD post-bone defects group vs. NCSD post-bone defects group at different times . The Student’s http://david.abcc.ncifcrf.gov/) software was used to identify the enriched pathways and biological processes of the DEGs by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional analyses , respectively . The biological processes of the genes and proteins were visualized through the Cytoscape (version 3.7.1) software platform, using 400 as the default confidence cutoff , and the cDNA was amplified using the HiScript II QRT SuperMix for qPCR . The qPCR was performed use a real-time 7500 PCR system  using AceQ qPCR SYBR Green Master Mix . All primer sequences are listed in Each bone callus was ground to fine particles with pestle and mortar in liquid nitrogen. Subsequently, the tissue was transferred to the EP tube for protein isolation. Equal amounts of proteins were separated via sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane. After blocked with 5% bovine serum albumin, the membrane was incubated overnight at 4°C with primary antibodies. The primary antibodies used were as follows: anti-phospho-JAK2 , anti-JAK2 , anti-phospho-Stat3 , anti-Stat3 , and anti-β-actin . Next, immunodetections were performed using the appropriate secondary antibodies , and the immunoreactive bands were visualized via the Tanon 4600SF System .t-test was used for comparisons between two groups. Differences between groups were considered significant at a value of p < 0.05.In all cases, data are presented as the means ± SEM from at least three independent biological replicates. GraphPad Prism 9.0.0  and SPSS software version 26.0  were used to conduct statistical analyses. Unpaired two-tailed Student’s Box plots of the CSD and NCSD data at different time points  before and after normalization are presented in p = 4.64 × 10−03), neuroactive ligand-receptor interaction (p = 2.52 × 10−02), and synaptic vesicle cycle , neuroactive ligand-receptor interaction (p = 1.33 × 10−02), fat digestion, and absorption , ECM-receptor interaction (p = 1.30 × 10−02), the TGF-β signaling pathway (p = 1.43 × 10−02), the RIG-I-like receptor signaling pathway (p = 2.18 × 10−02), and the PI3K-Akt signaling pathway , amino sugar and nucleotide sugar metabolism (p = 1.96 × 10−03), metabolic pathways (p = 4.35 × 10−03), protein export (p = 9.31 × 10−03), and purine metabolism , intestinal immune network for IgA production (p = 2.53 × 10−02), long-term depression (p = 3.90 × 10−02), the Janus kinase-signal transducer and activator of transcription (JAK–STAT) signaling pathway (p = 4.03 × 10−02), and the oxytocin signaling pathway , circadian entrainment (p = 1.35 × 10−04), synaptic vesicle cycle (p = 2.27 × 10−04), GABAergic synapse (p = 4.30 × 10−04), and neuroactive ligand-receptor interaction , system process (p = 4.07 × 10−13), and multicellular organismal process (p = 4.97 × 10−13). At other two time points, the upregulated DEGs were enriched in some development and cellular processes, which contained the multicellular organismal process (p = 5.18 × 10−12), single-multicellular organism process (p = 1.10 × 10−11), single-organism cellular process (p = 1.28 × 10−11), anatomical structure development (p = 1.10 × 10−09), and system development (p = 2.20 × 10−09) terms at 14 days, and multicellular organismal process (p = 1.79 × 10−16), single-multicellular organism process (p = 2.78 × 10−15), response to chemical (p = 2.45 × 10−12), reproduction (p = 6.58 × 10−08), and system process (p = 9.46 × 10−08) terms at 21 days.The top 5 GO terms  of the up- and downregulated DEGs are all summarized in p = 8.87 × 10−20), organic substance metabolic process (p = 2.42 × 10−18), metabolic process (p = 1.20 × 10−17), and mRNA metabolic process (p = 3.02 × 10−17) terms. At 14 days post injury, they were remarkably associated with the system process (p = 3.52 × 10−10), G-protein coupled receptor signaling pathway (p = 1.95 × 10−09), and neurological system process (p = 4.97 × 10−09) terms. Finally, downregulated DEGs were enriched in neurological and synaptic-related functions at 21 days post injury, which were anterograde trans-synaptic signaling (p = 1.02 × 10−09), trans-synaptic signaling (p = 1.06 × 10−09), synaptic signaling (p = 1.49 × 10−09), cell–cell signaling (p = 1.51 × 10−08), and chemical synaptic transmission (p = 2.60 × 10−08).At 7 days post injury, the downregulated DEGs were mostly enriched in the metabolic process, such as cellular metabolic process  were reduced at 7 days post injury , genes , and genCurrently, it remains a challenge to repair a variety of bone defects caused by various reasons. Therefore, it is essential to understand the molecular process of bone defect progression . BecauseIn this present study, there were 2,057, 827, and 1,024 DEGs at 7, 14, and 21 days post injury, respectively, at the site of new bone formation. These results indicated that we could focus on different changes in molecular events at these three time points. The majority of alterations in molecular events occurred at 7 days post injury. There were 1,754 downregulated genes at this time point. The KEGG analysis, GO enrichment analysis, and PPI analyses consistently showed that the downregulated DEGs were primarily associated with metabolic pathways and cellular metabolic process at 7 days after the bone injury. Previous research has found that inflammation plays an important role in fracture healing, and that interfering with any inflammation-related pathways or proteins will either promote or inhibit fracture healing , 23. A pIn this study, we found that the G protein-coupled signaling pathway and JAK–STAT signaling pathway played key roles at 14 days after the bone injury. The largest family of cell surface molecules involved in signaling consists of G-protein coupled receptors (GPCRs), which play key physiological roles, and their dysfunction leads to several diseases, such as osteoporosis, osteoarthritis, and ischemic stroke seizures , 27. GPCNotably, numerous downregulated DEGs were enriched in neurological and synaptic-related functions at 21 days post injury. Previous studies have shown that neurological damage  can accelerate fracture healing , 38, andIn recent years, a growing number of studies have focused on the effects of circadian rhythms on bone homeostasis . CoincidA key limitation of our study was that we had no raw data at 4 weeks, 5 weeks, or more subsequent time points post injury, limiting the information obtained regarding the molecular progress. Furthermore, there is still controversy over the definition of CSD, as the classical theory holds that this defect cannot be completely healed throughout the animal’s natural life. Another definition is that this time period is the entire experimental process. This involves the choice of the diameter of the defect. Additionally, we had only performed a vertical comparison by time-point grouping, and we did not find some transcriptomes that have an impact throughout each time point.Notwithstanding, the present study revealed that three sets of molecular processes  served important roles in the progression of bone defects. These related genes may provide new insights into the treatment of orthopedic diseases, like bone defects, non-union, and fractures, or even to address systemic conditions, such as skeletal disorders and osteoporosis. However, the roles of these molecular processes in the progression of bone defects still need to be confirmed by further studies.https://www.ncbi.nlm.nih.gov/geo/, GSE20980.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: SZ and GY designed the present study. HL, YL, XZ, and JY performed the experiments, data analysis, statistical analysis, and wrote the manuscript. HL, YL, XZ, CZ, ZZ, and SD were involved in the revision of the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation of China .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


B)-resistant Trueperella pyogenes from a dog revealed a new 23S ribosomal RNA methylase gene erm(56). Expression of the cloned erm(56) confers resistance to MLSB in T. pyogenes and Escherichia coli. The erm(56) gene was flanked by two IS6100 integrated on the chromosome next to a sul1-containing class 1 integron. GenBank query revealed additional erm(56)-containing elements in another T. pyogenes and in Rothia nasimurium from livestock.Whole-genome sequence analysis of a macrolide, lincosamide, streptogramin B (MLSerm(56) flanked by insertion sequence IS6100 was identified in a Trueperella pyogenes isolated from the abscess of a dog and was also present in another T. pyogenes and in Rothia nasimurium from livestock. It was shown to confer resistance to macrolide, lincosamide, streptogramin B antibiotics in T. pyogenes and E. coli, indicating functionality in both Gram-positive and Gram-negative bacteria. The detection of erm(56) on different elements in unrelated bacteria from different animal sources and geographical origins suggests that it has been independently acquired and likely selected by the use of antibiotics in animals.A novel 23S ribosomal RNA methylase gene  Trueperella pyogenes, a commensal Gram-positive bacterium of the skin and mucous membranes of animals, can cause suppurative infections in multiple animal species and rarely humans (B) antibiotics in T. pyogenes have been associated with the presence of erythromycin ribosome methylase (erm) genes, specifically erm(B) and erm(X), that prevent the binding of the MLSB antibiotics to the 23S rRNA  elements  criteria (B methylase (Erm) (erm(B) and erm(X) as determined using previously described PCR assays (de novo assembled using Flye 2.9.1 (erm gene listed in the Nomenclature Center for MLSB Genes (https://faculty.washington.edu/marilynr/) (https://blast.ncbi.nlm.nih.gov) and erm(X) as subject sequence (GenBank accession number NC_005206) identified a novel putative erm gene. This gene encoded a 267-aa 23S rRNA methylase and showed the closest relatedness to the Erm(X) determinant of plasmid pAP2 from T. pyogenes with 58% amino acid (aa) and 54% nucleotide (nt) identity (erm(56) (http://faculty.washington.edu/marilynr/) (erm(56). This 153 bp upstream region contained the ribosomal binding sites and imperfect inverted repeats capable of folding into stem-loops which may play a role in the translational attenuation of the Erm(56) methylase  . The absR assays  promptedrilynr/) . GenBankidentity  and was rilynr/) . Putativrilynr/)  31 and 1ethylase .erm(56), a 1,078 bp region of strain 09KM1269 including erm(56) and promoter sequences was amplified by PCR using Q5 High-Fidelity DNA Polymerase  and primers erm56-SalI-F3 (5′-cacatgtcgacGCCATCACACTGCTTGTTTCAACGA) and erm56-SpeI-R (5′-cacagactagtATTTTTCTTGGCCCGCCTCCCCAGA) . The primers contained overhangs (lowercase) with restriction site sequences (underlined) to facilitate cloning into the SalI and SpeI restriction sites of pJRD215 (erm(56)-containing plasmid pJEM1269 was obtained in Escherichia coli DH5α after ligation and heat shock transformation and selection on LB agar plates containing 30 µg/mL kanamycin. Plasmid pJEM1269 was subsequently transformed by electroporation into susceptible strains of E. coli AG100A (ΔacrAB::KANR) (T. pyogenes 13OD0707 (GenBank accession number CP123403) using settings of 18 kV/cm, 50 µF, 246 Ω and a time constant of 10 ms   using elof 10 ms . E. coliE. coli and T. pyogenes strains were determined by broth microdilution using Mueller-Hinton broth supplemented with 5% lysed horse blood and 48 h incubation for Trueperella following CLSI recommendations (erm(56) was expressed from plasmid pJEM1269 in T. pyogenes 13OD0707, the MIC of MLSB antibiotics increased by more than 256-fold for erythromycin and clindamycin and by 16-fold for pristinamycin IA, while no difference was seen for the streptogramin A pristinamycin IIA as assumed for an Erm methylase. Increased MICs of erythromycin (64-fold) and clindamycin (8-fold) were also measured for AG100A containing pJEM1269, but no conclusion could be drawn for pristinamycin IA, for which the MIC was already high for the recipient strain and remained unchanged in the presence of erm(56) , clindamycin (lincosamide) , pristinamycin IA (streptogramin B), and pristinamycin IIA (streptogramin A)  of ndations  (Table 1erm(56) gene of T. pyogenes 09KM1269 was preceded by a hypothetical protein (hp) and integrated into the chromosome between two identical IS6100 elements located in the same orientation, each flanked by 14 bp inverted repeats . The IS6100-hp-erm(56)-IS6100 element was situated next to a class 1 integron containing the sulfonamide resistance gene sul1, which was also delimited by a copy of IS6100 (erm(56) associated with IS6100 in T. pyogenes strain TP1 isolated from bovine lung in China (GenBank accession number CP033902) and in Rothia nasimurium strain E1706032 isolated from duck brain in China (GenBank accession number CP056080) -IS6100 element was found on a large 27.4 kb fragment which was related to the Lactobacillus phage PLE2 (GenBank accession number NC031036) using PHASTER (6100 situated upstream of erm(56) in T. pyogenes TP1 had a cytosine deletion at position 164 leading to a frameshift and an early stop codon in the IS6100 transposase. In R. nasimurium E1706032, erm(56) was only preceded by one IS6100 and integrated next to tet(Z) and a class 1 integron containing aac(6')-Ib3, qacEΔ1, and sul1; this resistance element was delimited by two copies of IS6100 (The f IS6100 . GenBankP056080) . In T. pf IS6100 .6100 was detected by PCR using primers pointing outward from erm(56)  and subsequent Sanger sequencing (erm(56) gene could not be transferred by either filter mating  (GenBank accession number CP123400) or electroporation of genomic DNA (T. pyogenes 13OD0707 (GenBank accession number CP123403) using BHI agar containing 5% sheep blood (BHI-S) added of 50 µg/mL of streptomycin and 10 µg/mL of erythromycin for the selection of transconjugants and 10 µg/mL of erythromycin for the selection of electrotransformants.Although a circular conformation containing one copy of ISquencing , the ermr mating  into MLSomic DNA  into T. erm(56) could not be transferred in vitro, its detection on different elements in unrelated bacteria suggests that it has a potential for broader dissemination. Given its detection in bacteria from different animal sources and geographical origins, it is likely that it has been independently selected by the use of antibiotics in animals.Although


To explore the potential active ingredients and related mechanisms of Jiaotai Pill in the treatment of Type 2 diabetes mellitus (T2DM) based on network pharmacology and molecular docking. The main active components of Jiaotai Pills were obtained by TCMSP and BATMAN-TCM database combined with literature mining, and the targets of the active components of Jiaotai Pills were predicted by reverse pharmacophore matching (PharmMapper) method. Verifying and normalizing the obtained action targets by using a Uniprot database. Obtaining T2DM related targets through GeneCards, the online mendelian inheritance in man, DrugBank, PharmGKB and therapeutic target databases, constructing a Venn diagram by using a Venny 2.1 online drawing platform to obtain the intersection action targets of Jiaotai pills and T2DM, and the protein–protein interaction network was constructed by String platform. Bioconductor platform and R language were used to analyze the function of gene ontology and the pathway enrichment of Kyoto Encyclopedia of Genes and Genomes. A total of 21 active components and 262 potential targets of Jiaotai Pill were screened by database analysis and literature mining, including 89 targets related to T2DM. Through gene ontology functional enrichment analysis, 1690 biological process entries, 106 molecular function entries and 78 cellular component entries were obtained. Seven pathways related to T2DM were identified by Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Jiaotai Pill can achieve the purpose of treating T2DM through multiple active ingredients, multiple disease targets, multiple biological pathways and multiple pathways, which provides a theoretical basis for the clinical treatment of T2DM by Jiaotai Pill. The Epidemiological survey of diabetes mellitus conducted by the Endocrinology Branch of the Chinese Medical Association in 31 provinces in China from 2015 to 2017 showed that the prevalence of diabetes mellitus among people aged 18 and above in China was 11.2%, and the main type of diabetes was Type 2 DM (T2DM). The incidence of T2DM is about 90%, and T2DM is often accompanied by metabolic syndrome such as hypertension, dyslipidemia and obesity. As a result, the risk and harm of complications of T2DM are significantly increased, and modern people are not alert to the harm of T2DM. At present, the preferred treatment for T2DM is western medicine, but long-term use of western medicine will lead to adverse reactions, such as liver and kidney damage, hypoglycemia or obesity in mild cases, and shock and cardiovascular and cerebrovascular diseases in severe cases. Therefore, modern medicine proposes that the treatment strategy for T2DM is comprehensive, including the control of blood sugar, blood pressure, blood lipids and weight. Modern pharmacological studies have shown that some traditional Chinese medicines (TCM) are rich in hypoglycemic active ingredients such as alkaloids, flavonoids, terpenoids and polysaccharides, which can be used to treat T2DM and reduce and improve its complications. In view of the multi-organ targeted damage of T2DM, the TCM compound has the advantages of multiple active ingredients and extensive effects, which can solve this problem. The TCM compound ingredients can intervene in the development and treatment of the disease through multiple ways, such as adding single target effect, synergistic effect on multiple targets and toxicity dispersion effect, and can effectively play a role in clinical treatment of reducing toxicity and enhancing efficacy.Diabetes mellitus (DM) is a metabolic disorder syndrome caused by the absolute or relative insufficiency of insulin secretion and the decrease of insulin sensitivity of the corresponding target tissues. It is characterized by hyperglycemia. Long-term hyperglycemia can cause multiple organ damage, such as cardiovascular and cerebrovascular, kidney, nervous system and eye. The cinnamon is pungent and sweet in taste and hot in nature, and has the effects of tonifying fire and supporting Yang, inducing fire to return to the primordial qi, dispelling cold and relieving pain, warming and dredging meridians, Modern pharmacological studies have shown that cinnamon can increase insulin sensitivity, improve the secretion level of glucagon-like peptide-1, further delay gastric emptying, and inhibit the activity of intestinal α-glucosidase to control the blood glucose level of T2DM patients. Modern studies have found that Jiaotai Pill is effective in the treatment of diabetes mellitus, and its mechanism of action is diverse, including the protection of islet B cells, the promotion of insulin secretion, and the improvement of IF. In addition, Jiaotai Pill has outstanding performance in the prevention and treatment of T2DM complications. However, the molecular mechanism