Guizhi Fuling Wan (GZFLW) is a widely used classical Chinese herbal formulae prescribed for the treatment of endometriosis (EMs). This study aimed to predict the key targets and mechanisms of GZFLW in the treatment of EMs by network pharmacology and molecular docking.Firstly, related compounds and targets of GZFLW were identified through the TCMSP, BATMAN-TCM and CASC database. Then, the EMs target database was built by GeneCards. The overlapping targets between GZFLW and EMs were screened out, and then data of the PPI network was obtained by the STRING Database to analyze the interrelationship of these targets. Furthermore, a topological analysis was performed to screen the hub targets. After that, molecular docking technology was used to confirm the binding degree of the main active compounds and hub targets. Finally, the DAVID database and Metascape database were used for GO and KEGG enrichment analysis.A total of 89 GZFLW compounds and 284 targets were collected. One hundred one matching targets were picked out as the correlative targets of GZFLW in treating EMs. Among these, 25 significant hub targets were recognized by the PPI network. Coincidently, molecular docking simulation indicated that the hub targets had a good bonding activity with most active compounds (69.71%). Furthermore, 116 items, including the inflammatory reaction, RNA polymerase, DNA transcription, growth factor activity, and steroid-binding, were selected by GO enrichment analysis. Moreover, the KEGG enrichment analysis results included 100 pathways focused on the AGE-RAGE pathway, HIF pathway, PI3K Akt pathway, MAPK pathway, and TP53 pathway, which exposed the potential mechanisms of GZFLW in treating EMs. Also, the MTT colorimetric assay indicated that the cell proliferation could be inhibited by GZFLW. Compared with the control group, the protein levels of P53, BAX, and caspase3 in the drug groups were all increased in Western blotting results. The results of flow cytometry showed that the percentage of apoptotic cells in the GZFLW group was significantly higher than that in the control group.Through the exploration of network pharmacology and molecular docking technology, GZFLW has a therapeutic effect on EMs through multi-target mechanism. This study provided a good foundation for further experimental research. Endometriosis (EMs), a common painful gynecological disease, occurs in 10% women of childbearing age . The mosHistorical Chinese medical texts have documented the use of the traditional Chinese herbal formula Guizhi Fuling Wan (GZFLW) for the EMs-like symptoms such as dysmenorrhea since the late Eastern Han Dynasty (200-210AD), and it is still widely used today for the treatment of EMs  due to eThe holistic treatment of traditional Chinese medicine (TCM) has attracted more and more attention. The mechanism of drug therapy has shifted from single target to multiple interacting targets mediated by multiple compounds. Nowadays, the extensive application of network pharmacology and molecular docking provides a more effective method for the research and evaluation of the multi-target effect of multi-component drugs on diseases, which can reveal the mechanism of TCM treatment from a holistic perspective . In the Therefore, in this study, we screened multiple databases to find the active components of GZFLW and its possible targets for treating EMs. Target genes of GZFLW and EMs were matched to obtain overlapping results. PPI network data was obtained through the STRING database, and the hub targets were screened through topology analysis. After that, molecular docking technology was used to screen and verify the binding degree of the main active compounds and hub targets. Finally, the DAVID database and Metascape database were used for gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis. GO is generally used to describe the genes function and genes relationships , and KEGhttps://tcmspw.com/tcmsp.php), the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (http://bionet.ncpsb.org/batman-tcm/index.php) and Chinese Academy of Sciences Chemistry Database (http://www.organchem.csdb.cn/scdb/default.htm) were used, which are both bioinformatics analysis tools for the main components of TCM. Four hundred ninety-four herbal ingredients were screened out.To collect the chemical ingredients of the five herbs contained in GZFLW, the Traditional Chinese Medicine Systems Pharmacology Database  . DL is often used to select the active ingredients with “drug-like” properties in TCM composition  of drug, oral bioavailability (OB), and drug-likeness (DL) were used as screening indexes. OB is the percentage of oral drugs absorbed into the bloodstream, which is a frequently-used pharmacokinetic parameter. The OB was obtained using the OBioavail1.1 software, which covers 805 different drug and drug-like molecules . OB is aposition . The OB The active compounds’ targets in GZFLW were obtained from TCMSP, BATMAN-TCM, and CASC database, with the species limited as “Homo sapiens”.https://www.genecards.org/). The GeneCards database is often used to predict genetic information related to human diseases. We searched the database with the keyword “endometriosis” to obtain targets. Finally, the targets of GZFLW active ingredients were matched with the therapeutic targets of EMs. The overlapping targets were chosen as the potential targets of GZFLW in the treatment of EMs. These targets were then uploaded to STRING Database (http://string-db.org/) to obtain the interactions of the screened targets with the confidence ≧0.7 and the result of protein-protein interaction (PPI).Related targets of EMs were screened out by the GeneCards Database   is measured by the percentage of all shortest paths. The nodes with high betweenness can significantly impact the net by controlling the information transmitted between other nodes. The node parameters are positively correlated with their topology importance in the network.http://autodock.scripps.edu/) was a molecular docking software that could be used to dock the hub targets and active ingredients based on network pharmacology. In this docking process, the 3D structure of