We associated the mutation of the gene signature resulted from the stepwise Cox regression with the 1661 patients’ survival. Other 553 ICI-treated patients were collected from 6 cohorts for validation. We also performed this survival association in patients without ICI treatment from MSKCC as discovery (n = 2244) and The Cancer Genome Atlas (TCGA) as validation (n = 763). Pathway enrichment analysis were performed using transcriptome profiles from TCGA and IMvigor210 trial to investigate the potential mechanism.FGFR2, PIK3C3, FGFR4, FGFR1, FGF3, AKT1, mTOR, and RPTOR, resulted from stepwise Cox regression in discovery (n = 1661). In both discovery (n = 1661) and validation (n = 553), the mutation of the 8-gene signature was associated with better survival of the patients treated with ICI, which was independent of tumor mutation burden (TMB) and mainly attributed to the missense mutations. This survival association was not observed in patients without ICI therapy. Intriguingly, the mutation of the 8-gene signature was associated with increased TMB and PD1/PD-L1 expression. Immunologically, pathways involved in anti-tumor immune response were enriched in presence of this mutational signature in mTOR pathway, leading to increased infiltration of immune effector cells , but decreased infiltration of immune inhibitory M2 macrophages.We identified 8 genes involved in mTOR pathway, including These results suggested that mTOR pathway gene mutations were predictive of better survival upon ICI treatment in multiple cancers, likely by its association with enhanced anti-tumor immunity. Larger studies are warranted to validate our findings.The online version contains supplementary material available at 10.1186/s12967-022-03436-1. Immune escape of cancer cells driven by immune checkpoints, including PD-1 and CTLA4, yielded hot-spots to be targeted in the recent years . The tremTOR pathway was well recognized as simulators for cancer malignancy in multiple cancers. Recently, it was also known as key modulator for cancer immunity by its function in cell metabolism –13. DeleBecause of the relationship between mTOR pathway and cancer immunity, we hypothesized that genetic mutations in mTOR pathway may affect the cancer immunity microenvironment and predict the response to ICI treatment for cancer patients. To test this hypothesis, we performed a two-stage survival association study and identified an 8-gene signature in mTOR pathway whose mutation was associated with better survival in cancer patients received ICI treatment. Mutation of this 8-gene signature was associated with increased genetic alterations in DNA repair genes, which was a potential reason underlying its association with increased TMB. The TMB state did not alter the trend towards better response to ICI treatment in presence of the mutation in the mTOR pathway signature, highlighting its potential use in clinical practice independent of TMB. Importantly, this pathway-derived mutation caused enrichment in metabolism and anti-tumor immune pathways, leading to increased infiltration of immune effector cells in cancers. Together, mTOR pathway mutations in tumors may result in immunologically “hot” microenvironment sensitive to ICI treatment.We presented the workflow and summary of the present study in Fig. https://pathcards.genecards.org) and identified genes involved in mTOR pathway. We compared the MSK-IMPACT, PGDx elio Tissue Complete, and Foundation One, the three widely used next generation sequencing (NGS) panel in clinical trial of TMB, for the mTOR pathway genes involved. We next scanned the mutation information in our discovery dataset of Memorial Sloan-Kettering Cancer Center (MSKCC) study and identified genes with available mutation information for further survival analysis.We searched for PathCards database , anti-PDL1  and anti-CTLA4  agent. In the discovery, we included 1661 patients from a MSKCC cohort that investigated the association of TMB and ICI treatment . We incoFGFR2, PIK3C3, FGFR4, FGFR1, FGF3, AKT1, mTOR, and RPTOR. We calculated the mutation frequency of these 8 genes as well as their integrated mutation frequency across multiple cancer types. The MAF files of TCGA were also used to plot the mutation exclusion pattern among them.We extracted 48,834 cancer patients from 188 non-redundant studies deposited in cbioportal database, and further identified 44,078 patients with available mutation information in 8-gene signature in mTOR pathway, including We performed subgroup analysis according to the mutation types to explore its effect on patients’ survival upon immune checkpoint blockade. Because of the limited sample size in some rare types of mutations, patients were divided into those with missense mutation and others without this type of mutation. Then Kaplan–Meier plot with log-rank test, along with multivariate analysis  were used for survival comparison.We associated the mutation states of the 8-gene signature with TMB of 5646 cancer patients from MSKCC cohort and validated this association in TCGA including 10,163 patients. Wilcoxon rank-sum test was used to compare the means of TMB between two groups because of the abnormal distribution of the data. We also tested the survival association of the mutation states in low- (< 10 mut/Mb) and high-TMB (> 10 mut/Mb) patients treated with ICI in MSKCC cohort. In the validation of independent published studies for survival interaction with TMB, a cutoff of 10mut/Mb was also used in the next-generation sequencing study for TMB classification, and the upper quartile of TMB was used for classification in whole-exon sequencing studies. Because decreased sample size in subgroup division that may introduce statistical bias, we also pooled the results in discovery and validation by meta-analysis with random effects model.As we know, the activation or inactivation of mTOR pathway influence its target genes. To explore whether the mutations may affect the activation status of mTOR pathway, we associated the 8-gene signature mutation in mTOR pathway with mRNA expression of the well recognized target genes using Wilcoxon rank-sum test.To explain the relationship between 8-gene signature mutation in mTOR pathway and increased TMB, we asked whether there is an association between the signature mutation with genetic mutations involved in DNA damage response (DDR) pathway. Then, we extracted the transcriptome data to calculate immune phenotype  of cancer patients according to published TCGA studies , 27. WitWe reported one case with advanced lung squamous cell carcinoma in our center with AKT1 mutation, who was received two cycle treatment of 200 mg pembrolizumab upon initial diagnosis. The patient has signed a written informed consent to denote related clinical data for the purpose of scientific research. We got the computed tomography images before and after the pembrolizumab treatment, in order to visualize the treatment response.By extraction of sequencing data from blood TMB study , we inveP < 0.05 deemed as statistically significant.We performed all survival analysis and genetic analysis in the present study by R version 4.1.0, with two-side FGFR2, PIK3C3, FGFR4, FGFR1, FGF3, AKT1, mTOR, and RPTOR, that fitted an optimal survival model for patients treated with ICI  upon multivariate adjustment for age, sex, TMB, and treatment . However, compared with docetaxel, we found that mutant-type patients had prolonged overall survival (OS) . The validation for this association stratified by mutation types (C-D). Visualization in discovery (E) and validation (F) for the multivariate survival analysis in overall patients, missense mutation carriers, and other mutation carriers. Figure S2. Mutation frequency change of the DNA damage response pathway genes in presence of mutations in the 8-gene signature of mTOR pathway. The change of mutation frequency for each gene in DNA repair pathways in presence of the 8-gene signature mutation. Analysis was performed in TCGA (A) and was validated in MSKCC study (B), with frequency changes expressed as logarithmic ratio (mutant-type signature/wild-type signature patients). logarithmic P value indicated the level of significance, and those above the dashed line indicated statistical significance. Figure S3. Mutations and the activation status of mTOR pathway. Target gene of key mTOR pathway complement, including mTORC1 and mTORC2 (B). The association between the mutations in the 8-gene signature and mRNA expression of the target genes in response to mTOR pathway. Figure S4. Validation of the pathway enrichment analysis using transcriptome data from IMvigor210 clinical trial. Pathway enrichment analysis from IMvigor210 clinical trial using GSEA method, in the comparison of mutant-type versus wild-type patients for the 8-gene signature involved in mTOR pathway. We showed representative pathway enrichment involved in cancer immunity (A). All pathways enriched was summarized in one figure (B). We also summarized the enriched pathways overlapped with TCGA database using venn diagram and listed the overlapped in details (C). Figure S5. One clinical case with AKT1 mutation have good response to ICI treatment. An 82-year-old man with advanced squamous cell carcinoma of the lung was tested to have AKT1 mutation in tumors upon transbronchil biopsy. The tumor had shrunk by roughly two thirds after 2 cycle treatment of 200 mg pembrolizumab, as indicated by computed tomography images of plain film (A), mediastinal window (B) and pulmonary window (C). Figure S6. mTOR pathway mutations in circulating free DNA (cfDNA) and ICI treatment. Blood-based NGS was performed in OAK and POPLAR trial with comparison of ICI treatment versus chemotherapy, which was extracted for the mutation states of the 8-gene signature involved in mTOR pathway. The outcome comparison of atezolizumab versus docetaxel in patients with the 8-gene signature mutations in cfDNA, in terms of OS (A), PFS (B), and ORR (C) and DCB rate (D). Figure S7. The treatment outcome of patients with mTOR pathway mutations in circulating free DNA (cfDNA) in the subgroups stratified by TMB. The OS and PFS comparison of atezolizumab versus docetaxel in patients with the 8-gene signature mutations involved in mTOR pathway in cfDNA, with subgroup analysis in low-TMB patients (A-B) and high-TMB patients (C-D), respectively.Additional file 2: Table S1. Patients and studies included for discovery and validation. Table S2. Demographics of patients in discovery stage. Table S3. Demographics of patients in validation stage. Table S4. mTOR pathway genes with mutation data available for analysis in TMB and immunotherapy study from MSKCC as discovery. Table S5. Frequency of DNA damage pathway genes mutation in the comparison of mTOR pathway wild-type versus mutant-type patients using sequencing data of 1661 cancer patients from MSKCC immunotherapy study. Table S6. Frequency of DNA damage pathway genes mutation in the comparison of mTOR pathway wild-type versus mutant-type patients using sequencing data of 10182 cancer patients from TCGA. Table S7. Pathway enriched in the comparison of mutant-type versus wild-type patients for the 8-gene signature involved in mTOR pathway in TCGA cancer tissues. Table S8. Pathway enriched in the comparison of mutant-type versus wild-type patients for the 8-gene signature involved in mTOR pathway in IMvigor210 study.