of Jiaotai Pill is still unclear due to the numerous chemical components of TCM and the complex and diverse regulatory effects on the target, which is also one of the problems to be solved by researchers.The prescription and name of Jiaotai Pill are clearly recorded in Wang Shixiong, a famous doctor in the Qing Dynasty, in the chapter of “Brief Prescriptions of Four Departments, Anshen,” “Shengchuan Lian Wuqian, Cinnamon Heart Wufen. Jiaotai Pill is widely used in clinical practice. Besides the treatment of insomnia, modern physicians often use Jiaotai Pill to treat T2DM, nonalcoholic fatty liver and other diseases. Rhizoma Coptidis was first recorded in Shen Nong’s Classic of Materia Medica. It is bitter in taste and cold in nature. It has the effects of clearing heat and drying dampness, purging fire and detoxifying. Modern pharmacology has proved that Rhizoma Coptidis can reduce the blood sugar level of the body through its antioxidant effect. The network pharmacology of TCM clarifies the mechanism of action of drugs and their components by constructing the network of active ingredient-target, active ingredient-target-disease, active constituent-target-action pathway of TCM, which is suitable for the study of the mechanism of action of compound Chinese medicine.As a new mode of modern TCM research and development, network pharmacology of TCM contains the holistic concept of TCM, including the unique diagnosis and treatment ideas of TCM and rich clinical experience, which constitutes a research mode characterized by “network and “system.” Therefore, it is of great significance to screen the main active ingredients of Jiaotai Pill and explore its targets for the treatment of T2DM by means of network pharmacology, and to analyze the molecular mechanism of Jiaotai Pill in the treatment of T2DM by constructing the biological functions and pathway networks involved in the active ingredient-target, protein interaction, protein molecular docking and targets.Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSPDA) TCMSP is a platform and database for Chinese medicine research based on systems pharmacology, which contains the key information of Chinese herbal medicine in the Chinese Pharmacopoeia, such as composition, molecular structure, target of action and related diseases. It provides a basis for screening the active ingredients of TCM, studying the mechanism of drug action, broadening the therapeutic direction of ancient prescriptions and building new compound compatibility, and provides a new way for modern prescriptions and TCM research.https://old.tcmsp-e.com/tcmsp.php) database and BATMAN-TCM  Database (http://bionet.ncpsb.org/batman-tcm/) and other ways to collect the main chemical components of Coptis chinensis and Cinnamomum cassia, which constitute Jiaotai Pill, and then combined with literature data mining and data integration, the main chemical components of Coptis chinensis and Cinnamomum cassia were screened out according to the content of the components in the drug and related biological functions.Using TCMSP  platform, and finally the compounds were saved as mol2 format files.After oral administration, TCM needs to go through the process of absorption, distribution, metabolism and excretion to reach the target site and play a therapeutic role. Oral bioavailability (OB) is one of the most important pharmacokinetic parameters in absorption, distribution, metabolism and excretion. OB is an important index to determine the drug likeness (DL) index of active ingredients. Substances with OB ≥ 30% are considered to have high bioavailability; DL is used to estimate molecular druggability in drug design, and its index can be used for rapid screening of active substances, and substances with DL index ≥ 0.18 are considered to be highly druggable. and uploading the mol2 (*.mol2) format files of the screened active ingredients of Coptis chinensis and Cinnamomum cassia. The selected virtual protein targets of Jiaotai Pill were mined for gene target information, and UniProt database (https://www.uniprot.UniProtKB search function in org/), input the screened protein molecules, the database selection is limited to “Reviewed (Swiss-Prot),” the species is “Human,” query the target, and obtain the gene target of the active ingredient of Jiaotai Pill. The screening of T2DM disease targets was carried out by Gene cards (The Human Gene Database) (https://www.genecards./), online Mendelian inheritance in man database (https://www.omim.org/), Drug (DrugBank) database (https://go.drugbank.com/), Global pharmacogenomic information resource PharmGKB Database (https://www.pharmgkb.org/), therapeutic Target Database (http://db.Idrblab.Net/TTD/), and the higher the Relevance score in the Gene cards database, the closer the relationship between the target and the disease, so the target with Relevance score greater than the median was taken as the target of T2DM. The reported genes related to T2DM were searched by inputting the keyword “type 2 diabetes mellitus,” and the duplicate and false positive genes were excluded. Verified by literature retrieval, the finally obtained T2DM related target is matched with the Jiaotai Pill action target obtained by PharmMapper, so as to obtain the potential hypoglycemic action target of the Jiaotai Pill active ingredient. Through the online software Venny 2.1 mapping platform (https://bioinfogp.cnb.csic.es/tools/venny/index). The Venny plots of T2DM target screening and active ingredient-T2DM target of Jiaotai Pill were drawn respectively.The virtual protein targets were obtained by logging in the PharmMapper serverThe information of active components and targets of Jiaotai Pill was imported into Cytoscape V3.9.0 software, and the interaction network diagram of “Jiaotai Pill-active components-T2DM-targets” was constructed. Network Analyzer of Cytoscape V3.9.0 software was used to calculate the constructed network graph, and the degree of the network was analyzed by statistical analysis to screen out the core active components. (https://cn.string-db). Homo sapiens was defined as the species by using the multiple proteins tool, and the protein interaction was obtained. During the acquisition process, the minimum interaction threshold of the protein target was set as “highest confidence” (>0.9), and other settings were default settings. Hide the free protein in the result and save the TSV format file. The information of node1 and node2 in the file was imported into Cytoscape V3.9.0 software to draw the protein-protein interaction network, and the topology of the network was analyzed. At the same time, researchers found that the MCC algorithm of Cytohubba plug-in of Cytoscape V3.9.0 software has outstanding advantages in predicting disease targets. Therefore, in this study, the MCC algorithm was used to screen the core targets from the potential target data, and the core target data was visualized to construct the protein–protein interaction (PPI) network diagram.The target of Jiaotai Pill was introduced into the database of functional protein association networkshttp://bioconductor.org/biocLite.R) was used to query the ID of the related core target genes of Jiaotai Pill-T2DM. Install the Bioconductor platform with R language tools, adjust the parameters P and q value to <0.05, and then perform gene ontology (GO) on the core targets. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, selection of biological process (BP), cell component (CC). The 3 modules of CC and molecular function (MF) were used for GO enrichment analysis, and KEGG was used for pathway analysis. The pathways that were related to the pathogenesis of T2DM and enriched with most of the key genes were selected for target-pathway analysis, and the relevant pathway maps were drawn.The Bioconductor platform ” and “Human” were defined, and 262 gene targets were obtained.T2DM targets were predicted in GeneCards, online Mendelian inheritance in man, DrugBank, PharmGKB, therapeutic target database and other databases, and 6717 potential targets were obtained by screening the predicted targets and removing duplicates. At the same time, the screened T2DM targets and the active ingredient targets of Jiaotai Pill were mapped with the potential targets of T2DM by Venny 2.1 online drawing tool, and Venn diagrams  signaling pathway, endocrine resistance pathway, tumor necrosis factor (TNF) signaling pathway and Toll-like receptor signaling pathway are shown in Figure  The etiology and pathogenesis of T2DM are still unclear, but a large number of studies have shown that in addition to obesity, sedentary, high-calorie diet and population aging, IF, adipokine disorders, inflammatory response, oxidative stress and intestinal microflora abnormalities, immune disorders and mitochondrial dysfunction have become important pathological factors. Patients with T2DM have a higher risk of microvascular  and macrovascular  complications, most organ systems are adversely affected by complications associated with T2DM, and T2DM increases the risk of cancer. At present, cancer has become one of the main causes of death in T2DM patients.