thirteen hub targets were retrieved from RCSB Protein Data Bank (PDB) (http://www.rcsb.org/): AKT1 , TNF , TP53 , VEGFA , MAPK1 , MMP9 , JUN , MAPK8 , INS , EGF , IL6 , PTGS2 (PDB ID: 5F19), and FOS (PDB ID: 1FOS); the 3D shapes of active compounds were provided from ZINC Database (http://zinc.docking.org/) and PubChem Database (https://pubchem.ncbi.nlm.nih.gov/). Binding energy was used as docking score to evaluate the protein-ligand binding potential of molecular docking. Among them, those results with value ≤ -5 were selected and considered to have moderate binding potential and tight combination.Molecular docking is a computational tool to predict the binding ability and connection type of proteins and ligands. It can calculate and predict the conformation and direction of ligands at active protein sites. AutoDock 1.5.6 (https://david.ncifcrf.gov/) (http://metascape.org/).We used the DAVID (rf.gov/)  for GO erf.gov/)  (http://http://www.cytoscape.org/)  , an open∘C in the cell incubator with a humid atmosphere containing 5% CO 2. After that, the vimentin staining of cells was identified by immunofluorescence.HEM15a cells were cultured in DMEM containing 10% FBS and 1% penicillin/streptomycin at 37 ∘C for 48 h and reacted with GZFLW of different concentrations, hEM15a cells were reacted with 10 μL MTT solution. Finally, 150 μL dimethylsulfoxide (DMSO) was added after reacting for 4 h. The optical absorbance was detected at 568 nm by a plate reader.The hEM15a cells were seeded in a 96-well plate and divided into the control group and the drug group. After incubation at 37 ∘C overnight. Subsequently, the matched secondary antibody was added to the membranes.The hEM15a cells were splitted in RIPA buffer (Beyotime) added with 10 μl PMSF (Aladdin) and phosphatase inhibitors (Beyotime) to extract total proteins. After that, equivalent amounts of proteins were resolved by poly acrylamide gel electrophoresis (PAGE) and transferred to polyvinylidene fluoride (PVDF) membranes . Then, TBST with 5% skim milk was adopted for blocking. Afterward, the membranes were reacted with primary antibodies against GAPDH , BAX , P53 , and caspase3 , at 4 The apoptosis of hEM15a cells was evaluated by Annexin V-FITC/PI apoptosis kit . The collected hEM15a cells, divided into normal group and GZFLW group, were resuspended in 500 ul binding buffer and added with Annexin V-PI solution. Then, the hEM15a cells were incubated for 5∼15 min without light. The percentage of apoptotic cells was then immediately detected on a flow cytometry (Beckmancoulter).By retrieving from the TCMSP, BATMAN-TCM, and CASC database, there were 494 related ingredients of GZFLW in total, and there were 220 (44.5%) of CT, 34 (6.9%) of PC, 55 (11.1%) of CM, 119 (24.1%) of RPR, 66 (13.3%) of PK. With OB ≧30% and DL ≧ 0.18 as indexes, 89 active ingredients were screened out , and theIn this process, we collected 284 targets of 89 active ingredients, and there were 58 in CT, 48 in PC, 185 in CM, 104 in RPR, and 62 in PK. Via the keyword of “endometriosis,” the therapeutic targets of EMs were obtained from GeneCard Database with a total of 1350. One hundred one overlapping targets were then obtained as the related targets of GZFLW in the treatment of EMs .The data of the PPI network of those 101 targets were subsequently obtained in STRING Database. With confidence ≧0.7, there were 95 nodes and 751 edges in total. Taking two essential parameters of “degree” and “betweenness” as screening indexes, the topological analysis of targets mentioned above was performed. Targets more significant than or equal to the median are used as hub targets for GZFLW against EMs. The screened thresholds were degree ≧11.5 and betweenness ≧0.007, and the results were 25 hub nodes with 234 edges, including IL6, JUN, TNF, MAPK1, TP53, EGF, MAPK8, MMP9, VEGFA, AKT1, INS, FOS, ICAM1, PTGS2, CCL2, CCND1, EGFR, IL1B, MYC, IL10, PTEN, ESR1, PPARG, RELA and MMP2 . When thBased on the 25 key targets and related 29 active ingredients, we further established the network of Hub nodes—Compounds . Among tThirteen hub targets with top degrees of GZFLW were identified with seven active compounds by AutoDock. There were 122 results (69.71%) of them had a moderate binding potential, which indicated that active ingredients of GZFLW could well bind to the targets for the treatment of EMs . The ligWith the database of DAVID and Metascape, the enrichment analysis on 101 targets was performed and resulted in 116 GO items and 100 KEGG pathways.P-value, the first eight items of three parts, BP, MF, and CC, were selected , HIF-1 signaling pathway (hsa04066), PI3K-Akt signaling pathway (hsa04151), MAPK signaling pathway (hsa04010), EGFR tyrosine kinase inhibitor resistance (hsa01521)  . Then, wsa01521) .The primary endometrial stromal cells were spindle-shaped or star-shaped with large and round nuclei, and proliferative fibrous tissue could be seen. After that, the immunofluorescence was used to identified the vimentin staining of cells in different groups .P < 0.05, 0.01 or 0.001; In comparison with the control group, in addition to 0.5 mg/ml, the other different doses of treatment groups  had significant inhibitory effect on the proliferation of endometrial stromal cells (P < 0.05 or 0.01) in endometriotic lesions  had tight combinations with Jun, FOS, and PTGS2, and the other three active ingredients  had strong putative interaction with P53, which demonstrates that GZFLW is likely to affect on proliferation and apoptosis through a multi-ingredient synergistic way.To verify the effect of GZFLW on EMs, we conducted in vitro experiments. The results of MTT and flow cytometry confirmed that GZFLW can effectively promote the apoptosis of ectopic stromal cells in EMs. In the treatment group, the expression levels of P53, caspase3 and BAX were significantly up-regulated, suggesting that the therapeutic effect of GZFLW may be related to the regulation of P53 pathway and apoptosis pathway.More and more evidence has shown that the pathophysiological mechanism of EMs is complicated, and hypoxia plays an essential role in EMs damage . The AGEFortunately, triterpenoids and flavonoids