Zishen Yutai (ZSYT) pill, a patent Chinese medicine, has been widely used in the treatment of infertility, abortion, and adjunctive treatment of in vitro fertilization (IVF) for decades. Recently, the results of clinical observations showed that premature ovarian failure (POF) patients exhibited improved expression of steroids and clinical symptoms associated with hormone disorders after treatment with Zishen Yutai pills. However, the pharmacological mechanism of action of these pills remains unclear.The compounds of Zishen Yutai pills found in blood circulation were identified via ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) technique in the serum of POF mice after oral administration of Zishen Yutai pills. The potential targets of compounds were screened using Traditional Chinese Medicine Systems Pharmacology Database, Traditional Chinese Medicine Database@Taiwan, Drugbank Database, PubChem, HIT, Pharmapper, and Swiss Target Prediction. The target genes associated with POF were collected from Online Mendelian Inheritance in Man Database, PharmGkb, Genecards, Therapeutic Target Database, and Genetic Association Database. The overlapping genes between the potential targets of Zishen Yutai pills’ compounds and the target genes associated with POF were clarified via protein-protein interaction (PPI), pathway, and network analysis.Nineteen compounds in Zishen Yutai pills were detected in the serum of POF mice after oral administration. A total of 695 Zishen Yutai (ZSYT) pill-related targets were screened, and 344 POF-related targets were collected. From the results of Zishen Yutai (ZSYT) pill-POF PPI analysis, CYP19A1, AKR1C3, ESR1, AR, and SRD5A2 were identified as key targets via network analysis, indicating their core role in the treatment of POF with Zishen Yutai pills. Moreover, the pathway enrichment results suggested that Zishen Yutai pills treated POF primarily by regulating neuroactive ligand-receptor interaction, steroid hormone biosynthesis, and ovarian steroidogenesis.Via virtual screening, we found that regulation of neuroactive ligand-receptor interaction, steroid hormone biosynthesis, and ovarian steroidogenesis was the potential therapeutic mechanism of Zishen Yutai pills in treating POF. Our study suggested that combining the analysis of Zishen Yutai pills’ compounds in blood in vivo in the POF model and network pharmacology prediction might offer a tool to characterize the mechanism of Zishen Yutai pills in the POF.The online version contains supplementary material available at 10.1186/s12906-022-03763-2. Premature ovarian failure (POF) is an ovarian dysfunction with cessation of menstruation before 40 years of age . POF proThe pathological causes of POF include genetic, autoimmune, therapeutic, and environmental stimulus, etc. . The preThe clinical diagnostic criteria of POF is amenorrhea together with elevated gonadotropin levels (follicle-stimulating hormone [FSH] > 20 IU/L), low estradiol (E2) levels (< 20 pg/mL), and low anti-Müllerian hormone (AMH) levels –< 0.5 ng/mL < 1 ng/mL) . POF aff ng/mL . Traditional Chinese medicine (TCM) has been widely used in treating gynecological disorders in Asian countries and has been proposed for treating POF . Zishen Zishen Yutai pills contained 15 herbal materials. The complexity of herbal materials made a difficulty in studying the synergistic effects of TCM prescriptions. The research strategy of UHPLC-Q-TOF-MS technology in analyzing the plentiful absorbed compounds in combination with network pharmacology via virtual screening has made the mission possible .In this study, the UHPLC-Q-TOF-MS analysis approach was used to identify the absorbed compounds of the Zishen Yutai pills in the POF mouse model in vivo. Next, network pharmacology analysis was performed to screen potential active ingredients, seek their targets for treating POF, and investigate the underlying pharmacological mechanisms. The combination of UHPLC-Q-TOF-MS and network pharmacology helped to build the pharmacology network for exploring the therapeutic mechanism of Zishen Yutai pills in the treatment of POF, and might lead to a full understanding of the interactions taking place between absorbed compounds of Zishen Yutai pills in vivo and potential targets involved in the POF treatment Fig. .Fig. 1ScSemen Cuscutae, Fructus Amomi Villosi, Radix Rehmanniae Preparata, Radix Ginseng, Herba Taxilli, Equus asinus L., Radix Polygoni Multiflori, Folium Artemisiae Argyi, Radix Morindae Officinalis, Rhizoma Atractylodis Macrocephalae, Radix Codonopsis, Cornu Cervi Degelatinatum, Fructus Lycii, Radix Dipsaci, and Cortex Eucommiae, comprising the Zishen Yutai pills (batch No. A00010) were provided by Guangzhou Baiyunshan Zhongyi Pharmaceutical Co., Ltd. The main ingredient of Cornu Cervi Degelatinatum was Ca3(PO4)2 and it has no botanical name [3-B-3113-98). For the experiments, Zishen Yutai pills were broken into powder, passed through an 80-mesh screen, and stirred uniformly in pilot facilities.Fifteen herbs, cal name . BotanicFemale C57BL/6 mice between 6 and 8 weeks of age were obtained from the HUNAN SJA Laboratories Animal Co., Ltd. The mice were housed in a temperature-controlled environment (22 ± 2 °C) under a 12/12 h light/dark cycle with a relative humidity of 60%. The mice were separated into five mice per cage for seven days to adapt to the environment. All mice were provided free access to chow and tap water.After one-week acclimatization, 20 C57BL/6 female mice were randomly divided into two groups: POF and POF + Zishen Yutai (ZSYT) pill, with 10 mice in each group. Mice in the POF and POF + Zishen Yutai (ZSYT) pill groups were treated with cisplatin to induce POF. The mice were intraperitoneally administered 2 mg/kg cisplatin  once a day for 10 days , 17. Cis− 1 body weight), whereas the mice in the POF group were orally administered with an equal volume of 0.3% CMC-Na solution. The Zishen Yutai pills’ powder was dissolved in 0.3% CMC-Na solution as an oral suspension. All mice were dosed twice daily for 3 days [After intraperitoneal administration for 10 days, the mice in the POF + Zishen Yutai (ZSYT) pill group were orally administered with Zishen Yutai pills , equipped with an ExionLC degasser, an ExionLC AD pump, an ExionLC AD autosampler, an ExionLC AD column oven, and an ExionLC PDA detector.− 1 and the column temperature was maintained at 40 °C. The gradient program was set as follows: 0–10 min, 5% B; 10-20 min, 5–10% B; 20 − 40 min, 10–25% B; 40 − 60 min, 25–30% B; 60 − 75 min, 30–40% B; 75 − 90 min, 40–75% B; 90 − 120 min, 75 − 90% B; 120 − 140 min, 90 − 100% B; 140 − 190 min, 100% B [A Hypersil gold C18  column was used to separate the extract components. Mobile phase A consisted of 0.1% formic acid (v/v) solution, and mobile phase B was acetonitrile. The flow rate was 0.4 mL min, 100% B . Data acMass spectrometry of serum samples was conducted on a SCIEX X500R QTOF mass spectrometer  equipped with an electrospray ionization (ESI) source connected to the UPLC system. Sciex PeakView™ 1.7. software  was used for data acquisition and processing.The analysis was performed in both positive and negative ESI ion modes. The information-dependent acquisition scan mode was selected, and the mass range was recorded at m/z 100–1500 . Calibrahttp://www.chemspider.com/), SCIEX-Standard Chinese medicine Mass Spectral Database, SCIEX metabolite Mass Spectral Database, and SCIEX lipids Mass Spectral Database [After data acquisition via UHPLC-Q-TOF-MS, compounds simultaneously with the same mass and equal fragment intensity between the results from the POF + Zishen Yutai (ZSYT) pill and POF groups were excluded. Next, the mass and fragments of the remaining candidate compounds were matched with our previously detected compounds from methanol extract of Zishen Yutai pills in vitro  and onliDatabase , 20. Thehttp://lsp.nwu.edu.cn/tcmsp.php), TCM Database@Taiwan (http://tcm.cmu.edu.tw/), Drugbank Database (https://www.drugbank.ca/), PubChem (https://pubchem.ncbi.nlm.nih.gov/), HIT (lifecenter. Sgst. cn/hit /), Pharmapper (http://www.lilab-ecust.cn/pharmmapper/), and Swiss target prediction (http://www.swisstargetprediction.ch/) [The relevant targets of the Zishen Yutai pills’ compounds identified in vivo were obtained from the Traditional Chinese Medicine Systems Pharmacology Database  . By inpuhttp://www.omim.org/), Pharmacogenomics Knowledge Base , Genecards (http://www.genecards.org/), Therapeutic Target Database , and Genetic Association Database .The target genes associated with POF were collected from the Online Mendelian Inheritance in Man Database  was used to filter possible protein-protein interactions (PPIs) [high values to large size” and “high values to dark colors” respectively on basis of the edge count for both settings [http://david.abcc.ncifcri.gov/) and Bioinformatics (http://www.bioinformatics.com.cn/), respectively [To explore the Zishen Yutai pills’ mechanisms of action in the treatment of POF, we investigated the pathways associated with POF treatment. First, the STRING database (s (PPIs) . The atts (PPIs) . Then, tsettings , 25. Thiectively –29 .. The candidate compounds were identified qualitatively using Sciex PeakView™ 1.7. software [The medicated serum samples were collected after the oral administration of Zishen Yutai pills in the POF mice, while the blank serum samples were collected after the oral administration of 0.3% CMC-Na solution in the POF mice. The both samples were analyzed via UHPLC-Q-TOF-MS. The peaks of candidate compounds in the medicated serum samples were extracted from both positive and negative ion chromatograms after excluding the same mass with equal fragment intensity in the blank serum Fig. . The cansoftware .Fig. 2Tohttp://www.chemspider.com/), SCIEX-Standard Chinese medicine Mass Spectral Database, SCIEX metabolite Mass Spectral Database, SCIEX lipids Mass Spectral Database, and our previously identified chemical profiles of Zishen Yutai pills in vitro.The MS/MS spectra of all candidate compounds were evaluated and matched with hits in the Chemspider  pill-related targets. In this study, the 695 Zishen Yutai (ZSYT) pill-related targets were screened from the TCMSP, TCM Database@Taiwan, Drugbank Database, PubChem, HIT, Pharmapper, and Swiss target prediction corresponding to the 19 compounds from the Zishen Yutai pills absorbed in vivo.Homo sapiens” proteins linked to POF were selected. We obtained a total of 344 genes.The POF-related target proteins were screened from OMIM, Pharmacogenomics Knowledge base (PharmGkb), GAD, Drugbank Database, and TTD. Only “(Table ). According to the overlapped genes, a total of 7 modulated pathways were found to be regulated by Zishen Yutai pills in the POF treatment. Both overlapped genes and modulated pathways were retained for network construction . The composed compound-target-pathway network consisted of 82 nodes and 229 edges which included 51 edges for pathway-target interactions and 178 edges for compound-target interactions  were obtained and selected from STRING databases for PPI network construction using Cytoscape software  pill-POF PPI network formed 45 nodes and 