–27 At present, the main treatment strategies of T2DM are comprehensive, including the control of blood sugar, blood pressure, blood lipid and weight, antiplatelet therapy and lifestyle improvement. Medical nutrition therapy and exercise therapy belong to lifestyle management, which are the basic treatment measures to control hyperglycemia and persist for a long time. The drug treatment of hyperglycemia is to correct the impairment of IF and insulin secretion. According to the different effects, hypoglycemic drugs can be divided into drugs that promote insulin secretion and drugs that reduce blood sugar through other mechanisms. Western medicine hypoglycemic drugs have advantages in improving hyperglycemia-related indicators, but their way of action is single, some Western medicines have great side effects, while increasing the incidence of cancer and other diseases, the treatment results often do more harm than good. However, TCM treatment of T2DM can correct the disorder of glucose and lipid metabolism by protecting beta cells, promoting insulin secretion, improving insulin sensitivity, regulating the structure of intestinal microbiota and improving the function of immune system, which can regulate many indicators of T2DM patients and better improve their quality of life.As a chronic metabolic disease, T2DM is characterized by hyperglycemia, insulin resistance, insufficient insulin secretion, increased hepatic glucose production and lipid metabolism disorders.,31 Studies have shown that protoberberine compounds, including berberine, palmatine, jatrorrhizine and berberrubine, have hypoglycemic, hypolipidemic, anti-tumor, anti-inflammatory, anti-atherosclerosis and other effects. Among them, the hypoglycemic function of berberine is mainly to promote the regeneration of islet cells, inhibit hepatic gluconeogenesis, and increase the level of glucagon-like peptide-1, serum insulin and the number of islet beta cells in the intestine.–34 The active ingredient of Cinnamomum cassia Presl is also one of the hot researches in the treatment of diabetes in recent years. Its active ingredient EGCG has many beneficial effects such as preventing free radical damage, anticancer and antioxidant activity, improving glucose and lipid metabolism and protecting cardiovascular system. Meanwhile, foreign scholars have proved that EGCG can improve IF and enhance insulin sensitivity. Can control lipid metabolism, reduce body weight and that like, ECGC has a broad prospect in the treatment of type 2 diabetes. Kaempferol and quercetin, which belong to the same category as ECGC, are recommended as alternatives to the drug treatment of T2DM to prevent or improve T2DM and its related complications. Quercetin has anti-inflammatory, antioxidant, antihypertensive, anticancer, antiviral, and neuroprotective effects. It has strong anti-diabetic properties, can enhance the body’s glucose uptake and down-regulation of gluconeogenic enzyme activity in the liver, improve the function and proliferation of beta cells, and improve diabetic bone disease in T2DM patients. Kaempferol has anti-inflammatory, antihypertensive, lipolytic and anticancer properties, and its antidiabetic function is related to reducing pyruvate carboxylase activity, inhibiting hepatic gluconeogenesis, increasing the translocation and expression of glucose transporter 4, and improving insulin sensitivity by inhibiting proinflammatory cytokines. Stigmasterol also has the functions of anti-tumor, anti-inflammatory, antioxidant, cholesterol lowering and memory improvement, and also has a good anti-diabetic effect, which has potential benefits for T2DM. The above components have special effects on the treatment of T2DM, which indicates that the screening of the relevant active components of Jiaotai Pill in the treatment of T2DM has a reliable basis.The active ingredient-T2DM action target network of Jiaotai Pill Fig.  found th,40 Studies have found that CCND1 plays a role in obesity-related T2DM, which is related to its participation in fatty acid and glucose metabolic pathways and oxidation/reduction reactions; while CDK4/6 has a regulatory effect on human beta cell proliferation. Studies have found that CDK1 is the key to drive oxidative phosphorylation, and the abnormal increase in the secretion of oxidative phosphorylation components may be the basis for the development of diabetes. In the pathogenesis of T2DM, the increase or decrease of the above targets plays an important role in the occurrence, development and treatment of T2DM.Using MCC algorithm of Cytoscape V3.9.0 software to screen the core targets of Jiaotai Pill in the treatment of T2DM, CCND1, CDK4, CDK1, CDK2, CDKN1A, CDK6, CCNB1, and CDKN1C in the top 10 targets are the regulators of cyclin-dependent kinases. Modern studies have found that TP53, RB1, CCNB1 and CDKN1A have anti-diabetic activity.,45 Second, the AGE-RAGE axis is activated by signals transducing oxidative stress and inflammatory responses that damage vascular endothelial cells, renal mesangial/endothelial/smooth muscle/podocytes, and retinal pericytes, leading to the diabetic triad. Hyperglycemia promotes the formation of AGEs, and proximal tubular injury delays the catabolism and clearance of AGEs. This forms a vicious circle in the development of T2DM, but studies have confirmed that flavonoids and phytochemicals can act as AGE-forming inhibitors to block the AGE-RAGE signaling pathway. It has also been found that flavonoids enhance glucose uptake in peripheral metabolic tissues by protecting and proliferating pancreatic beta cells, improving their insulin secretion function, and improving the efficacy of insulin in preventing and alleviating obesity and diabetes by inhibiting inflammation, lipotoxicity, and oxidative stress.,47 The active ingredients of Jiaotai Pill act on the pathways mentioned above, on the 1 hand, it can inhibit the damage of inflammatory factors to pancreatic beta cells, and promote the proliferation of pancreatic beta cells, on the other hand, it can improve if and enhance insulin sensitivity. It can control lipid metabolism, reduce body weight and other multiple effects, and also has a good preventive effect on T2DM complications.KEGG pathway enrichment analysis was performed on the 89 selected intersection targets. It was found that the main pathways related to T2DM were age − RAGE signaling pathway, PI3K-AKT signaling pathway, endocrine resistance pathway, TNF signaling pathway and Toll-like receptor signaling pathway. Mitogen-activated protein kinase (MAPK) signaling pathway. Inhibition of PI3K-AKT signaling pathway is one of the mechanisms of IF, which is the main feature of T2DM, and chronic inflammation is also one of the important reasons leading to IF and T2DM. MAPK signaling pathway, TNF signaling pathway and Toll-like receptor signaling pathway play a vital role in inflammation, and studies have found that MAPK can regulate signaling pathways by phosphorylating target proteins, and can promote the absorption and utilization of glucose by up-regulating the expression of PI3K, AKT and glucose transporter-4. Nhance the sensitivity of the insulin receptor. Thereby improving insulin resistance, and TNF can trigger the activation of the MAPK pathway. Studies have shown that the level of TNF receptors in the circulatory system can predict the development of diabetes and the occurrence of complications. Moreover, intestinal flora can promote the absorption of active ingredients of Chinese herbal medicine. For example, the oral availability of quercetin is low, but some enzymes in intestinal bacteria and intestinal mucosal epithelial cells can convert quercetin and its derivatives into various metabolites such as phenolic acids, which are absorbed, transformed or excreted by the intestine. Researchers found that Jiaotai Pill could inhibit the secretion of inflammatory cytokines TNF-α and IL-6, promote the secretion of intestinal flora metabolites, and further promote the secretion of insulin. Therefore, the development prospect of Chinese herbal medicine in the treatment of T2DM should focus on the function of intestinal flora.Modern studies have found that gut microbial-derived metabolites can affect host obesity, insulin resistance and hormone secretion, thus jointly affecting the progression of T2DM. The regulation of insulin secretion is controlled by nutrients , endocrine peptides, hormones  and autonomic nervous system.To sum up, this study studied the mechanism of Jiaotai Pill in the treatment of T2DM by network pharmacology, and found that the active ingredients of Jiaotai Pill may be quercetin, EGCG, kaempferol, hydroberberine, stigmasterol, berberine, palmatine and so on. Combine with T2DM target proteins such as CCND1, CDK4, CDK1, CDK2, CDKN1A, RB1, CDK6, TP53, CCNB1 and CDKN1C, And further regulate that AGE-RAGE signal pathway, the PI3K-AKT signal pathway, the endocrine resistance pathway, the TNF signal pathway and the Toll-like receptor signal pathway as well as the insulin resistance pathway and the MAPK signal pathway of diabetic complications. Inhibition of T2DM formation through improvement of IF, enhancement of insulin sensitivity, inhibition of inflammatory response and oxidative stress and enhancement. The results show that Jiaotai Pill can treat T2DM through multiple active ingredients, multiple disease targets, multiple biological pathways and multiple pathways, which provides a theoretical basis for the clinical treatment of T2DM and a basis for broadening the clinical use of Jiaotai Pill.Conceptualization: Xiaona Chen, Chenggang Liu.Data curation: Xiaona Chen, Chenggang Liu.Formal analysis: Xiaona Chen, Chenggang Liu.Funding acquisition: Xiaona Chen.Investigation: Xiaona Chen.Methodology: Xiaona Chen.Project administration: Xiaona Chen.Resources: Xiaona Chen, Lin Du.Software: Xiaona Chen, Chenggang Liu.Supervision: Xiaona Chen.Validation: Xiaona Chen, Zhao Yang.Visualization: Xiaona Chen.Writing – original draft: Xiaona Chen, Zhao Yang, Lin Du, Yuxin Guan, Yunfang Li, Chenggang Liu.Writing – review & editing: Xiaona Chen, Zhao Yang, Lin Du, Yuxin Guan, Yunfang Li, Chenggang Liu.