including hederagenin, β-sitosterol , campestHypoxia and inflammatory, as an essential characteristic of EMs microenvironment, can stimulate the transcription of hypoxia-inducible factor HIF  and inflIt is worth mentioning that triterpenoids  and flavonoids (taxifolin) in GZFLW were detected in our study, they could have a modest effect on inhibiting VEGF in EMs, which have been confirmed by experiments and in line with our molecular docking results . PreviouIn this study, network pharmacology and molecular docking were utilized for exploring the potential effects of GZFLW in anti-EMs, mainly focusing on four aspects: the suppression of inflammatory response, the regulating of apoptosis and proliferation, the reduction of the oxidative stress, and the inhibition of angiogenesis. Among them, the pro-apoptotic effect was confirmed by our in vitro experiments. In these crucial biological functions, triterpenoids and flavonoids contained in the GZFLW were considered that those might be the critical active ingredients involved in the potential therapy mechanisms.Our study had several limitations. Firstly, to some extent, network pharmacologic analysis can help to predict the possible molecular mechanism of GZFLW in treating EMs. However, we can only speculate but not confirm if such mechanisms have an impact because, in evidence-based medicine, the conclusion can not be determined until subsequent biological experiments. Secondly, the data set of the GZFLW related compounds and targets were identified through the TCMSP, BATMAN-TCM, and CASC database, and GeneCards built the EMs target database. Choosing different databases may lead to the study biases. Moreover, because all the databases we use are based on existing research results, this may limit the discovery of new targets related to EMs treatment.In conclusion, our study reveals that GZFLW may have a multiple effect on EMs. Although the mechanism remains confirmed by further experiments, the relevant targets and signaling pathways for GZFLW against EMs have been preliminarily studied and systematically summarized.10.7717/peerj.11087/supp-1Supplemental Information 1Click here for additional data file.


Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism of natural products is often unclear and studied insufficiently. Pathway fingerprint similarity based on “drug-target-pathway” heterogeneous network provides new insight into Mechanism of Action (MoA) for natural products compared with reference drugs, which are selected approved drugs with similar bioactivity. Natural products with similar pathway fingerprints may have similar MoA to approved drugs. In our study, XYPI, an andrographolide derivative, had similar anti-inflammatory activity to Glucocorticoids (GCs) and non-steroidal anti-inflammatory drugs (NSAIDs), and GCs and NSAIDs have completely different MoA. Based on similarity evaluation, XYPI has similar pathway fingerprints as NSAIDs, but has similar target profile with GCs. The expression pattern of genes in LPS-activated macrophages after XYPI treatment is similar to that after NSAID but not GC treatment, and this experimental result is consistent with the computational prediction based on pathway fingerprints. These results imply that the pathway fingerprints of drugs have potential for drug similarity evaluation. This study used XYPI as an example to propose a new approach for investigating the pharmacological mechanism of natural products using pathway fingerprint similarity based on a “drug-target-pathway” heterogeneous network.The online version contains supplementary material available at 10.1186/s13321-021-00549-5. Andrographis paniculate, which makes up about 4%, 0.8 ~ 1.2% and 0.5 ~ 6% in dried whole plant, stem and leaf extracts respectively [Andrographis paniculate was used as an herbal medicine in both traditional Indian and Chinese medicine  [More than half of new chemical entities from 1981 to 2010 were derived from natural products directly or indirectly . The appectively . And Andctively) , 12. OurDrugs without common targets can also exert similar therapeutic effects on the same disease because different targets participate in the same pathway or interact with each other, which is closely associated with the pathological process . TherefoHowever, known “natural product-pathway” associations discovered by biological experiments are too less. Only Connectivity Map (cMap) has been employed to predict potential drug-pathway association which collect genomewide transcriptional expression data form cell lines after small molecules treatment , 23. ButIn this study, we proposed an approach using pathway fingerprint similarity based on a “drug-target-pathway” heterogeneous network to explore the mechanisms of natural drugs XYPI using NSAIDs and GCs as reference agents. A heterogeneous network similarity algorithm used for social networks was applied to investigate the pathway fingerprint similarity between drugs in the heterogeneous "drug-target-pathway" network and to predict the drug-pathway associations of XYPI. The results indicate that XYPI may have similar MoA with NSAIDs, neither than GCs. To validate the prediction result, LPS-induced macrophage activation model was applied and transcriptome of drug treatment to model were launched to investigate the transcriptome expression pattern after drug treatment, which can validate potential drug-pathway association of XYPI based on experiment data. The drug-pathway association predicted based on HIN is consistent with association from experiment data which indicate that XYPI has similar MoA with NSAIDs, and pathway-based approach using “drug-target-pathway” heterogeneous network is promising for investigating the pharmacological mechanism of natural products.CS) was applied to comprehensively evaluate the probability of a drug-target interaction from the three types of resources same like the combined score of protein–protein interactions in STRING [Three types of drug-target interactions of drugs (XYPI and positive/negative controls) were used to construct the target profile for drug. STITCH provides the drug-target interactions based on