100 edges Fig.  after anP < 0.05) were shown in Fig. CITED2, SRD5A1, IGFBP2, OPRK1, PPARG, ESR1) and 5 genes  at the false discovery rate of 3.32E-04 and 3.94E-04, respectively. It also revealed that the absorbed compounds from Zishen Yutai pills might influence the steroid binding, steroid hormone receptor activity, and drug binding in MF. The steroid binding and steroid hormone receptor activity were identified as main regulation pathways in MF at the false discovery rate of 3.49E-06 and 3.49E-06, which influenced 6 genes  and 7 genes , respectively. Furthermore, the integral compound of plasma membrane, or plasma membrane at the false discovery rate of 7.72E-06 and 6.64E-04 in CC potentially involved in the therapeutic effect on POF via concurrently regulating ACVR2B, CCR3, CNR1, CRHR1, DRD2, DRD3, FGFR1, GABBR2, HTR1A, HTR2A, IGF1R, INSR, OPRD1, OPRK1, OPRM1, SLC6A3, SLC6A4, SSTR3, and TNF.GO enrichment analysis showed that the majority of these 56 targets were identified using the DAVID Bioinformatics Resources. There were 183 biological processes (BP), 24 cellular components (CC), and 62 molecular functions (MF) identified. The top 15 significantly enriched terms in the BP, CC, and MF categories (P < 0.05) were affected by the compounds absorbed from Zishen Yutai pills in the in vivo POF model, namely steroid hormone biosynthesis, ovarian steroidogenesis, cocaine addiction, cytochrome P450, neuroactive ligand-receptor interaction, serotonergic synapse, and dopaminergic synapse . These rhodopsin-like GPCRs include hormones, neuropeptides, neurotransmitters, and light receptors, transducing extracellular signals into specifically targeted cells . This prThree classes of steroid hormones—estrogens, progesterone, and androgens—are regarded as part of the ovary reproductive unit. These steroid hormones can be synthesized by the enzymes in the distinctive steroid-producing cells of the ovary. The enzymes are located in the mitochondria and endoplasmic reticulum and include five hydroxylases, two dehydrogenases, a reductase, and an aromatase . Among tINSR and IGF1R mice [Granulosa and theca cells work together to organize the biosynthesis of ovarian steroids. In the results of the predicted pathway, six potential targets, INSR, HSD17B2, AKR1C3 (HSD17B5), CYP19A1, CYP17A1, and IGF1R might be involved. INSR and IGF1R are important for follicular development, ovulation, and luteinization, as demonstrated by the conditional knockout of F1R mice . CYP17A1F1R mice . HSD17B2Because estrogen is essential for reproduction, estrogen therapy has become the mainstay of POF treatment . HoweverTherefore, our findings suggested that the Zishen Yutai pills treated POF primarily by regulating neuroactive ligand-receptor interactions, steroid hormone biosynthesis, and ovarian steroidogenesis resulting in improved levels and bioactivity of E2 and clinical symptoms of the hormone disorders in patients with POF , 12.In conclusion, our virtual screening results suggested that Zishen Yutai pills treated POF primarily by regulating neuroactive ligand-receptor interactions, steroid hormone biosynthesis, and ovarian steroidogenesis. The combining the absorbed compounds of Zishen Yutai pills in vivo in the POF models and network pharmacology prediction might offer a tool to characterize the mechanism of Zishen Yutai pills in the POF. However, our study has limitations since it has not been validated by additional animal experiments and clinical trials. The animal study for confirming the results of network pharmacology would be investigated in a future study.Additional file 1: Table S1. The edge count of the compound-target-pathway network for the absorbed compounds of Zishen Yutai pills by orally administered in a POF mouse model. Table S2. The tabular form of PPI result. Table S3. The target degree for PPI network results. Table S4. KEGG Pathway analysis of proteins regulated by the absorbed compounds from Zishen Yutai pills in the POF treatment.


Listeria monocytogenes (L. monocytogenes) is a serious food-borne pathogen that can cause listeriosis, an illness caused by eating food contaminated with this pathogen. Currently, the treatment or prevention of listeriosis is a global challenge due to the resistance of bacteria against multiple commonly used antibiotics, thus necessitating the development of novel green antimicrobials. Scientists are increasingly interested in microbial surfactants, commonly known as “biosurfactants”, due to their antimicrobial properties and eco-friendly nature, which make them an ideal candidate to combat a variety of bacterial infections. Therefore, the present study was designed to use a network pharmacology approach to uncover the active biosurfactants and their potential targets, as well as the signaling pathway(s) involved in listeriosis treatment. In the framework of this study, 15 biosurfactants were screened out for subsequent studies. Among 546 putative targets of biosurfactants and 244 targets of disease, 37 targets were identified as potential targets for treatment of L. monocytogenes infection, and these 37 targets were significantly enriched in a Gene Ontology (GO) analysis, which aims to identify those biological processes, cellular locations, and molecular functions that are impacted in the condition studied. The obtained results revealed several important biological processes, such as positive regulation of MAP kinase activity, protein kinase B signaling, ERK1 and ERK2 cascade, ERBB signaling pathway, positive regulation of protein serine/threonine kinase activity, and regulation of caveolin-mediated endocytosis. Several important KEGG pathways, such as the ERBBB signaling pathway, TH17 cell differentiation, HIF-1 signaling pathway, Yersinia infection, Shigellosis, and C-type lectin receptor signaling pathways, were identified. The protein–protein interaction analysis yielded 10 core targets . Molecular docking was used in the latter part of the study to verify the effectiveness of the active biosurfactants against the potential targets. Lastly, we found that a few highly active biosurfactants, namely lichenysin, iturin, surfactin, rhamnolipid, subtilisin, and polymyxin, had high binding affinities towards IL2, MAPK1, EGFR, PTPRC, TNF, ITGB1, IL1B, ERBB2, SRC, and mTOR, which may act as potential therapeutic targets for listeriosis. Overall, based on the integrated network pharmacology and docking analysis, we found that biosurfactants possess promising anti-listeriosis properties and explored the pharmacological mechanisms behind their effect, laying the groundwork for further research and development. Listeria monocytogenes (L. monocytogenes) is called “listeriosis” and is usually a result of eating food that has been contaminated with this food pathogen. In a wide range of food products, such as dairy products, raw vegetables, and raw meat, as well as ready-to-eat products, this bacterium has been found to be present [L. monocytogenes are a Gram-positive, rod-shaped, non-spore forming, non-capsule forming bacteria, which are motile at 10 to 25 °C [L. monocytogenes first encounters epithelial cells of the gut lining and then enters the host’s monocytes, macrophages, or polymorphonuclear leukocytes. The bacterium becomes blood-borne and multiplies both intracellularly and extracellularly. In pregnant women, it can migrate through the placenta to reach the fetus intracellularly [L. monocytogenes is infected in mice, the bacteria first appear in macrophages before spreading to liver hepatocytes [L. monocytogenes is capable of growing at refrigerated temperatures [More than 200 diseases can be caused in humans by food-borne contaminations, which are caused by a variety of factors that are involved with the cause and development of disease related to food consumption . In this present . The L. to 25 °C . They callularly . When L.atocytes . Severaleratures . L. monocytogenes in the food industry.There are several high-risk populations that are susceptible to listeriosis, including the elderly, pregnant women, newborns, and immunocompromised patients due to kidney transplant, cancer, HIV/AIDS, and steroid therapy . Around Over the past few years, natural products and their derivatives have been gaining more and more attention as insights into research and possible drug sources for targeted therapy, owing to their variety of structural features, multi-target action, and low toxicity . There hBiological surfactants (biosurfactants) are surface active compounds which are synthesized by the microbes (bacteria and fungi) on their cell surface or excreted that can reduce surface and interfacial tension . There iIn previous studies, biosurfactants have been demonstrated to have antimicrobial, antibiofilm, and anti-listeriosis properties ,14,15, sIn total, 15 biosurfactants were selected, and their detailed information was retrieved from the PubChem database in order to be analyzed, using the SwissTargetPrediction database . We predThe human genome database was used to collect the targets that are related to listeriosis. A total of 197, 276, and 211 targets were identified in the OMIM, DisGeNET, and GeneCard databases, respectively. As a result of removing duplicate entries from these three kinds of databases, a total of 244 listeriosis targets were obtained B. By intUtilizing the GeneMANIA tool, we imported 37 target genes in order to obtain a PPI network that demonstrates the relationships between these 37 target genes and other genes in the network. In the results, the percentage represents the weight that is given to interaction relationships in the network. According to our results, 29.46% of the interactions between the targets in the network resulted in co-expressions, and 35.77% of them resulted in physical interactions. Furthermore, there was a relationship between co-localization and shared protein domains A. In Tabp-value < 0.05, as screening conditions, a total of 1255 items were obtained pertaining to biological process, 149 items were obtained pertaining to molecular function, and 94 items were obtained pertaining to cellular composition. The hypothesis was put forth that biosurfactants could be involved in inhibiting listeriosis through the positive regulation of MAP kinase activity, protein kinase B signaling, ERK1 and ERK2 cascade, and the ERBB signaling pathway; positive regulation of protein serine/threonine kinase activity and leukocyte cell–cell adhesion; positive regulation of establishment of protein localization; and regulation of caveolin-mediated endocytosis via molecular functions such as integrin binding, phosphoprotein binding, protein tyrosine kinase activity, growth factor receptor binding, phosphatase binding, cytokine activity, NEDD8 transferase activity, and cadherin binding in cellular compartments such as membrane raft, membrane microdomain, focal adhesion, cell–substrate junction, myelin sheath, basal plasma membrane, and basal part of the cell. A total of 190 enrichment results were obtained from the KEGG pathway enrichment analysis. Among them Shigellosis, Yersinia infection, the ERBB signaling pathway, Th17 cell differentiation, the HIF-1 signaling