Background and Objectives: Aucklandiae Radix is a well-known medicinal herb that is often used to treat gastric ulcer, but its molecular mechanism of anti-ulcer action is poorly understood. This research aimed to reveal the potential active components, core targets, and mechanisms of Aucklandiae Radix in treating gastric ulcer by combining network pharmacology and animal experimentation. Materials and Methods: First, a network pharmacology strategy was used to predict the main components, candidate targets, and potential signaling pathways. Molecular docking was then used to confirm the binding affinity between the main components and primary targets. Finally, rats were treated with indomethacin 30 mg/kg to establish a gastric ulcer model. Aucklandiae Radix extract  was pre-treated in rats by oral gavage for 14 days, and the protective effect and candidate targets of network pharmacology were validated through morphological observation, pathological staining, and biochemical index detection. Results: A total of eight potential active components and 331 predicted targets were screened from Aucklandiae Radix, 37 of which were common targets with gastric ulcer. According to the component–target network and protein-protein interaction (PPI) network, stigmasterol, mairin, sitosterol, and dehydrocostus lactone were identified as the key components, and RAC-alpha serine/threonine-protein kinase (AKT1), prostaglandin-endoperoxide synthase 2 (PTGS2), interleukin 1 beta (IL1B), caspase-3 (CASP3), and CASP8 were selected as the core targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment results revealed the pharmacological mechanism of Aucklandiae Radix against gastric ulcer related to many biological processes and pathways, including antibacterial, anti-inflammatory, prostaglandin receptor response, and apoptosis. Molecular docking verification showed that the key components and core targets had good binding affinities. In the in vivo experiments, Aucklandiae Radix notably relieved the gastric ulcer by reducing the levels of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and myeloperoxidase (MPO) while improving the gastric histopathological features. Conclusion: The overall findings suggest that Aucklandiae Radix treats gastric ulcer with a multi-component, multi-target, and multi-mechanism model.  Gastric ulcer (GU) is a high-incidence gastrointestinal disorder that can occur at any age, and about 10% of people worldwide suffer from GU . There aAucklandia Radix (AR), the dried root of Aucklandia lappa Decne, is a traditional herbal medicine in China. AR is warm in nature, and pungent and bitter in taste. Due to its effects of invigorating spleen and digesting food, promoting qi, and relieving pain, AR is widely used in the clinical treatment of digestive system diseases [In recent years, the use of plant-derived medicinal materials or extracts to treat GU has attracted the interest of researchers. diseases ,10. Withdiseases . These cdiseases , anti-indiseases , antitumdiseases , and prodiseases ,16. The diseases . In addiHerbal medicine has the characteristics of being multi-component and multi-target in treating diseases. With the advancement of bioinformatics, the research strategy of a compound–target interaction such as network pharmacology and molecular docking has been developed to facilitate the study of phytomedicine with complicated mechanisms ,18,19. Ahttps://old.tcmsp-e.com, accessed on 25 July 2022) [Pharmacopoeia of the People's Republic of China (ChP) [http://bionet.ncpsb.org.cn/batman-tcm/, accessed on 25 July 2022) [http://www.swisstargetprediction.ch/, accessed on 25 July 2022) [https://www.uniprot.org/, accessed on 25 July 2022) were eliminated; the duplicate genes were removed; and finally, the potential targets of AR were obtained. The chemical ingredients that met the conditions of oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18 in the Traditional Chinese Medicine Systems Pharmacy Database and Analysis Platform  , these tly 2022)   [https://omim.org/, accessed on 25 July 2022) [https://www.pharmgkb.org/, accessed on 25 July 2022) [http://db.idrblab.net/ttd/, accessed on 25 July 2022) [https://go.drugbank.com/, accessed on 25 July 2022) [Several databases including GeneCards (ly 2022) , OMIM (hly 2022) , PharmGkly 2022) , and Druly 2022)  were apphttps://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on 25 July 2022), and a Venn diagram was drawn to display visually.The common targets of AR and GU were obtained using Venny v2.1.0  . The deghttps://cn.string-db.org/, accessed on 27 July 2022) [The shared targets were retrieved using STRING (ly 2022) , and thehttps://metascape.org/gp/index.html#/main/step1, accessed on 27 July 2022) [p-value.In order to deeply understand the function of the previously obtained common targets of AR for the treatment of GU, the Metascape database (ly 2022)  was usedhttps://pubchem.ncbi.nlm.nih.gov/, accessed on 27 July 2022), optimized by ChemBio3D Ultra 14.0.0.117 software , and converted to PDB format. The crystal structures of targets were acquired from the RSCB PDB database  [The 2D structure of the compound was obtained from the PubChem database (ly 2022) . Compounly 2022) . The strThe dried root of AR materials (batch No.: 2205101) was obtained from Hainan Shounanshan Ginseng Industry Co., Ltd. . Identification of the AR was performed by an expert from the Identification Teaching and Research Office of Hainan Medical University. Indomethacin was obtained from Sigma Chemical Co. , (Lot# 088M4033V). Ranitidine (batch No.: 2101532) was bought from Foshan Chiral Pharmaceutical Co., Ltd. . Rat tumor necrosis factor-α , interleukin-1β , myeloperoxidase , and Prostaglandin E2  enzyme-linked immunosorbent assay (ELISA) kits were obtained from Jingmei Biotechnology Co. , Ltd. Hematoxylin-eosin (H&E) staining kits were purchased from the Nanjing Jiancheng Bioengineering Institute .w/v) 95% ethanol at reflux for 2 h twice, then we combined the filtrate, volatilized the ethanol at a low temperature, and concentrated in vacuum to obtain the final freeze-dried AR extract (ARE). The yield was 10.26%.The AR ethanol extract was prepared using traditional methods. Briefly, 500 g of material was extracted with 5 volumes of  were obtained from the Changsheng Biotechnology Company . Animal experimental procedures were reviewed and approved by the Ethics Committee of Inner Mongolia Minzu University . All animals were housed in standard laboratory conditions , and allowed free access to food and water. The animals were acclimatized for 1 week before conducting the formal experiments.n = 6): the control group , the model group (30 mg/kg dose indomethacin only), the ranitidine group (40 mg/kg dose ranitidine), and the ARE group  according to the clinically recommended dose [2) of gastric mucosa was measured by ImageJ-win64 software . The animals were divided randomly into 6 groups  