literature mining , PubChemn STRING , which wPstitch represents the probability of drug-target interaction from STITCH, Ppubchem represents the probability of drug-target interaction from PubChem Bioassay , and Pbatman represents the probability from BATMAN-TCM. In subsequent analysis, the drug-targets interactions with CS > 0.4 were selected as interactions with higher reliability for analysis.Based on the similar structure and activities, drug-target interactions of four compositions of XYPI were combined in to one dataset to present XYPI. If there are common targets between four compositions, largest confidence score of same target was used as the final confidence score of drug-target interactions. For drug-target interactions of XYPI, using "Xi yan ping Injection”, "Andrographolide sulfonates" and "Andrographolide" as keywords to mine the medical literature abstracts from PubMed from 1950 to 2015, related genes mentioned in the literature were excavated based on sentence co-occurrence. Combining targets based on literature mining, targets predicted by BATMAN-TCM, and targets extracted from PubChem Bioassay, potential targets of XYPI were kept with combined score > 0.4.Associations of target-pathway were obtained from three pathway databases, including Gene Ontology (GO) , Reactom“Drug-target-pathway” heterogeneous information network consists of three type of objects  and two semantics relationships between them (drug-target interactions and target-pathway associations) mentioned above.The target similarity between two drugs was measured based on comprehensive targets of the compounds. For example, The hierarchical clustering of N compounds was executed by the R package hClust  based on3 receptor antagonists. Only GCs with similar targets profile were included in further analysis which was cluster together into one cluster based on the hierarchical clustering of target similarity, same as the NSAIDs and three types of negative control to make these drugs representative for their MoA respectively.GCs and NSAIDs were selected as positive controls which are representative anti-inflammatory drugs and three types of negative control without anti-inflammatory activities include H2 receptor antagonists, antidepressants, and 5-HTThe pathway fingerprint similarity of two drugs was measured based on a “drug-target-pathway” heterogeneous network using the PathSim method, which has been applied to recommendation systems in social networks . Under tThe second definition is the similarity of pathway fingerprints of two drugs ,dimethyl sulfoxide (DMSO), LPS (CST), fetal bovine serum , a penicillin–streptomycin double antibody , DMEM culture medium , and a mouse IL-6 ELISA kit  were used. The RAW264.7 mouse macrophage cell line was purchased from the Cell Resources Center of the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, and frozen in liquid nitrogen for use. A ZD-420 electric thermostatic water bath, a Multiskan Ascent microplate reader , a BS224S electronic balance (Serdulis in Germany), a Napco5410 carbon dioxide incubator (American NAPCO), a DMIL inverted microscope (Germany LEICA), an optical microscope (Olympus), a program storage box, an ultraclean platform were also used.Cell culture and experimental procedure Normal RAW246.7 cells were cultured in DMEM culture medium containing a final concentration of 10% fetal bovine serum and 100,000 U/L penicillin and placed in a cell incubator with 5% CO2 and a temperature of 37 °C. The medium was changed once every 48–72 h according to the cell growth. After the cells had grown to 70% ~ 80% confluence, they were digested with 0.25% trypsin–EDTA and centrifuged to separate the cells. The cells were then subcultured by passage once every 6 days and frozen in liquid nitrogen for later use. The total number of inoculated cells was 2 × 104 per well, and the plates were placed in a cell incubator containing 5% CO2 at 37 °C. After 24 h of adherence, the cell culture solution was discarded, and 100 μl of DMEM culture medium containing a final concentration of 0.5 to 5 μg/ml LPS was added for inflammatory stimulation. After 48 h of treatment, the cell supernatant in each group of wells was collected. Interleukin (IL)-6 concentrations were determined using Duo Set ELISA Kits . The experiment was conducted following the manufacturer’s instructions. And the morphological differences between cells in each group were observed with a microscope.5 cells/well in a 6-well plate) were pretreated with or without XYPI, ketoprofen, or prednisolone for 24 h and then incubated with LPS for 48 h. Cells lysates were prepared with RIPA lysis buffer and protease inhibitors , and centrifuged at 14,000g for 15 min at 4 °C. Then protein samples were completed for western assay.RAW264.7 cells  according to the manufacturer’s instructions, and RNA integrity was assessed using the RNA Nano 6000 Assay Kit and the Bioanalyzer 2100 system . Three biological replicates were used. A total amount of 3 μg RNA per sample was applied for library construction. Briefly, sequencing libraries were generated by using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina®  according to the instructions. First, poly-T oligo-bound magnetic beads were applied to purify mRNA before RNA fragmentation was carried out. Then, random hexamer primers and M-MuLV Reverse Transcriptase (RNase H-) were used to synthesize first-strand cDNA, and DNA Polymerase I and RNase H were used to synthesize second-strand cDNA. After that, PCR was performed to enrich the cDNA template following adenylation of the 3’ ends of the DNA fragments and ligation of the adapters. The PCR products were purified (AMPure XP system), and the Agilent Bioanalyzer 2100 system was used to evaluate library quality. The AcBot Cluster Generation System was applied for clustering of the index-coded samples by using TruSeq PE150 Cluster Kit v3-cBot-HS (Illumina). Then, the sequencing library was sequenced on an Illumina HiSeq 4000 platform, and 150 bp paired-end reads were generated. This whole experiment was conducted at Novogene Bioinformatics Technology Co., Ltd. .The EdgeR package was used for RNA-SEQ differential