pathway, the C-type lectin receptor signaling pathway, and bladder cancer pathways are closely associated with listeriosis and are in accordance with the enrichment results of GO. There was a significant abundance of KEGG pathways and gene pathways with p-values ≤ 0.05. Based on the Shiny GO platform, the first ten components were analyzed , molecular function (MF) and cellular component (CC) A–C, alonanalyzed D. Based Virtual screening using molecular docking is a computational method for identifying potential leads against predefined targets. By employing this method, compounds with appreciable binding affinities and specific interactions with target proteins were identified. A docking analysis of all the biosurfactants revealed the presence of several compounds with a significant affinity for the respective target proteins . The higL. monocytogenes has been identified as a human pathogen that has the potential to cause disease. There has been a demographic shift in the last few decades, and there has been an explosion of immunosuppressive medications used for treating malignancies and managing organ transplants. This has led to an increasing number of immunocompromised individuals who are at an increased risk for listeriosis [Over the past 80 years, teriosis . There iteriosis . A high teriosis .Clostridium perfringens, Bacillus subtilis, Staphylococcus aureus, etc. (Gram-positive bacteria); Escherichia coli, Enterobacter aerogenes, Salmonella Typhimurium, etc. (Gram-negative bacteria); and Mucor sp., Phytophthora capsici, Fusarium graminearum, Botrytis cinerea, and Phytophthora infestans (pathogenic fungi) [A number of biosurfactants have been tested for their antimicrobial activity, which has shown to be effective against different types of bacterial pathogens, such as c fungi) ,38. Therc fungi) .P. aeruginosa PA1 produces rhamnolipids that have antibacterial activity against L. monocytogenes ATCC 19112 and ATCC 7644. In addition to screening microbial surfactants for the treatment of listeriosis, the present study identifies a new therapeutic concept for further investigation into the mechanism of biosurfactants. For complex diseases such as listeriosis, in terms of predictive analysis, network pharmacology offers unique advantages [L. monocytogenes interacts with this tyrosine kinase receptor and E-cadherin, which might be a common pathogen invasion mechanism for the entry of L. monocytogenes [L. monocytogenes. As a result of L. monocytogenes infection, the heavy chain of non-muscle myosin IIA (NMHC-IIA) is phosphorylated at a specific tyrosine residue [Moreover, to date only one study is published on the anti-listeriosis activity of biosurfactants. According to the report published by de Araujo et al. , there ivantages . Analyziytogenes . SRC  receptor for adhesion to or invasion of mammalian cells [Listeria entry. Cell growth, autophagy, and actin cytoskeleton development are controlled by mTOR, a serine/threonine kinase that responds to growth factor stimulation and nutrient, energy, or oxygen availability. Further to this, MAPK family proteins are also reported to play a crucial role in the infection of L. monocytogenes, and therefore, the treatment of infection with MAPK inhibitors is reported to affect the inhibition of bacterial internalization towards the host cells during infection [L. monocytogenes infection. The dysregulation of TNF production and function has been reported to be associated with L. monocytogenes pathogenesis since it plays a crucial role in inflammation [L. monocytogenes infection in an experimental model, and it can also stimulate the production of IFN-γ [L. monocytogenes, it is also found to be involved in a T-cell-independent pathway that leads to macrophage activation [L. monocytogenes has been shown when TNF- or TNF Receptor 1 (R1)-deficient mice succumbed to the L. monocytogenes infection relatively quickly instead of recovering after a few days like the control mice [L. monocytogenes infections in the future.Bacteria can induce phosphorylation through direct contact with host cells or via soluble factors . In orde pathway . Pathogean cells ,49,50,51nfection . Additioammation . The proof IFN-γ . In expetivation . Moreoverol mice . All of Neisseria meningitidis to endothelial cells leads to the clustering of ERBB2 receptors, followed by phosphorylation of receptor tyrosine and activation of downstream signaling molecules, leading to actin polymerization and bacterial internalization [L. monocytogenes and other bacteria also trigger the activation of the ErbB2/ErbB3 heterodimer signaling pathway in order to invade host cells [L. monocytogenes hijacks the caveolin–endocytic machinery. The activation of these processes is mediated by a subset of caveolar proteins . Moreover, it is well-known that pathogens manipulate the post-translational modifications (PTMs) of host proteins to interfere with the normal functioning of host cells in various ways. A key target among these modifications is ubiquitin (UBI), ubiquitin-like proteins (UBLs), and neural precursor cell expressed developmentally downregulated protein 8 (NEDD8), which regulate pathways necessary for the host cell. The PTM modifiers, for instance, regulate the pathways that are crucial to the spread of infection, such as the entry, replication, propagation, or detection of the pathogen by the host, which have all been linked to these PTM modifiers. Different enzymes are involved in this biological process, as well as molecular functions, such as protein kinase binding activity, and the reactions are occurring in a variety of locations, such as the membrane and cytoplasm [Based on the GO analysis, possible targets for biosurfactants against listeriosis are involved in multiple important GO processes. Human epidermal growth factor receptor tyrosine kinases have a size of around 180 kDa and are a family of candidate tyrosine kinase receptors . This falization . The envlization . Likewisst cells . One morst cells ,65,66. Iytoplasm . There aAccording to the KEGG pathways analysis, potential targets of biosurfactants against listeriosis are significantly enriched in several important pathways, such as the ERBB signaling pathway, C-type lectin receptor signaling, Th17 cell differentiation and HIF-1 signaling pathway, etc. As described above, the ERBB signaling pathway plays an important role in listeriosis to invade bacteria in host cells. A key role that dendritic cells play in tailoring immune responses to pathogens is the expression of C-type lectin receptors (CLRs). Different signaling pathways are triggered by CLRs following the binding of pathogens, which are responsible for triggering the expression of specific cytokines that determine the fate of T cells during polarization. The activation of certain CLRs can be accompanied by the activation of nuclear factor-kappa B, while other CLRs can influence the activation of Toll-like receptors via signaling pathways. Depending on what signaling motifs are present in the cytoplasmic domains of CLRs, they can induce many different types of responses, including pro-inflammatory, antimicrobial, endocytic, phagocytic, and anti-inflammatory responses . Th17 ceListeria and other food-borne diseases.Furthermore, we performed docking experiments for the biosurfactants and the ten Hub genes in accordance with the “compounds-targets networks”. Additionally, the results of docking analyses confirmed our results and showed that lichenysin, iturin, surfactin, rhamnolipid, subtilisin, and polymyxin bind stably to the active pockets of target proteins. Therefore, these compounds could be considered for use as a potential treatment for listeriosis by inhibiting proteins such as, IL2, MAPK1, SRC, EGFR, PTPRC, TNF, IL1B, and ERBB2. Taking into account the role of network pharmacology, the present study examines the active biosurfactants, their potential targets, and their associated pathways, as they pertain to the treatment of listeriosis, which provides a theoretical foundation for further experimental studies. In consideration of the limitations of network pharmacology, it is only through data mining that the basic pharmacological mechanisms for the treatment of listeriosis can be identified. Currently, network pharmacology relies on a variety of databases to support the analysis of bioactive properties. Due to the fact that there are many different information sources and experimental data in databases, it is inevitable that they will show discrepancies. In spite of the fact that we have presented some interesting results, further research and clinical trials are required to evaluate the potential of biosurfactants to validate their usage as a prevention measure against http://pubchem.ncbi.nlm.nih.gov/), accessed on 13 September 2022. Using a public database, SwissTargetPrediction, and a STITCH database, we predicted the target of bioactive microbial biosurfactants based on the species of Homo sapiens as the only species targeted for the study. To complete the process of standardizing the target names, UniProtKB database (https://www.uniprot.gov/) was used [In the present study, we selected biosurfactants that have been reported to possess antimicrobial activity in the literature. The information about their structure, molecular weights, and canonical smiles and the corresponding sdf files were obtained from the PubChem database  for the creation of a compound–target network. To measure the topology scores of the nodes in the network, we used the CytoNCA plugin (v2.1.6), which measures betweenness, closeness, and the centrality of subgraphs of the nodes in each graph. Accordingly, the option “without weight” was selected [The biosurfactants that were collected and the effective targets that were identified were analyzed by using Cytoscape 3.9.1 software (selected . https://www.genecards.org/) [https://omim.org/), and gene–disease associations database  accessed on 20 September 2022 [https://www.UniProt.org/) was used to convert the target protein name to a gene name [A search for targets related to listeriosis was conducted by using keywords such as “listeriosis” and “listeriosis infection” in the GeneCards database (ds.org/) , Online ber 2022 ,76, and ene name . All lisIn order to detect the core target of the biosurfactants for the treatment of listeriosis, the prediction results of the biosurfactants were matched with the search results of the listeriosis related targets, and the target with the most overlap was selected as the core target. Using the FunRich Tool version 3.1.3, we mapped the targets that biosurfactants and listeriosis share. A Venn diagram was drawn in order to visualize the process. In order to construct a common target network, the Cytoscape software version 3.9.1 was used.The GeneMANIA tool, in addition to constructing a PPI network, is able to find a series of genes related to the input gene based on a large volume of function-related data and analyze the interaction between these genes, based on their co-localization and co-expression . In the https://maayanlab.cloud/Enrichr/). An analysis was conducted to explore the enrichment of GO functions