solution was added, then ground on ice, and the mixture was centrifuged at 5000 rpm for 10 min to collect the gastric tissue homogenate. The levels of TNF-α, IL-1β, MPO, and PGE2 in the gastric tissue were determined by commercial assay kits according to the instructions. The overall workflow is shown in p < 0.05 or ** p < 0.01.All data were expressed as mean ± standard error of mean (S.E.M.) of the parameters. Statistical significances between the groups were analyzed by one-way ANOVA with the uncorrected Fisher’s Least-SignificantDifference (LSD) test using GraphPad Prism 8.0.2 software . Statistical significance was expressed by * In the TCMSP database, six components of AR that accorded with the conditions of OB ≥ 30% and DL ≥ 0.18 were obtained. Costunolide  and dehydrocostus lactone  were included together according to ChP though they did not meet the inclusion criteria. Taken together, a total of eight potential bioactive ingredients were used for the subsequent analysis, as shown in In total, three hundred and eleven, ninety-seven, nineteen, nine, and seventy-nine known disease targets related to GU were obtained from GeneCards, OMIM, PharmGKB, TTD, and Drugbank, respectively. After merging these targets and removing duplicates, a total of 359 GU targets were collected. Then, the 331 targets of AR and the 359 targets of GU were intersected, and 37 common targets were obtained, which were the potential targets of AR against GU .A compound–target network was constructed to clarify how the bioactive compounds of AR may act against GU. There were 47 nodes and 114 edges in the compound–target network, and the results are shown in The common targets of AR and GU were uploaded to the STRING database, and the PPI network was visualized through Cytoscape 3.8.0 software. There were 31 nodes (six free nodes were hidden) and 66 edges in the network. The top 10 key targets were screened according to the degree value: AKT1, PTGS2, IL1B, CASP3, CASP8, CYP2C19, NFKB1, MDM2, BCL2L1, and ESR1, which were speculated to be the core targets of AR for the treatment of GU .The common targets of AR and GU were uploaded to the Metascape platform to analyze GO and KEGG pathways, and a total of 491 GO entries and 102 KEGG pathways were obtained. A bar diagram  and bubbIn addition, molecular docking was performed to elucidate the interactions between the key targets obtained from the PPI analysis and the main bioactive components obtained from the network analysis. The docking results showed that these main components were combined well with the active site residues of key targets. As can be seen from the gastric morphology in p < 0.05). Among the three dose groups of ARE, the ARE 0.6 g/kg group had the lowest ulcer area along with the highest inhibition rate .The quantitative analysis of the ulcer area was consistent with the morphological observations . CompareThen, H&E staining was used to investigate the protective effects of ARE against GU . In the p < 0.01) and IL-1β (p < 0.01) and MPO, whereas it reduced the secretion of PGE2 in gastric tissues compared to the control group. Notably, the pre-treatment with ranitidine and ARE reversed the above alterations  and 10 core targets  of AR against GU were predicted through the component–target network and PPI analysis. Molecular docking simulations indicated that the predicted main bioactive ingredients and core targets had good binding affinity. The enrichment analysis of GO and KEGG revealed that the protective role of AR against GU involved various GU healing-related biological processes and signaling pathways. Meanwhile, the in vivo experiments showed that the ethanol extract of AR alleviated the GU induced by indomethacin, which was attributed to the regulatory effects of TNF-α, IL-1B, MPO, and PGE2.Medical plants have significant effects in the treatment of GU, which depend on their multiple chemical components. Therefore, it is particularly important to reveal the chemical components of natural plants. At present, the anti-ulcer effect and mechanism of many kinds of medicinal plants and their components have been revealed. For example, previous reports have recorded that the aged garlic and garlic oil extract enhanced the expression of PGE2, thus protecting the stomach injury caused by indomethacin and sodium taurocholate, and this effect may be related to the fatty acids and flavonoids abundant in garlic ,35. HoweAs an important part of the digestive system, the stomach has many special microbial community structures . GastroiWhen the stomach is infected by Helicobacter pylori, stimulated by ethanol or NSAIDs, neutrophils and monocytes may infiltrate the gastric mucosa, producing inflammatory cytokines such as TNF-α, IL-6, and IL-1β, which are the important factors causing gastric mucosal damage . IL-1β iProstaglandin (PG) is an important mucosal defense and repair mediator in the gastrointestinal tract, among which PGE2 is the most effective one. Cyclooxygenase (COX) is the key rate-limiting enzyme in PG synthesis . COX hasFurthermore, the balance between the proliferation and apoptosis of gastric epithelial cells is crucial for the integrity of gastric mucosa. Gastric ulcer will occur when various pathogenic factors disrupt this balance . StudiesIn summary, the present study analyzed the potential active components and mechanism of AR in the treatment of GU through network pharmacology, and revealed the multi-component, multi-target, and multi-pathway anti-ulcer mode of AR; in vivo experiments also showed that the AR extract can prevent indomethacin-induced GU, and its mechanism is related to the regulation of TNF-α, IL-1B, and PGE2. These findings provide important evidence for future research into the mechanism of AR against GU, and also provide an experimental basis for the further development and utilization of AR. However, due to the accuracy, integrity, and reliability of the database used in network pharmacology, the potential active ingredients and core targets obtained in this study may be different or missing. Therefore, in future experiments, we still need to further confirm the mechanism of these active ingredients against GU through a number of clinical trials and in vivo experiments.To sum up, AR treats GU through a multi-component, multi-target, and multi-mechanism mode: stigmasterol, mairin, sitosterol, dehydrocostus lactone, and other active compounds may exert anti-ulcer effects through AKT1, PTGS2, IL1B, CASP3, CASP8, and other targets, which are mainly related to mechanisms including the inhibition of bacteria and viruses, anti-inflammatory activity, regulation of PGE2, regulation of cell proliferation and apoptosis, and other pathways. Meanwhile, AR can prevent the damage of gastric mucosa induced by indomethacin and promote ulcer healing through reducing TNF-α, IL-1β, and MPO, and increasing the PGE2 in the gastric tissue. AR may be an effective multi-target anti-GU herbal drug.