gene expression analysis , and theThe differentially expressed genes can be found via transcriptome data after drug treatment. According to the predicted pathways regulated by XYPI based on HIN, the transcriptome data is used to verify the regulatory direction of XYPI on pathways. The ratio of up-regulated genes in pathway after drug treatment was used to evaluate the direction of drug regulation on pathway. Hierarchical clustering of differentially expressed genes in pathway was generated by hClust package .By integrating the confidence scores of targets from different resources, 140 targets of XYPI with a combined confidence score greater than 0.4 were retained for subsequent analysis. Based on the functional enrichment of the integrated targets by metascape, XYPI may be involved in regulation of the inflammatory response, leukocyte differentiation, ROS metabolic process, etc. , 65 common targets of GCs (target for at least two GCs) and 161 common targets of NSAIDs (target for at least two NSAIDs) whose confidence scores were greater than 0.4. To investigate the similarity of XYPI with GCs and NSAIDs, Venn diagram of XYPI, GCs and NSAIDs showed that there were 12 common targets of XYPI, GC and NSAIDs, and XYPI had common targets with both GC and NSAIDs , Reactome and WikiPathways Fig. D. For taTo explore the role of XYPI in immune and inflammatory responses, a Venn diagram was generated to compare the differences between XYPI drug targets and genes in the GO terms “immune response” and “inflammatory response”. The results showed that 59/140 and 37/140 targets of XYPI participate in immune and inflammatory responses, respectively. Similar results were obtained for GCs and NSAIDs Fig. A. This rTo further illustrate the inflammatory and immune related characteristics of XYPI, we identified the shared pathways of XYPI and NSAIDs (ketoprofen) to predict the associated pathway of XYPI. In the inflammatory pathways, XYPI tends to affect molecules related to positive regulation of the inflammatory response and molecules involved in cytokine production, which include proinflammatory proteins such as NFKBIA, FABP4, IL2, and CCL4 Fig. A. In theTranscriptome assays from the LINCS database also provide an opportunity to explore the expression of sharing pathway of reference drugs. Prednisolone and ketoprofen were selected as representative of GCs and NSAIDs, respectively, because of their high similarity with XYPI based on pathway fingerprints. In this study, differential expressing pathways after ketoprofen and prednisolone treatment were investigated based on LINCS data , 23 whicTo validate the prediction MoA of XYPI (similar to ketoprofen), and to compare the pharmacological effects of XYPI, an LPS-activated murine macrophage model was used. Prednisolone and ketoprofen were selected as representative GCs and NSAIDs, respectively, because of their high similarity with XYPI based on pathway fingerprints. In LPS-activated macrophages, transcription of the cytokine IL6 was increased after 48 h of stimulation  interact with each other to shut down a cellular pathway, which may be an unintended pathway, demonstrating the potential for polypharmacy to impact complex diseases. In addition to target profiles, pathway fingerprints have also been used in polypharmacological studies to describe the function of drug therapy , 15. In Natural products from traditional medicine inherit bioactivity from their source herbs. However, the pharmacological mechanism by which they protect against disease is often unclear and studied insufficiently. The prediction of MoA based on structures and target profiles has been performed extensively in drug discovery. However, the “drug-target-pathway” heterogeneous network provides new insight into drug MoA. Pathway fingerprints extracted from a “drug-target-pathway” heterogeneous network can describe the pathways affected by drugs, which are meaningful combinations of direct and indirect targets of drugs. The similarity evaluation of pathway fingerprints based on recommendation systems used in social networks is a technological transformation of social network technology to pharmacological research. Compared to novel compounds, natural products have more easily predictable bioactivities based on traditional use. Pathway fingerprint similarity provides new insight into natural products compared with reference drugs, which are selected approved drugs with similar bioactivity. Natural products with similar pathway fingerprints may have similar MoA to approved drugs. In our study, pathway fingerprints of drugs can provide new ideas for drug similarity investigations. Similarity evaluation for heterogeneous networks based on recommendation systems for social networks provides good reference methodologies for pathway fingerprints Fig. .Fig. 6WoIn summary, pathway fingerprints were applied to an XYPI MoA investigation. XYPI, GCs and NSAIDs have considerable anti-inflammatory effects. However, we found that XYPI has a similar target profile to GCs but similar pathway fingerprints to NSAIDs. Based on the experimental validation of the transcriptome, we found that the expression profile of ketoprofen is similar to that of XYPI, but prednisolone has a distinct profiling pattern, which indicates that the anti-inflammatory mechanism of XYPI may be different from that of GCs but similar to that of NSAIDs because XYPI does not have an immunosuppressive effect, unlike GCs. This experimental result is consistent with the computational prediction based on pathway fingerprints. This study used XYPI, an andrographolide derivative, as an example and proposed a new approach for investigating the pharmacological mechanism of natural products using pathway fingerprint similarity based on a “drug-target-pathway” heterogeneous network.Additional file 1. Hierarchical clustering of XYPI, positive and negative controls based on target similarity (A) and pathway fingerprint similarity (B).Additional file 2. Hierarchical clustering of XYPI, GCs and NSAIDs based on pathway fingerprint similarity with different drug-target interaction cutoff (0.3-0.5) using three types of pathway datasets.