and pathways within the human genome based on the species H. sapiens. As part of the functional analysis of GO, we considered biological processes (BPs), cellular components (CCs), and molecular functions (MFs). The data were visualized as histograms and bubble charts, using the SRPLOT application (http://bioinformatics.com.cn/srplot), as well as the ShinyGO 0.76.2 database (https://bioinformatics.sdstate.edu/go/).Based on the results obtained from the above screening, the target of biosurfactants that shared a common target with listeriosis was imported into the Enrichr database (https://bioinformatics.sdstate.edu/go/). Through the use of enrichment analysis, potential targets of biosurfactants for treating listeriosis, biological processes, and signaling pathways were defined by nodes in the network, and the interactions between these nodes were defined by edges.In order to perform a deeper analysis of the signal pathways, biological processes, and molecular functions, ten representative pathways were screened. As part of the construction of the target pathway/functional network, the ShinyGO 0.76.2 database was used , EGFR (PDB ID: 4WKQ), IL1B (PDB ID: 9ILB), IL2 (PDB ID: 1M4C), SRC (PDB ID: 4MXO), PTPRC (PDB ID: 5FMV), ITGB1 (PDB ID: 7CEB), ERBB2 (PDB ID: 3WLW), MTOR (PDB ID: 5WBU), and MAPK1 (PDB ID: 4G6O) were retrieved from RCSB PDB. Two-dimensional structures of selected biosurfactants were retrieved from the well-known organic compound database PubChem in SDF format. These compounds were then converted into three-dimensional structures, using Avogadro, and saved in PDB format .For the preparation of input files for docking, Autodock software 1.5.7  was usedThe binding sites of all protein structures were predicted by using Discovery Studio v. 21.1.0.20298 . The pocThree-dimensional structures of proteins derived from RCSB PDB were prepared for molecular docking, using AutoDock Tools . Before L. monocytogenes infection, indicating that biosurfactants may have a regulatory effect on listeriosis caused by L. monocytogenes. Furthermore, our findings indicate that IL2, MAPK1, EGFR, PTPRC, TNF, ITGB1, IL1B, ERBB2, and mTOR genes may be viable therapeutic targets for the reduction of listeriosis. In addition to providing an alternative or complementary therapy for the treatment of listeriosis, these findings lay the foundation for future studies. There are, however, some limitations to this study, as pharmacological and clinical research still needs to be conducted to verify our findings. A groundwork has been laid for further study of biosurfactants’ protective mechanisms and drug discovery applications based on network pharmacology.The purpose of this study was to investigate the molecular mechanisms of biosurfactants to treat listeriosis, using a network pharmacology approach and molecular docking. As a result of the current study, biosurfactants have been found to be capable of targeting multiple proteins and regulating multiple signaling pathways induced by


Sophora flavescens Aiton with significant antitumor, analgesic, and anti-inflammatory effects. However, the underlying mechanism of OMT on chronic itch is obscure, which limits clinical application. Hence, this study is aimed to clarify the pruritus alleviation mechanism of OMT by combining network pharmacology analysis, weighted gene co-expression analysis (WGCNA), and molecular docking. We screened 125 common targets of OMT regulating inflammation and pruritus with pharmacology technology, the GO enrichment function analysis and KEGG signaling pathway analysis to demonstrate the close relation to the signaling pathways regulating inflammation such as MAPK signaling pathway and PI3K-AKT signaling pathway. We adopted the most relevant templates for pruritus diseases, combined with network pharmacology to preliminarily screen out 3 OMT functions and regulatory targets, exerting a good connection and correlation with the target at the screened disease targets. Further experiments were conducted to explore the potential mechanism of OMT using the LPS-induced RAW264.7 cell inflammation model. The results showed that pretreatment with different concentrations of OMT  for 24 h, inhibited expression of IL-6, iNOS TLR4 and TGFR-1 as well as apoptosis of Raw264.7 cells induced by LPS. Moreover, OMT effectively inhibited LPS-induced MAPK pathway activation and the expression of related sites MAP2K1, MAPK8 MAP2K4, and MAPKAP-K2 in RAW 264.7 cells. The OMT also reduced the phosphorylation of p-38, associated with site in the activation of MAPK signaling pathway. These results could contribute to a better understanding of the mechanisms underlying how OMT alleviates inflammation to treat chronic pruritic diseases and provide a potential drug for the treatment of chronic itch.The treatment of chronic itch is considered to be a challenge for its non-histamine dependence and the search for alternative medicine is still striving. The pathology of the chronic itch is closely related to immune system regulation and inflammatory response. Oxymatrine (OMT) is a traditional Chinese medicine ingredient extracted from the roots of  Itching is a typical side effect of skin conditions, which was initially defined as a feeling that may inspire the desire to scratch by Samuel Hafenreffer in 1660 . ChronicAntihistamines, hormone medications, anticonvulsants, antidepressants, immunosuppressants, and other systemic therapy agents are commonly used to treat intractable pruritus in more severe cases. However, the majority of these medications have substantial side effects, such as sleepiness, gastrointestinal reactions, raised or lowered blood pressure, upper respiratory tract infection, mental problems, and so on . PatientSophora flavescens (SF) is a typical traditional Chinese medicine (TCM) widely used in China to relieve itch through oral administration in tablet, capsule and decoction, or topical application  (http://www.lilab-ecust.cn/pharmmapper/) (https://www.uniprot.org/)  (The molecular structure file of OMT was downloaded from PubChem (ih.gov/)  and uplomapper/)  databaseot.org/) . The repr.4.9.0) , and theHomo sapiens,” the PPI network map was created, and the minimum needed interaction score was set to a high confidence level of 0.07, with the discrete targets concealed. The PPI network diagram was then fine-tuned using the Cytoscape statistical tools (version 3.7.0)  .p values of 0.05 and an FDR of 0.25 were deemed statistically significant when the minimum gene set was 5 and the largest gene set was 5,000.We utilized the GO annotations of genes in the R package org for gene set functional enrichment analysis. Hs.eg.DB (version 3.1.0) as the background to mapping the genes to the background set using the R package cluster profile (version 3.14.3) to perform enrichment analysis to obtain gene set enrichment results. https://www.kegg.jp/kegg/rest/keggapi.html) to retrieve the most recent gene annotations from the KEGG Pathway as a backdrop for gene set functional enrichment analysis. To obtain the results of gene set enrichment in the background set, the enrichment analysis was performed using the R software package cluster profile (version 3.14.3). p values of 0.05 and an FDR of 0.25 were deemed statistically significant when the minimum gene set was 5 and the largest gene set was 5,000.We utilized the KEGG rest API (GSE120721 (GPL570) data were obtained from the GEO database. We deleted genes with a standard deviation of 0 in each sample, removed outlier genes and samples using the good samples genes method of the R software package WGCNA, built a scale-free co-expression network with WGCNA, and Pearson all paired genes were conducted. Correlation matrix and mean linkage method, then use the power function Amn = |Cmn|^β (Cmn = Pearson correlation) to construct a weighted adjacency matrix between Gene m and Gene n (Amn = the adjacency between Gene m and Gene n) . β was ahttps://www.rcsb.org/)  . The watsb.org/)  and prod2 incubator. The cytotoxicity of OMT on RAW264.7 cells was measured by CellCountingKit-8 . The cells were incubated for 24 h in 96-well plates. The cells were then co-treated for 6 h with different concentrations of OMT  . 10 µL CCK-8 was added to each well and cultured. The well’s absorbance at 450 nm was monitored for another 1–4 h. The feasibility of the project was calculated. The following is how it was calculated: Cell viability = [(As-Ab)/(Ac-Ab)] × 100%. As: Absorbance of the experimental well , Ac: Absorbance of control wells , Ab: Absorbance of the blank well .RAW264.7 cells  were grown in MEM-ALPHA culture medium  at 37°C in a 5% CO−ΔΔCt method was used to calculate the relative expressions of the relevant genes.Total RNA was isolated from RAW264.7 cells using Trizol reagent, and the collected RNA was analyzed using a spectrophotometer for concentration and purity. RNA with an absorbance ratio of roughly 2.0 (OD260 nm/OD280 nm) was chosen for qPCR and transcribed into cDNA using RT Master Mix for qPCR II. Using a qPCR PreMix (SYBR Green) Kit, quantitative real-time PCR (qRT-PCR) was used to quantify the expression of the mRNA.Primers used in the test are shown in 1× Binding Buffer, mix well, transfer to 96-well plate, 1 h Fluorescence detection was performed by flow cytometry. (Annexin V-FITC has a maximum excitation light of 488 nm and an emission light of 520 nm. 7-AAD has a maximum excitation light of 488 nm and an emission light of 647 nm).The apoptosis of RAW264.7 cells induced by LPS induced by OMT was detected by flow cytometry. The cells in the six-well plate were pretreated with OMT and then LPS was added for co-culture for 24 h. After that, the cells in each group were harvested and subjected to two experiments. Wash with PBS, resuspended in 100 μL 1× Binding Buffer, add 2 μL 0.5 mg/ml 7-AAD and 2 μL L Annexin V-FITC solution, incubate for 15min at room temperature in the dark, add 300 μLvia ImageJ.The procession of protein extraction in this study can be referred to the article . 