However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to inaccurate and contradicting interpretations. For the lists of gene, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for the biological interpretation. But, GO analysis has not been achieved for metabolomics datasets. We present a new knowledgebase and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA knowledgebase covers 2,324 metabolic GO terms and associated 2,818 genes, 22,264 transcripts, 20,158 proteins, 1,482 EC annotations, 2,430 reactions and 2,212 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted over 250 GO terms being significantly overrepresented (FDR <0.05). On contrast, for the same metabolite list, MetaboAnalyst and Reactome Pathway Analysis suggested less than 5 pathways at FDR <0.05. We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not mentioned by alternative pathway analysis approaches. Overall, we suggest that metabolomics researchers should not limit the interpretation of metabolite lists to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a powerful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at  These metabolic genes function in a highly coordinate fashion to operate a metabolic network of linked reactions that provide energy, substrates, signaling and defense metabolites, as well as neutralizing foreign harmful chemicals1. Many metabolic processes such a glycolysis or purine biosynthesis are conserved across all domains of life2 and several such a steroid biosynthesis are specific to mammalians. Analysis of these metabolic genes, their products, and endogenous metabolites using omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, can identify metabolic processes that are important during different life stages in normal and adverse health conditions3. These molecular omics approaches let us discover new insights into how metabolic processes are altered by diseases, toxic exposures and baseline genetic variations, enabling new prevention and therapeutic strategies for human diseases7.Metabolism is a fundamental biological process of living organisms that transfers energy and matter among them, supporting adaptations and essential biological processes from the cell-cycle to reproduction. About 17% of annotated genes and their products (transcripts and proteins) in the human genome are involved in regulating and catalyzing metabolic processes through enzymatic transformations and transport mechanisms 8. Recently reported datasets can have between 1,500 to 2,000 named metabolites and several thousand unidentified metabolites9. These metabolites originate from overlapping pathways of catabolic and anabolic reactions and can also be biomarkers for metabolic processes10. Environmental, genetic, or biological factors can alter the regulatory, signaling, and enzyme kinetic mechanisms in one or more metabolic pathways and processes, leading to altered levels of related metabolites in cells, tissues or body fluids12. For example, aging reprograms carbohydrate and lipid metabolism pathways in the liver13, tobacco smoke exposure alters the nucleotide and reactive oxidative stress species metabolism14, and FADS gene polymorphisms alter the levels of circulating PUFAs15. We can expect to see a continuous growth in the number of named metabolites in metabolomics datasets due to new advances17 in analytical techniques and computational methods and resources.Metabolomics enables the simultaneous study of multiple metabolic processes, including pathways, transport, and reactions. Metabolomics assays are diverse and complex in terms of their analytical conditions, but they can generate quantitative and semi-quantitative data for hundreds of endogenous metabolites18. The first step in interpreting metabolomics datasets is to pre-process the data to remove any noise or artifacts that may affect the accuracy of the analysis19. This typically involves normalization, scaling, and filtering of the data. Once the data has been pre-processed, it can be analyzed using a variety of statistical and bioinformatics tools, including univariate and multivariate analysis, pathway and network analysis, and machine learning algorithms18. Pathway and network analysis can provide mechanistic insights into the biological pathways linked to the altered metabolites20. To gain a better understanding of the biological mechanisms underlying the metabolic alterations, metabolomics data can be integrated with other types of data, such as transcriptomics, proteomics, and genomics data3. This can provide a more comprehensive view of the underlying biological processes and their interactions. Interestingly, metabolomics datasets often have metabolites that are yet to be connected to a biochemical reaction and pathway22. To also include these poorly studied metabolites, hybrid approaches of the atomic mapping of reaction and chemical similarity network (MetaMapp) and enrichment analysis (ChemRICH) can be used22. A manual curation process to link metabolites in metabolomics datasets to biomedical literature is inefficient to cover the ever-growing volume of the literature. Therefore, an automated process to create the chemical to publication linking can be used to identify the prior publication that can support the mechanistic interpretation obtained from the pathway and network analysis23. Finally, it is important to validate the mechanistic interpretation using independent datasets or follow up experiments7 to provide further support for the underlying biological mechanisms.One of the key challenges in utilizing metabolomics datasets is how to interpret these large chemical lists for mechanistic insights10. A hypergeometric test is typically conducted for a background database dependent pathway analysis18. The background pathway information for these approaches can be obtained from Kyoto Encyclopedia of Genes and Genomes(KEGG)24, BioCyc25 and Reactome26, which are representative and curated biochemical databases18. In parallel with these pathway analyses, gene and protein lists are also often interpreted using gene ontology (GO) term enrichment analysis27, which covers terms that relate to pathways as well as other biological processes such as cell cycle or apoptosis, or even pathways that are not yet included in other biochemical databases. A GO term analysis can provide a comprehensive interpretation of an input list of genes, proteins, and metabolites. However, there is not yet a single tool developed that can perform a GO analysis for a metabolite list.To interpret a list of significant metabolites, protein or genes, in the context of functional and biological relationships among them, a pathway analysis approach is often used to find the pathways that are significantly over-represented in the input listhttps://goa.idsl.me/ site.We have developed a new tool named ‘IDSL.GOA’  to perform GO enrichment analysis for a list of metabolites. The tool is supported by a knowledge base representing a metabolic network consisting of genes, nucleotides, proteins, enzymes, reactions, and reactants (metabolites) that are directly sources from National Center for Biotechnology Information (NCBI), KEGG, Expasy and GO consortium databases. We present a case study of an aging mouse metabolic atlas to highlight the metabolic processes that were suggested to be related to the aging process and were only identified by the IDSL.GOA based GO analysis method. The online tool is available at We assembled and integrated information from a diverse set of data sources, including genes, transcripts, proteins, enzymes, reactions, compounds, atomic-pairs, gene ontology terms and the relationships among them. For the GO analysis, we employed an overrepresentation analysis (ORA) test using the hypergeometric distribution. This statistical test is a widely accepted method for determining whether a set of molecular entities (gene or proteins or metabolites) is significantly overrepresented in a particular biological pathway or process, given a background database. We also applied filters 1) overlap >= 3, 2) at least three genes in the GO process and 3) the overlap should be >5% of the total set size for a GO term. The overlap represents how many out of the input KEGG list are found among the KEGG identifiers linked with a GO term. These filters narrow down the list of GO terms to only the most relevant ones, ensuring that our analysis was focused and accurate for the relationships between metabolites and GO terms.phyper, where x is the overlap between the input list of KEGG identifiers and compounds linked with a GO term, y is the count of all compounds linked with the GO term, a is the count of all compounds not linked with the GO-term , b is the count of the KEGG identifiers from the input list that were found in the KB. By default, the phyper function in R calculates the probability of drawing less than or equal to x for a GO term. Use of the parameters “x-1” and “lower.tail=FALSE” returns the probability of drawing more than or equal to x for a GO term. The total number of KEGG identifiers linked with GO terms was 2,212. For example, for the Nucleoside salvage (GO:0043174), x was 13, y was 61, b was 62, and a was 2150  for the test study’s results. The p-value of this GO term was computed as ‘phyper’ which returns 4.218424e-09.We have used “phyper” function in R to compute the hypergeometric test. The parameter for the test were – The IDSL.GOA tool uses the False Discovery Rate (FDR) cutoff of 0.05 to control the proportion of false positives in multiple hypothesis testing in GO analysis. We repeated this test for all metabolically relevant 2,324 GO-terms.8, a comprehensive resource that provides information on the metabolites found in the different regions of brain of aging mice. Specifically, we compared the brain metabolome of the cortex region in an older female mouse against that of a young mouse. To identify the significantly different metabolites, we used the student t-test. To perform IDSL.GOA overrepresentation analysis, we needed to map the PubChem identifiers to KEGG identifiers. We obtained the necessary mapping information from the PubChem Identifier Exchange Service ( https://pubchem.ncbi.nlm.nih.gov/idexchange/idexchange.cgi ), which provides a convenient web interface for converting between various types of chemical identifiers. Using this tool, we converted the PubChem identifiers for the compounds in our test study to their corresponding KEGG identifiers which were used as input for IDSL.GOA analysis. Specifically, we used KEGG identifiers for the compounds that had a p-value of less than 0.05 in the student t-test. The same KEGG identifier list was used as input for a pathway analysis by Reactome26 and MetaboAnalyst28 tools.Our test study was based on publicly available data from the Aging Mouse Brain Metabolome Atlashttps://reactjs.org/), which is known for its efficient rendering of dynamic user interfaces. To facilitate data visualization, we utilized the Google Chart (https://developers.google.com/chart ) and Cytoscape JS plugins (https://github.com/plotly/react-cytoscapejs ), specifically designed to work with ReactJS. Google Chart enabled us to create interactive charts, while Cytoscape JS was instrumental in generating network diagrams that depicted the relationships among genes, transcripts, proteins, enzymes, reactions, compounds, and atomic-pairs. By leveraging these informatics tools, we