Objective: In this study, we mainly explored two questions: Which microorganisms were functionally active in the endometrium of patients with endometrial cancer (EC)? What kind of response did the human host respond to functionally active microorganisms?Methods: Nine endometrial cancer patients and eight normal subjects were included in this study. HMP Unified Metabolic Analysis Network 3 (HUMAnN3) was used to obtain functional information of microorganisms. In addition, metaCyc-based GSEA functional enrichment analysis was used to obtain information on the metabolic pathways of the human host. At the same time, the O2PLS model and Spearman correlation analysis were used to analyze the microorganisms–host interaction.Results: With the novel metatranscriptome analysis pipeline, we described the composition of more than 5,000 functionally active microorganisms and analyzed the difference in microorganisms between the EC and the normal group. Our research found that these microorganisms were involved in part of the metabolic process of endometrial cancer, such as 6-sulfo-sialyl Lewis x epitope, N-acetyl-beta-glucosaminyl. In addition, the host–microbiota crosstalk of EC endometrium also included many biological processes, mainly functions related to tumor migration and the Apelin signaling pathway.Conclusion: The functionally active microorganisms in the EC endometrium played an essential role in the occurrence and migration of tumors. This meant that functionally active microorganisms could not be ignored in the treatment of endometrial cancer. This study helped to better understand the possible role of endometrial functional, active microorganisms in the occurrence and development of EC in patients with endometrial cancer and provided new information for new attempts to treat EC. Endometrial cancer (EC) is the fourth most common malignant tumor in women . The numFor an extended period, the uterus was considered sterile. With the development of detection technology, the endometrium was proved to have its resident microbiota, the microbial community . The micIn addition, the current standard methods of microbial research, such as 16s rRNA and metagenome analyses, were all based on DNA sequences. However, the existence of characteristic DNA sequences was not equal to the existence of living microorganisms. Furthermore, since DNA molecules might exist for decades , the DNAIn this study, based on the functional, active endometrial microbiota mapping pipeline using metatranscriptome  , we expln = 9, age: 68.2 ± 10.3 years (mean ± standard deviation) and CON cohort: n = 8, age: 61.8 ± 13.5 years.The data of nine endometrial cancer patients and eight control subjects were included in this study. The raw SRA files of RNA-seq were extracted from PRJNA612305 from the Gene Expression Omnibus (GEO) repository. As mentioned earlier , the QubQ-value ≤ 20) bases. Subsequently, the remaining sequences were aligned to the human reference genome GRCh38 from Gencode v26 using HISAT2  . Then, tg HISAT2 . The comg HISAT2 . Next, tg HISAT2  was usedg HISAT2 . The prog HISAT2 . SubsequThe HMP Unified Metabolic Analysis Network 3 (HUMAnN3)  was usedp-value < 0.05. Since microbial metabolic pathways were analyzed based on the MetaCyc database, we used the Pathway Tools utility to obtain all human metabolic pathways  in the MetaCyc database. The obtained pathway .col file contains a total of 390 human metabolic pathways. Then we converted the .col file into a gene set enrichment analysis (GSEA) input file in GMT format and used the GSEAPreranked method in GSEA to perform enrichment analysis of human metabolic pathways based on the results of the difference analysis  model  showed ep-value ≤ 0.05. Compared with other methods (such as Pearson), the normalized count (gene expression) and component data (relative abundance of microbiota) of Spearman’s correlation analysis performed better  enrichment analysis  and usedn = 2, nx = 1, ny = 1, and the MSE = 127.60 at this time. The model’s loading diagram , we used the metascape tool  in tumors . At the same time, we found that these species with high abundance in the EC endometrium were closely related to activating the Apelin signaling pathway, which had been confirmed to be related to the increased risk of endometrial cancer  can be found in the article/CF and PC carried out the study. PC and YG analyzed and interpreted the data. TL and TH drafted the manuscript. CF, PC, and TL coordinated the study, participated in the design, and reviewed the manuscript. TL, LJ, JW, and TH took part in revising the manuscript. All authors read and approved the final manuscript.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.


Xanthophyllomyces dendrorhous is a basidiomycete yeast that naturally produces the red–orange carotenoid astaxanthin, which has remarkable antioxidant properties. The biosynthesis of carotenoids and sterols share some common elements that have been studied in X. dendrorhous. For example, their synthesis requires metabolites derived from the mevalonate pathway and in both specific pathways, cytochrome P450 enzymes are involved that share a single cytochrome P450 reductase, CrtR, which is essential for astaxanthin biosynthesis, but is replaceable for ergosterol biosynthesis. Research on the regulation of carotenoid biosynthesis is still limited in X. dendrorhous; however, it is known that the Sterol Regulatory Element-Binding Protein (SREBP) pathway, which is a conserved regulatory pathway involved in the control of lipid metabolism, also regulates carotenoid production in X. dendrorhous. This review addresses the similarities and differences that have been observed between mammal and fungal SREBP pathways and what it is known about this pathway regarding the regulation of the production of carotenoids and sterols in X. dendrorhous. Xanthophyllomyces dendrorhous is a natural source of astaxanthin. This red–orange carotenoid has remarkable antioxidant activity [X. dendrorhous [X. dendrorhous, is still limited. It is known that astaxanthin production is regulated by environmental and nutritional conditions. For example, reactive oxygen species induce carotenoid production, which