20 µg pp-value of 0.05 or less was considered statistically significant, GraphPad Prism 7 software was used for graphical analysis and visualization.All data are presented as mean ± SEM of independent experiments. Statistical evaluation of the results was performed by one-way ANOVA and a OMT has a total of six pharmacophores, the structure of which is depicted in To understand the link between inflammation and itching, here we collected 10,868 “inflammation” related targets and found 7,466 reported pruritus-related genes inputting the keywords “pruritus” and “itch” and were used to screen OMT Co-acting targets of prospective targets through Venn diagrams as shown in The 125 target genes in p < 0.05) were selected listed in To reveal the action of OMT on inflammation and itching, the biological process (BP), cellular component (CC), and molecular function (MF) were analyzed. The biological processes, components, and molecular activities of common targets are the primary focus of GO enrichment analysis. A total of 125 common targets were enriched in BP, CC, and MF, and the top 20 with the highest relevance  MM in the black module was plotted in p values of the black and yellow panels are less than 0.001, indicating that they are highly correlated. Therefore, these two modules were selected for subsequent analysis.To accurately reflect the characteristics of itching, the atopic dermatitis clinical dataset GSE120721 from the GEO was adopted to show the differential gene analysis, and 2127 differential genes were obtained utilizing shown in When the 254 genes in these two modules were compared to drug-disease relevant genes, it was discovered that LCK, AMD1, and MAPKAPK2 shared three genes in common . KEGG isvia hydrogen bonds and π-π bonds interactions. AR, the docking protein with the highest score, attaches to amino acids like ASN705, LEU707/873, MET742/787, and PHE764 through hydrogen bonds and -bonds. MAPK8, MAPK14, and MAPK2K1, all of which are involved in the MAPK signaling pathway, had binding energies of −7.8, −7.9, and −8.4, respectively, indicating excellent binding . RAW264.7 cells’ apoptosis rate was decreased to varying degrees following pre-protection with different concentrations of 25, 50, and 100 μM OMT as compared to the LPS group. The proportions were 19.73 percent, 16.91 percent, and 11.77 percent, respectively, indicating that there was a dosage dependency. These results showed that OMT exerted a protective effect on LPS-induced inflammation.To investigate the protective effect of OMT on LPS-induced RAW264.7 cells, flow cytometry was applied for evaluation. RT-qPCR was used to identify the amounts of inflammatory components to validate whether the inflammation model was effective. As shown in To further verify the analysis results, we conducted the following experiments. MAPK14 (p38) is a major MAPK signaling pathway target gene and in the LPS-induced RAW264.7 cell inflammation model, the effect of OMT on the mRNA, protein, and phosphorylation levels of the p38 gene was investigated. As shown in Many skin disorders such as atopic dermatitis, psoriasis, dry skin diseases, liver diseases, kidney diseases, and metabolic diseases are characterized by significant itching sensations. These disease types are mostly non-histamine-dependent pruritus . PrurituMacrophages of innate immune cells play an important role in the development of chronic pruritus. Which contribute to histamine-independent pruritus through chronic inflammation and are one of the main reasons for maintaining chronic inflammation and neural sensitization. Although corticosteroids can be used for therapy, they are easy to abuse and long-term usage can result in major side effects such as skin thickening, substance metabolism issues, rebound after medication withdrawal, and even skin itching .Sophora flavescens and is commonly used for analgesia, anti-inflammatory, and anti-itching in the acute phase of the disease. According to previous research . MAPK kinase kinases, MAPKs kinases, and MAPKs are the fundamental components of the MAPK signaling pathway, which is largely conserved from yeast to humans. These three kinases can all be active at the same time to control cell development, differentiation, stress response, inflammatory response, and other important biological processes. Extracellular signal-regulated protein kinase (ERK), c-Jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (p38 MAPK), and extracellular signal Regulatory Kinase 5 (ERK5) are the four main branches of the MAPK pathway , the cell morphology improved to various degrees, and the morphological alterations of LPS-induced RAW264.7 cells were blocked, according to flow cytometry data. The fraction of apoptotic RAW264.7 cells dropped to varying degrees, and the LD content induced by LPS was greatly reduced. These results demonstrated that OMT exerted a protective effect on LPS-induced inflammatory apoptosis. In addition, OMT can inhibit the release of inflammatory factors and the expression of inflammatory factor receptors in LPS-induced RAW264.7 cells. The mitogen-activated protein kinase (MAPK) family includes p38 MAPK. P38 MAPK residues Thr180 and Tyr182 are phosphorylated by p38-specific MKK3 and MKK6 in the p38 MAPK signaling cascade. When p38 MAPK is phosphorylated, it undergoes a conformational shift, allowing the substrate to access the catalytic residue. The functions of the p38 MAPK signaling pathway have been mediated by more than 60 downstream substrates so far . P38 MAPRT-qPCR was also utilized to assess inflammatory factor levels, and the pre-protective impact of different doses of OMT considerably prevented the rise in the mRNA expression of inflammatory factor IL-6, INOS, and the inflammation relative receptor TLR4, TGF-beta1, TGFR-1 induced by LPS. Moreover, OMT also suppresses downstream of the MAPK signaling pathway-associated genes’ relative mRNA expression levels. The findings revealed that, in addition to the MAPKAPK-2 gene, OMT significantly reduced abnormally high mRNA expression of genes produced by LPS, such as TGF-beta1, TGFR-1, and MAPK8, MAP2K1, and MAP2K4.via the MAPK signaling pathway.Finally, the activity of phosphorylation sites in the MAPK pathway was examined and the facilitation of phosphorylation of P-38 protein induced by LPS was inhibited, indicating that OMT takes functional regulation of macrophages in vitro experimental results, suggesting that OMT may exert anti-inflammatory and antipruritic effects by affecting the MAPK signaling pathway, providing a reference for future mechanism research.OMT interacts with a multitude of targets and possesses anti-inflammatory and antipruritic properties that are multi-channel, multi-link, multi-target, and overall synergistic. In addition, the MAPK signaling network was mostly enriched when WGNCA, molecular docking, and KEGG pathway enrichment analysis were combined with


The coronavirus disease (COVID-19) is a pandemic disease that threatens worldwide public health, and rheumatoid arthritis (RA) is the most common autoimmune disease. COVID-19 and RA are each strong risk factors for the other, but their molecular mechanisms are unclear. This study aims to investigate the biomarkers between COVID-19 and RA from the mechanism of pyroptosis and find effective disease-targeting drugs.We obtained the common gene shared by COVID-19, RA (GSE55235), and pyroptosis using bioinformatics analysis and then did the principal component analysis(PCA). The Co-genes were evaluated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ClueGO for functional enrichment, the protein-protein interaction (PPI) network was built by STRING, and the k-means machine learning algorithm was employed for cluster analysis. Modular analysis utilizing Cytoscape to identify hub genes, functional enrichment analysis with Metascape and GeneMANIA, and NetworkAnalyst for gene-drug prediction. Network pharmacology analysis was performed to identify target drug-related genes intersecting with COVID-19, RA, and pyroptosis to acquire Co-hub genes and construct transcription factor (TF)-hub genes and miRNA-hub genes networks by NetworkAnalyst. The Co-hub genes were validated using GSE55457 and GSE93272 to acquire the Key gene, and their efficacy was assessed using receiver operating curves (ROC); SPEED2 was then used to determine the upstream pathway. Immune cell infiltration was analyzed using CIBERSORT and validated by the HPA database. Molecular docking, molecular dynamics simulation, and molecular mechanics-generalized born surface area (MM-GBSA) were used to explore and validate drug-gene relationships through computer-aided drug design.COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways(the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome complex, death-inducing signaling complex, regulation of interleukin production), natural immune pathways  and COVID-19-and RA-related cytokine storm pathways , TNF signaling pathway and regulation of cytokine-mediated signaling). Of these, CASP1 is the most involved pathway and is closely related to minocycline. YY1, hsa-mir-429, and hsa-mir-34a-5p play an important role in the expression of CASP1. Monocytes are high-caspase-1-expressing sentinel cells. Minocycline can generate a highly stable state for biochemical activity by docking closely with the active region of caspase-1.Caspase-1 is a common biomarker for COVID-19, RA, and pyroptosis, and it may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Minocycline may counteract cytokine storm inflammation in patients with COVID-19 combined with RA by inhibiting caspase-1 expression. In 2019, SARS-CoV-2-caused COVID-19 was recognized as a public health emergency of international concern (PHIEC) and subsequently identified as a pandemic by the World Health Organization (WHO) . SARS-Covia immunosuppression, and the use of biological agents  also raises the likelihood of viral infection in RA  . RA is oon in RA –57. TherPyroptosis is an emerging form of regulated cell death (RCD) and an active area of research . It is chttps://www.ncbi.nlm.nih.gov/geo/) (https://www.genecards.org/)  were screened using the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (ov/geo/) Table 1.ds.org/)  platformds.org/) , Zieglerds.org/) , and Jaids.org/) , respecthttp://www.bioconductor.org/packages/release/bioc/html/limma.html) . Co-genes were obtained from the intersection of COVID-19, RA-DEGs (GSE55235), and pyroptosis-related genes using the Venn-diagram package in R software and subjected to PCA.The empirical Bayesian method in the limma package (ma.html)  was usedFor the investigation of the pathway and function of the Co-genes, the R package “clusterProfiler”  was usedhttps://string-db.org/) (The STRING database (db.org/)  was utildb.org/) . It enabdb.org/)  is utilihttps://metascape.org/gp/index.html#/main/step1)   (Metascape (n/step1)  is a gennia.org)  is a webyst.ca/)  is a webhttp://www.swisstargetprediction.ch/) (http://ctdbase.org/) (https://go.drugbank.com/drugs/DB01017) (http://stitch.embl.de/cgi/input.pl) which are four databases to search for potentially related genes of minocycline. The STITCH database unifies drug-gene connections between more than 68,000 distinct compounds and 1.5 million genes; we utilize STITCH to visualize minocycline and target genes. COVID-19, RA-DEGs (GSE55235), pyroptosis-related genes, and minocycline-related target genes were intersected to determine the set of Co-targets. Subsequently, the Hub genes were intersected with the Co-targets to obtain Co-hub genes.CASP1, CASP3, and ILB in the hub genes were closely related to minocycline from NetworkAnalyst analysis. Therefore, minocycline was hypothesized to be an effective drug against this mechanism, and relevant validation was carried out. We used SwissTargetPrediction (ion.ch/) , CTD (htse.org/) , DrugbanDB01017)  and STITCo-hub genes were submitted to the NetworkAnalyst platform, TFs were obtained from the ENCODE database, and miRNAs were obtained from miRTarBase and TarBase. Visualization of TF-hub genes and miRNA-hub genes network using Cytoscape.To increase the reliability of the results as well as comprehensiveness, we included GSE55457 and GSE93272 as validation sets in this study. The intersection of the co-hub genes, RA-DEGs (GSE55457) and RA-DEGs (GSE93272), was identified as a key gene. Boxplot analyzed the expression of the key gene, and ROC  was usedhttps://speed2.sys-bio.net/)   is an uphttp://CIBERSORT.stanford.edu/) is a linear support vector regression-based methodology (https://www.proteinatlas.org/) contains data on the tissue and cellular distribution and expression abundance of nearly all human proteins. The HPA database was utilized to validate the key gene-immune cell associations to guarantee the accuracy of the results.The CIBERSORT algorithm (hodology  applied https://pubchem.ncbi.nih.gov/) (https://www.uniprot.org/) (https://www.rcsb.org/)  structure of minocycline was obtained in sdf format from the Drugbank database or the PubChem database (ih.gov/)  for use ot.org/)  (CASP1: sb.org/)  and downFurther investigation of the dynamic properties, stability, and structural flexibility of protein-drug complexes can be done by molecular dynamics simulations. It permits the examination of the interaction between the drug and the amino acid residues of the target protein and acts as an in-depth validation of molecular docking. MD to MDS and MM-GBSA calculations are a series of workflows for computer-aided drug design to study the properties of ligand-receptor interactions.