were able to provide users with a seamless experience for analyzing metabolite lists. Cytoscape online version is a lightweight and user-friendly tool that allows users to perform basic network visualization and analysis tasks without the need to install the software locally. For small networks, the online version may be sufficient, but for larger and complex network, it is recommended to download the Cytoscape SIF (Simple Interaction Format) file and use the local version of Cytoscape software to create high resolution graphics. Instructions to use the IDSL.GOA tool are provided on the landing page.The online tool was developed using the ReactJS JavaScript framework , enzymes, reactions, compounds, and gene ontology terms. The source data for these relationships were obtained from various publicly available key databases, including the NCBI, Expasy – SIB Swiss Institute of Bioinformatics, KEGG, and the Gene Ontology Consortium . We rest26, IDSL.GOA KB had 210 more metabolites linked with metabolite pathways and processes. Overall, the IDSL.GOA database provided a comprehensive resource for performing GO over-representation analysis for metabolite lists.The linkages among these entities had a power-law distribution. In comparison to the Reactome databaseIn this study, we aimed to investigate the changes in metabolite levels in the brain cortex of old and young mice using a metabolomics atlas that contained close to 1,547 identified compounds, out of which 389 were linked to KEGG compound identifiers. We identified 557 metabolites that were significantly different between the old (59 weeks) and young (3 weeks) female mouse brain cortex . Out of Next, we used the reaction network visualization feature in IDSL.GOA to create a more accurate representation of the nucleotide salvage pathway (GO:0043174) . This re29. The primary analysis results are visualized in a ‘GO Ontology network’ graph using Cytoscape JS library, which provides an intuitive and interactive way to explore the data. This view is analogous to the pathway ontology visualization in the Reactome database26. The size of the node in the graph reflects the significance of the term, with larger nodes indicating more significant terms in the hypergeometric test. Additionally, an impact plot shows how specific the GO terms are for the input list, by plotting the set size versus −log. The explore option allows users to navigate the GO ontology tree and access GO-term-specific metabolic reaction network graphs. Clicking on a GO term in the main analysis, query or explore options provide the GO-term specific metabolic reaction network graph that contains genes, transcripts, proteins, enzymes, reactions and reactants. This reaction network feature is analogous to conventional pathway maps but here we create these maps automatically and have the flexibility to create new reaction layouts with the most comprehensive biochemical view a metabolic process  in a statistical test should be used as input. The Cytoscape SIF and node attribute files are useful for creating high-resolution figures in the Cytoscape desktop software process . The reaTo compare our IDSL.GOA results against existing approaches, we queried the same list of 96 significant KEGG identifiers against MetaboAnalyst and Reactome Pathway Analysis, two commonly used tools for metabolomics data interpretations. However, the results obtained from both tools were drastically different from those obtained using IDSL.GOA. First, on the FDR cutoff of 0.05, MetaboAnalyst identified only 4 pathways, and Reactome identified only 2 pathways. This result was likely caused by MetaboAnalyst’s use of a manually curated list of 80 pathways which may not concord with GO ontology, and which may have poor coverage for the input list. The most significant pathways for the input list were related to amino acid metabolism . It also9. This can lead to the identification of key regulatory pathways and molecular mechanisms that are involved in the observed changes and can guide further experimentation and hypothesis testing. By leveraging the new IDSL.GOA knowledgebase, we were able to identify the overrepresented metabolic pathways and processes in our caste study dataset and gain new insights into the underlying mechanisms that govern metabolic activity in aging brain tissue.IDSL.GOA is the first bioinformatics tool that used GO terms for over-representation analysis of metabolomics datasets. By mapping the metabolites to their associated GO terms, IDSL.GOA can improve the mechanistic interpretation of metabolomics data by providing a functional annotation of the metabolites based on their associated metabolic processes and pathways in the Gene Ontology database. It is a more sensitive and accurate tool for data with larger lists (>1000 named metabolites)30. Unlike pathway analysis, a GO analysis is not limited to hand-drawn pathway maps which tend to differ from one database to another, making it more flexible and adaptable to different experimental conditions. Depending on the background pathway database, the interpretation of metabolite lists can differ and may be inaccurate, leading to contradicting results and less impact10. On contrast, GO analysis allows for a more detailed and accurate interpretation of results, as it provides a broader context for the function and regulation of metabolite levels. Because the GO system is standardized, it allows for greater consistency and comparability between different studies and datasets. GO terms not only covers the known pathway maps but also covers additional metabolic processes that are not yet included in the pathway databases.There are several advantages of GO analysis over traditional pathway analysis. GO analysis provides a more comprehensive annotation system for genes and their products than pathway analysis, allowing for a broader range of metabolic processes and pathways to be analyzed22. The partial network visualization subset the GO term specific reaction network for only the compounds that were detected in the specific study or specimen being analyzed. This view can be useful for multi-omics integration as it shows genes, transcripts, proteins and metabolites all in one connected view.An innovative feature of IDSL.GOA is to create focused reaction network for metabolic GO terms that combines genes, transcripts, proteins, and reactions in one view. The feature enables creating pathway maps like diagrams for GO-terms in the Cytoscape software. By providing a clear and intuitive visual representation of the metabolic pathway, the focus reaction network visualization can facilitate data interpretation and hypothesis generation, and ultimately lead to a better understanding of metabolic function and regulation. The focused network used the atomic mapping of reactant pairs to create the graph, which provides a more accurate view of the metabolic reactionsThe IDSL.GOA tool is a free, user-friendly and web-based platform that utilizes Gene Ontology (GO) terms for the analysis of metabolomics data. It offers an intuitive interface that allows users to perform GO enrichment analysis for an input metabolite list. The tool has a range of useful features to facilitate the interpretation and has a wide range of capabilities, including query, explore, statistics, and download options. Additionally, IDSL.GOA offers a focused reaction network visualization for an in-depth mechanistic interpretation of metabolomics data. The use of GO terms provides an improved biological interpretation of metabolomics data, which can help researchers identify novel and metabolically relevant pathways and processes. The tool is built on a robust knowledgebase that contains relationships among metabolic entities, obtained from various sources including NCBI, Expasy, KEGG, and the Gene Ontology Consortium databases. The tool allows for a more comprehensive and accurate analysis of metabolomics data by identifying not only the predefined pathways but also relevant metabolic processes that are not included in the commonly used pathway databases. It is the first of its kind tool for metabolomics data.30. IDSL.GOA can be used for multi-omics integration by combining the results of gene expression analysis and metabolite profiling. This can be achieved by comparing the GO term enrichment results obtained from the separate gene expression and metabolite profiling analyses and identifying the common significantly enriched GO terms. This integration has not been achieved before IDSL.GOA since there was not a single knowledgebase developed that created the GO-term specific focused reaction networks. IDSL.GOA can also support the statistical multi-omics analysis to extract meaningful biological information from correlated features across multiple omics datasets31. By integrating the results of IDSL.GOA with the results of statistical multi-omics analysis, researchers can obtain a more comprehensive understanding of the underlying biological mechanisms that are involved in the disease or condition of interest. IDSL.GOA’s unique feature of creating a comprehensive reaction network with genes, transcripts, proteins, and metabolites all in one view, along with its ability to subset the view based on detected compounds, make it a powerful tool for multi-omics integration and reaction network visualization.GO analysis can be used to integrate different types of data, such as genomics, transcriptomics, proteomics, and metabolomics data, providing a more complete metabolic view of biological systemsFew limitations should be noted. The IDSL.GOA tool relies on the availability of KEGG-linked metabolite data, and the coverage of metabolite curation may vary across different metabolomics laboratories. Not all KEGG compound identifiers are linked to reactions and enzyme commission numbers. The GO hierarchy and associated annotations may contain biases or inaccuracies due to incomplete or outdated information. There is some redundancy in GO term names which may inflate the over-representation analysis results. The mechanistic interpretation still needs to be validated by additional experimentation. By discussing these limitations, we can provide a more balanced view of the capabilities and potential drawbacks of the IDSL.GOA tool for GO analysis in metabolomics.In summary, the IDSL.GOA tool can enable a comprehensive and accurate biological interpretation of metabolomics data. A much-needed transition from pathway maps to GO terms for interpreting metabolomics datasets can be supported by the IDSL.GOA tool. It is more sensitive in identifying significantly enriched GO terms that are relevant for metabolic processes. It also provides a powerful and user-friendly approach for integrating multi-omics data and identifying the biological pathways and processes. By providing a comprehensive view of the underlying biology, this approach can facilitate the identification of key regulatory pathways and biomarkers that may be useful for diagnosis, prognosis, and therapeutic targeting.Supplement 1Table S1 : Significantly different metabolites between the older vs younger brain cortex region.Table S2 : Full results for the Gene Ontology AnalysisSupplement 2Figure S1: IDSL.GOA impact plot with all labelsSupplement 3Figure S2: Nucleotide salvage GO metabolic process with all the molecular entitiesSupplement 4File S1: Gene Ontology Network for the over-represented metabolic processes.