is probably related to the antioxidant protective role of carotenoids [X. dendrorhous is regulated by glucose‐dependent repression, depending on the DNA‐binding regulator Mig1 and the co‐repressor complex Cyc8-Tup1, which repress the expression of several genes including genes involved in carotenogenesis [X. dendrorhous is regulated by the SREBP (Sterol Regulatory Element-Binding) pathway [, which is a conserved pathway in several organisms involved in lipid homeostasis that is activated depending on sterol demand [X. dendrorhous, this pathway regulates several genes of the mevalonate (MVA) pathway and from ergosterol biosynthesis; interestingly, it also regulates two carotenogenic genes  transcription factors that are initially synthesized as inactive precursors that are anchored to the Endoplasmic Reticulum (ER) membrane [2-terminal  and COOH-terminal (the regulatory domain) domains facing the cytoplasm. These terminal domains are separated by a short hydrophilic sequence  which projects into the lumen of the ER [The SREBP pathway is involved in the regulation of lipid homeostasis and metabolism, and our knowledge originates primarily from studies in mammalian cells. Therefore, it is necessary to highlight the key elements of the mammalian SREBP pathway Fig. A, beforemembrane . In mammmembrane , 21 and membrane . The firf the ER . In the f the ER . Specifif the ER . When stf the ER , 27. On f the ER , which af the ER  and nextf the ER . As a reSchizosaccharomyces pombe [Aspergillus such as Aspergillus fumigatus [Cryptococcus neoformans [. In these fungi, the SREBP pathway is also involved in resistance to azole compounds, which are antifungal drugs that negatively affect ergosterol biosynthesis [A. fumigatus and C. neoformans [A. fumigatus [Neurospora crassa and Trichoderma reesei [SREBP-like protein encoding genes have been identified in several fungal genomes, and some have proven to be functional Table . The SREes pombe  and specumigatus , while soformans , 34. In ynthesis , and in oformans , 34. Novumigatus , and thea reesei , 37. DifS. pombe, homologs of SREBP, SCAP and Insig proteins have been identified  [S. pombe has a second SREBP homolog (Sre2), whose role is still unknown [S. pombe Sre1 is mediated by a different mechanism to the one described in mammalian cells, as it lacks S1P and S2P homologs. Sre1 activation depends on the Golgi Dsc (defective for SREBP cleavage) E3 ligase complex  [In the fission yeast ctively) . Interes unknown . The proctivity) , 39 and ctivity) , that bictivity) .Aspergillus, the proteolytic processing of the SREBP homolog (SrbA) [S. pombe since A. fumigatus and A. nidulans also lack S1P and S2P homologs. In A. fumigatus, the homologs of the Golgi Dsc E3 ligase complex (DscA to DscD) [A. fumigatus (SrbB), is probably independent of proteolytic cleavage as no transmembrane domains are predicted in its structure [A. nidulans SrbA is sequentially processed by Dsc-linked proteolysis followed by an aspartyl protease (SppA) [Aspergillus. Interestingly, a SppA homolog (Ypf1) was also detected in S. pombe, but it is not involved in the response to hypoxia conditions [In g (SrbA) , 42 appeto DscD) , and rhoto DscD)  are invotructure . InteresS. pombe and A. fumigatus, N. crassa has homologs of SREBP (SAH-2 and SRE-2), SCAP (SCP-1), SppA (Spp-1), Rbd2 (RBD-2) and of components of the Golgi Dsc E3 ligase complex (Dsc-1 to Dsc-6) [Like o Dsc-6) . In thiso Dsc-6) .C. neoformans, unlike S. pombe and Aspergillus, it was shown that the SREBP homolog (Sre1) [S. pombe and N. crassa, this basidiomycete has a SCAP homolog, which was shown to be involved in the SREBP pathway [In the case of the basidiomycete g (Sre1)  is proceg (Sre1) , 46. Int pathway .X. dendrorhous, SREBP and S2P homologs were recently described , which were proven to be involved in the SREBP pathway in this yeast [In the basidiomycete is yeast , 48 , which distinguishes SREBPs from other bHLH transcription factors [X. dendrorhous genome, suggesting that this yeast lacks a SCAP homolog. As in X. dendrorhous, C. neoformans Sre1 also lacks the DUF2014 domain, but this basidiomycete does have a SCAP homolog (Scp1) [A. fumigatus SrbA harbors the DUF2014 domain, but this ascomycete lacks a SCAP homolog [X. dendrorhous has a SCAP-independent Sre1 activation mechanism that could also be conserved in other fungi lacking SCAP, like the pathogen A. fumigatus. In mammals, the SCAP and the Insig proteins are important components of the SREBP pathway, as they bind cholesterol and cholesterol hydroxylated derivatives (such as 25-hydroxycholesterol), respectively [C. neoformans and Aspergillus, X. dendrorhous lacks an Insig homolog, and, even though an Insig homolog was identified in S. pombe, it is not involved in the SREBP pathway of this yeast [X. dendrorhous. factors . It also factors , which i factors . Howeverg (Scp1) . A. fumi homolog . Thus, iectively , 53, andis yeast . This evXanthophyllomyces dendrorhous lacks a S1P protease homolog involved in Sre1 cleavage, but it is known that a S2P protease homolog (Stp1) is involved in this process [C. neoformans, as although it also lacks a S1P homolog, it does possess a S2P homolog involved in Sre1 activation [C. neoformans is required for Sre1 activation in response to lower sterol levels under low oxygen conditions [S. pombe, the Sre1-Scp1 complex senses ergosterol, and Sre1 transport and activation depends on the ergosterol concentration in the ER [X. dendrorhous, the sterol signal that favors Sre1 activation is still unknown. However, in mutants that do not produce ergosterol and overproduce carotenoids (cyp61− mutants), Sre1 is mainly in its activated form  under standard laboratory conditions, correlating with the higher transcript levels of genes regulated by Sre1 that may be responsible in part for the carotenoid overproduction phenotype [ERG3 and ERG4) that also do not produce ergosterol, do not overproduce carotenoids, and transcript levels of genes regulated by Sre1 are the same as in the wild-type strain [X. dendrorhous, but rather Sre1 activation depends on other alterations in sterol composition. process . A similtivation . Howevernditions . In S. pn the ER . In