+/Cl-, and the topology and parameter files required for the molecular simulation are then output. Optimization of system energy via a 2500-step steepest descent method and a 2500-step conjugate gradient method. The system was warmed up at 200 ps and stabilized from 0 K to 298.15 K, followed by a 500 ps NVT ensemble simulation and a 500 ps equilibrium simulation. The system was warmed up at 200 ps, from 0 K to 298.15 K, followed by an NVT system simulation  at 500 ps, followed by an equilibrium simulation  at 500 ps. The final NVT system simulation  was carried out for 100 ns. Other parameters: truncation distance set to 10 Å, collision frequency γ set to 2 ps-1, system pressure 1 atm, integration step 2 fs, trajectory saved at 10 ps intervals.AMBER 18 was used to examine the stability of the complexes by simulating the molecular docking of ligands and receptors using all-atom MDS of ligands and receptors. Before the simulation, the charge of the minocycline was determined using the HF-SCF (6-31G**) computation with the antechamber module and gauss 09 software. The GAFF2 small molecule force field and the ff14SB protein force field were utilized to describe, respectively, the ligand (minocycline) and the receptor (caspase-1) , 102. ThThe free energy of binding between receptor and ligand is calculated by the MM/GBSA method , 104. Thinternal : Internal energy, ΔEVDW : Van der Waals interactions, ΔEelec : Electrostatic interactions, ΔGGB and ΔGSA : solvation-free energy.ΔEhttps://github.com/zheng5862/COVID-19-RA.git).The flowchart shows all of our study’s key and important procedures Figure 12230 RA-DEGs were obtained from the GSE55235 dataset and visualized using volcano maps and clustered heat maps 3. Co-geGO analysis showed that the biological process (BP) was mainly enriched in the immune system process  pathway; and Signaling by Interleukins Table 2 Top 100, 92, 12, and 10 minocycline-related target genes from SwissTargetPrediction, CTD, Drugbank, and STITCH databases, respectively   was obtained by the CIBERSORT algorithm. CASP1 was positively correlated with monocytes, dendritic cells activated, and neutrophils by Pearson correlation coefficient analysis  depicts the movement of caspase-1 and minocycline; a greater value and amplitude of the RMSD suggests an intense movement and vice versa for a smooth movement. In bind) implies that the medication binds to the protein with affinity, whereas a smaller value indicates a greater binding capability. The binding energy of minocycline/caspase-1 was -21.43 ± 3.89 kcal/mol, showing that minocycline has a strong binding affinity for caspase-1. The energy decomposition reveals that van der Waals and electrostatic forces are the primary contributors to their binding , and pyroptosis-related genes enriched in NLR/TLR signaling pathway, NLRP3 inflammasome complex, death-inducing signaling complex, regulation of interleukin production and cytokine production involved in immune responses. The top 11 hub genes in Metascape were enriched in the network map of the SARS-CoV-2 signaling pathway, activation of the NLRP3 inflammasome by SARS-CoV-2, NLR signaling pathway, and interleukins signaling pathway. While they were enriched in GeneMANIA in inflammasome complex, IL production pathway, NF-κB signaling, TNF signaling, and regulation of cytokine-mediated signaling pathway. CASP1 was most involved in these enrichment pathways. Minocycline was found to be closely associated with CASP1 by NetworkAnalyst analysis. Therefore, based on bioinformatics analysis and further network pharmacology analysis, it was surprising to find that the 7 Co-hub genes obtained from the intersection of minocycline with COVID-19, RA (GSE55235), and pyroptosis were all contained in the top 11 hub genes of COVID-19, RA (GSE55235), and pyroptosis. One important TF (YY1) and two important miRNAs (hsa-mir-429 and hsa-mir-34a-5p) associated with CASP1 were obtained by TF-hub genes and miRNA-hub genes network. The key gene was validated by the GSE55457 and GSE93272 validation sets and obtained as CASP1, which was highly expressed in the RA group in all three datasets and validated with ROC for significantly good test performance. This gene coincided with the results of previous pathway analysis. SPEED2 analysis indicates that CASP1 is associated with the JAK-STAT signaling pathway. Immune cell infiltration analysis revealed that monocytes, dendritic cells activated, and neutrophils were able to express CASP1 at high levels, and the reliability of the results was verified by using the HPA dataset and Monaco dataset databases. Finally, the relationship between minocycline and caspase-1 was investigated and verified by MD, MDS, and MM-GBSA: minocycline can dock close to the active site of caspase-1 to form a highly stable state and exert the biochemical activity of the drug.In this study, COVID-19, the crossover genes between RA and pyroptosis were enriched in the NLR/TLR signaling pathway, NLRP3 inflammasome complex, death-inducing signaling complex, regulation of interleukin production, NF-κB signaling, and TNF signaling. These pathways are all closely related to the caspase-1-induced pyroptosis pathway.It is known that the innate immune system can recognize the viral pathogen-associated molecular pattern (PAMP) and host cell-derived damage-associated molecular pattern (DAMP) using the pathogen recognition receptor (PRR) –107. PRRNLRP3 inflammasome requires an initiation and activation pathway. The beginning step is the NF-κB-NLRP3 axis, in which the detection of PAMP/DAMP by a particular PRR  activates the NF-κB pathway, increasing NLRP3 expression , 135. DuTherefore, pyroptosis is a classical cytolytic type of PCD induced by caspase-1 . The pyrIn this study, the top 11 hub genes pathways of COVID-19, RA, and pyroptosis were enriched in the Network map of the SARS-CoV-2 signaling pathway, Activation of the NLRP3 inflammasome by SARS-CoV-2, IL, NF-κB, TNF signaling pathway and regulation of cytokine-mediated signaling pathway. Caspase-1 activation is not only a critical effector molecule in the development of acute respiratory distress syndrome (ARDS) , 158, buvia TLR sensing and can release inflammatory cytokines by assembling NLRP3, activating caspase-1 to generate a “cytokine storm,” and synthesizing GSDMD-NT to induce cellular pyroptosis  was found to be positively correlated with Monocytes, and the reliability of the results was verified by the HPA dataset and Monaco dataset databases. Among the numerous immune cells, monocytes play a vital part in the cytokine storm of COVID-19 patients . It was roptosis , 168. Moroptosis , 166.The peripheral blood and synovial tissue of RA patients have been reported to contain a high level of expression and activation of the NLRP3 inflammasome and caspase-1, as well as a high level of expression of IL-1β and IL-18 –183. In via the TLR4-NLRP3-caspase-1 pathway, resulting in pyroptosis and the production of a significant variety of cytokines, including IL-1β and IL-18, and are positively linked with disease activity  and interferons   was closely related to the JAK-STAT signaling pathway. The JAK/STAT pathway, also called the IL-6 signaling pathway, can be activated by IL-6 , 199, wh, IFN-γ) –216, thiTherefore, we can look for drug targets downstream of the JAK/STAT pathway to avoid interfering with the IFN/ISG system by inhibiting the JAK/STAT pathway, but also to effectively inhibit the pyroptosis link, interrupting the “cytokine storm” that erupts from each other and thus interrupting the vicious cycle. Interestingly, caspase-1 is one of the common crosstalk targets between JAK/STAT and pyroptosis pathways.In the present COVID-19 pandemic, the discovery of new medications is challenging, time-consuming, risky, and less successful, and drug repurposing is a good option , 218. MiThus, minocycline can counteract the “cytokine storm” inflammatory response and resist pyroptosis in patients with COVID-19 combined with RA by inhibiting the expression and activation of caspase-1. This process also indirectly demonstrates a potential caspase-1-directed pyroptosis and a shared pro-inflammatory mechanism between COVID-19 and RA, which requires further basic and clinical research.Bioinformatic analysis revealed that COVID-19, RA, and pyroptosis-related genes were enriched in pyroptosis and pro-inflammatory pathways , natural immune pathways (activation of the NLRP3 inflammasome by SARS-CoV-2) and COVID-19-and RA-related cytokine storm pathways . Of these, CASP1 is involved in most pathways. The genes related to minocycline were then obtained by network pharmacology analysis and intersected with COVID-19, RA, and pyroptosis to obtain the common hub gene, and then the key gene was verified as CASP1 by two validation sets. Caspase-1 may be an important mediator of the excessive inflammatory response induced by SARS-CoV-2 in RA patients through pyroptosis. Finally, minocycline was analyzed by computer-aided drug design as an effective drug against the mechanism of caspase-1-induced pyroptosis. Our study provides insight into the causes of the high hospitalization and mortality rates of COVID-19 combined with RA from a new perspective of pyroptosis and offers potentially effective drugs that could provide new directions for further analysis of its pathogenesis and the development of targeted clinical treatments.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/QZ, RL and YC: Consulted the literature and prepared materials. QZ, RL, YC, QL, JZ and JBZ: Experimented and analyzed the data. QZ, RL and YC: Drawn up the manuscript. WW and WX devised the concept and supervised the study. All authors contributed to the article and approved the submitted version.We acknowledge the GEO and Genecards databases for providing their platforms and contributors for uploading meaningful datasets.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.


During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery.To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called https://github.com/dubingxue/MLGL-MP.The code and data underlying this article are freely available at  Enzymes catalyze drug or drug-like compounds into their metabolites, which differ significantly from these compounds themselves . As a coFormer machine learning-based methods can be roughly categorized into network-based and classification-based. Network-based methods generally construct certain interaction networks and leverage network propagation algorithms to infer potential pathways for compounds. For example, Classification-based methods leverage the technique of multi-label learning to infer potential metabolic pathways. Usually, there are three kinds of multi-label learning strategies. The first technique converts a multi-label classification into one or more binary classifications. For example, taking SVMs the binary classifier, The second sort uses a random k-label sets (RAKEL) (P) as the final features. However, these methods ignore the dependence between pathways.The last one directly performs a direct multi-label classification by deep learning. For example, In summary, although existing methods have achieved inspiring performance in metabolic pathway prediction, they neglect the dependence between pathways . For example, both Lipid Metabolism (LM) and Carbohydrate Metabolism (CM) are affected by Metabolism of Cofactors and Vitamins (CVM). Deficiencies of vitamin B1, folic acid and vitamins B6 and B12 (in CVM) lead to a significant increment of lipid deposits in the aorta (in LM) . MoreoveFurthermore, existing computational methods are inadequate to interpret why a compound attends a specific metabolic pathway . TherefoTo address the above issues (pathway interdependence and interpretability), we develop a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction (MLGL-MP). This end-to-end framework contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations based on molecular graphs, while the pathway encoder learns pathway interdependence embeddings. The multi-label prediction discriminates which pathways a compound attends based on two kinds of embeddings. Overall, the main contributions of our MLGL-MP are as follows.It provides an interpretable manner to indicate crucial compound substructures which are significantly associated with metabolic pathways.By capturing the pathway interdependence, it significantly improves the characterization of the relevance between compounds and their metabolic pathways.It proposes a direct multi-pathway prediction approach by measuring the proximity between compounds and metabolic pathways in a common embedding space.m compounds Given shown in .The compound encoder, adopting a two-layer GNN architecture, learns compound embedding representations by utilizing molecule graphs. Its first layer is a GAT , which fV is the set of N atoms and E is a set of chemical bonds. Let N=|V|) be its adjacency matrix, in which ij a= 1 indicates the occurring bond between atom i and atom j, and ija = 0 indicates no bond. Here, each node iv (atom) is initially represented by a q-dimensional binary feature vector According to chemical structure, each compound c, the GAT layer updates its features by aggregating the features of its neighboring atoms. The aggregation is implemented by a shared self-attention operation a: First of all, for each atom s is the dimension of updated atom features. Then, the updated features iv can be defined byj .Inspired by i,ju is the pairwise co-occurrence counts between pathway it and jt, i,ju = j,iu and C denotes the number of metabolic pathway types.The edge building contains three steps to capture the pathway interdependence. First, the pathway co-occurrence is calculated based on the dataset in hand. Define the co-occurrence matrix as P is calculated by the co-occurrence matrix ijp = p (jt|it) as the probability of jt occurring when given it occurring, and it. The probability matrix ijp can be calculated by = i,ju/iN . Note that ijP is usually not equal to jiP.Then, a probability matrix P is performed to address the possible long-tail distribution of co-occurrence patterns, where a few {ijP}are significantly greater than others. Formally, the conditional probability matrix P is turned to a binary matrixAs This binarization removes trivial edges, which could be noise.1  Last, to enhance the node distinguishability during the aggregation of neighboring nodes, an extra re-weighting scheme is utilized  containing an input layer, a hidden layer, and an output layer. Thus, the final compound representation feature is defined as i, and Last, the multi-label classification loss , which contains 11 types of metabolic pathways, including (i) Carbohydrate metabolism; (ii) Energy metabolism; (iii) Lipid metabolism; (iv) Nucleotide metabolism; (v) Amino acid metabolism; (vi) Metabolism of other amino acids; (vii) Glycan biosynthesis and metabolism; (viii) Metabolism of cofactors and vitamins; (ix) Metabolism of terpenoids and polyketides; (x) Biosynthesis of other secondary metabolites; and (xi) Xenobiotics biodegradation and metabolism.The experimental dataset was taken from https://github.com/dubingxue/MLGL-MP.As the compound encoder of our MLGL-MP requires, we removed 21 compounds that cannot be converted to molecular graphs. We finally constructed a dataset containing 6648 compounds and their 11435 metabolic pathway entries. Among them, 4898 compounds attend only one metabolic pathway while the remaining 1750 compounds attend multiple metabolic pathways. Specifically, 38 out of 1750 compounds attend all the 11 metabolic pathways. The metabolic pathway dataset is summarized in In the compound encoder, each node of input compound graph was initially represented by a 78-dimensional (78-d) binary atom feature vector, as suggested in For the pathway encoder, the pre-training of 11 metabolic pathways was implemented by the GloVe algorithm , as well as two state-of-the-art deep learning methods, including a GCN-based method  for all the methods and measured their performance by two groups of metrics . The firIn this section, we investigated why MLGL-MP can achieve inspiring prediction by ablation studies. We made three variants of MLGL-MP, of which the first removes the pathway encoder (denoted as w/o PE), the second one lacks the adapter in the multi-label predictor (denoted as w/o AP), the third (denoted as MLGL-MP-r) alters the pre-trained node feature vectors in the pathway dependence graph to randomly initialized Gaussian vectors .MLGL-MP significantly outperforms w/o PE over all the evaluation metrics. In detail, compared with w/o PE, MLGL-MP improves the Accuracy by 2.70%, the Precision by 5.88%, the Recall by 15.86% and the F1-score by 11.27% due to its pathway encoder. Meanwhile, MLGL-MP reduces the HL by 0.027, the Coverage by 0.212, the OE by 0.076 and the RL by over 0.018. The result indicates that the designated pathway embeddings improve metabolic pathway prediction greatly because it captures the pathway interdependence.Moreover, the comparison with w/o AP shows a similar improvement over all the metrics. In detail, MLGL-MP improves the accuracy by 1.01%, the precision by 4.43%, the recall by 3.16% and the F1-score by 3.84%. Again, it shows a better performance of multi-label learning with reducing the HL by 0.009, the Coverage by 0.046, the OE by 0.019 and the RL by 0.003. This result shows that the adapter in the predictor improves the prediction significantly by aligning compound embeddings with pathway embeddings.In addition, the comparison shows that the version with pathway pre-training (MLGL-MP) is better than that with pathway random initialization (MLGL-MP-r) over all the evaluation metrics. Thus, the pre-training strategy can improve the prediction.In general, the pathway encoder, the adapter and the pre-training strategy play indispensable roles in predicting multi-label metabolic pathways.Although deep learning is known as a black-box model, it is essential to understand how the model makes a prediction and whether the model can guide lead compound optimization in drug discovery. MLGL-MP leverages the GAT layer in its compound encoder to access why a compound participates in a specific pathway. Since the attention weights learned in the GAT layer can reflect the importance of chemical bonds in compounds, we can reveal the association between compounds’ substructures and their metabolic pathway.For example, Energy Metabolism and Amino Acid Metabolism are two important pathways in organisms  while thSulfur metabolism and Oxidative phosphorylation occur in the pathway of Energy Metabolism  (amino (−NH2) and carboxylic acid (−COOH) functional groups play an important role in Amino Acid Metabolism.Overall, the visualized attention weights show that most carbon (C)-based chemical bonds constructing compound backbones usually have small attention values. More importantly, the visualization reveals that crucial substructures having high attentions are pathway-specific. We went deeper into the case of Energy Metabolism , where Stabolism . Meanwhitabolism . For exatabolism . Oxidati target)  shows th target) , which iIn summary, MLGL-MP is an interpretable model, which can indicate compound substructures significantly associated with metabolic pathways. It would help reveal why a compound participates in a specific pathway.In this paper, we have proposed an MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. This end-to-end framework can address two existing issues, including inadequate characterization of pathway dependences and interpretable prediction.The comparison with popular shallow learning models and deep learning models demonstrates the superiority of MLGL-MP. Moreover, the ablation studies as well as the case study validate its contributions. First, it provides an interpretable manner to indicate crucial compound substructures which are significantly associated with metabolic pathways by molecular graph attention embedding. Secondly, by capturing the pathway interdependence, it significantly improves the characterization of the relevance between compounds and their metabolic pathways. Thirdly, by measuring the proximity between compounds and metabolic pathways in a common embedding space, it proposes a direct multipathway prediction approach without extra label strategy. In summary, we believe that our study provides new insights into label dependence representation learning for other multi-label classification problems (e.g. drug toxicity prediction) in drug discovery.Moreover, though the GAT can interpret the importance of drug substructures to metabolic pathways in some sense, other parts (i.e. the pathway encoder and the adapter) in the model are of the black box. In the coming future, it is anticipated that interpretable techniques derived from image processing  can be utilized to achieve better interpretability in predicting metabolic pathways for compounds.This work has been supported by the National Nature Science Foundation of China [Grant number 61872297) and Shaanxi Provincial Key Research & Development Program, China (Grant number 2020KW-063).Conflict of Interest: We declare that we have no conflict of interest.