X. dhenotype . Howevere strain . These oS. pombe, the active Sre1 transcription factor (Sre1N) is regulated by a casein kinase 1 family member (Hhp2) that promotes Sre1N proteasomal degradation through phosphorylation of Sre1N at specific residues [X. dendrorhous genome, genes encoding putative kinase homologs to S. pombe Hhp2 (CDZ96742.1) and to mammalian GSK-3 (CDZ96841.1) were identified, and several serine and threonine residues of Sre1N are predicted to be potential phosphorylation sites. However, studies are needed to determine if indeed phosphorylation by these potential kinases or by others has a role in the regulation of Sre1 in X. dendrorhous. Additionally, in S. pombe the protein Ofd1 (2-oxo-glutarate Fe(II) dioxygenase) accelerates Sre1N proteasomal degradation and inhibits Sre1N DNA binding under normoxic conditions [X. dendrorhous has a potential Ofd1 homolog (CED84823.1), but it may not be involved in the X. dendrorhous SREBP pathway as the phenotype of strains that only produce the active Sre1 transcription factor was not modified with the OFD1 mutation [Besides proteolytic activation, SREBPs are regulated by covalent modifications and by interaction with other proteins. This has been studied more in mammals than in fungi. In mammals, regulation of SREBP by phosphorylation-dependent degradation has been reported , 57. Forresidues . In the nditions . X. dendmutation .X. dendrorhous Sre1 is involved in lipid homeostasis and in resistance against antifungal drugs [A. fumigatus by the direct regulation by Sre1 of a cdr1B homolog gene [A. fumigatus cdr1B is regulated by AtrR, which is a transcription factor that also regulates the synthesis of ergosterol and shares target genes with SrbA [. At this point, it would be interesting to explore the existence and role of possible counterpart of AtrR in X. dendrorhous. Importantly, the SREBP pathway plays a role in carotenogenesis through the regulation of carotenogenic genes crtE and crtR , although no SRE elements were detected. However, when comparing transcript levels among strains having an activated SREBP pathway and sre1− mutants by RNA-seq analysis, the transcriptional profile of crtS was similar to that of crtR (higher transcript levels in strains having an active SREBP pathway and lower levels in sre1− mutants), suggesting that crtS might be regulated by Sre1 at some level [crtR− mutants of X. dendrorhous do not produce astaxanthin and accumulate β-carotene, but, although in a lower proportion when compared to the wild-type strain, they do produce ergosterol. This last observation supported that there is an alternative electron donor to the cytochrome P450 reductase enzyme (probably the NADH-dependent cytochrome b5 reductase and cytochrome b5 via) that in X. dendrorhous may assist the production of ergosterol, but not that of astaxanthin [crtR by Sre1 is proof of links between carotenoid and sterol production in X. dendrorhous.As a SREBP homolog, al drugs , 48, whilog gene  that enclog gene . Interesith SrbA . At thisme level . CrtR isme level  as the caxanthin . The regX. dendrorhous. In X. dendrorhous, Sre1 directly regulates the transcription of three genes of the MVA pathway (Fig. crtE in X. dendrorhous, favors carotenoid production [On the other hand, carotenogenesis depends on metabolites from the MVA pathway. It is important to note that in non-carotenogenic organisms, the MVA pathway is usually considered as the first steps of the sterol biosynthesis pathway. Therefore, these genes are generally considered as genes of sterol biosynthesis. However, products of the MVA pathway are also precursors of other metabolites than sterols; for example, carotenoids in non-photosynthetic organisms as in oduction , 67; theX. dendrorhous, like the basidiomycete C. neoformans, Sre1 cleavage depends on the mammalian S2P homolog, the Stp1 protease. However, after the first hydrolysis performed by Stp1, the possibility of a second cleavage of Sre1 by another protease is not ruled out. On the other hand, unlike C. neoformans, X. dendrorhous lacks a SCAP homolog, which is the sterol sensor protein that retains SREBP in the ER membrane. Thus, Sre1 processing in X. dendrorhous is probably SCAP-independent and depends on sterol composition changes that are still unknown. This mechanism is different from what is observed in mammals and in some fungi, where the transcription factor is activated in response to sterol depletion in a SCAP-dependent manner. These differences raise important questions in X. dendrorhous about how this yeast monitors sterol levels and regulates the stability and proteolytic activation of Sre1. On the other hand, it is likely that Sre1 in X. dendrorhous is also regulated by post-translational modifications, as reported in the yeast S. pombe; however, this has not yet been explored in the carotenogenic yeast. Another difference between members of the ascomycota and basidiomycota divisions is the presence of more than one SREBP homolog in ascomycetes. However, in some fungi, not all SREBP homologs have been shown to be involved in the regulation of lipid metabolism or in other functions of the SREBP pathway. Basidiomycetes X. dendrorhous and C. neoformans harbor only one Sre1 homolog, which is involved in lipid homeostasis, which in turn regulates other processes, for example, carotenogenesis in X. dendrorhous. The regulation of carotenoid biosynthesis in X. dendrorhous by the transcription factor Sre1 is thus a novel function of the SREBP pathway and is a regulatory bridge between the biosynthesis of sterols and carotenoids in this species. The recent data regarding the SREBP pathway in X. dendrorhous contributes to our understanding about the regulation of carotenogenesis in this yeast and to our knowledge of SREBP pathways in fungi.The SREBP pathway in fungi is still not fully understood and current knowledge is based on studies on few fungal models. In ascomycetes, the proteolytic activation of the SREBP homolog depends on several components of the SREBP pathway, which include proteins that anchor SREBP to the ER membrane and mechanisms that include at least one protease involved in the processing of SREBP. In


Hypertension and coronary heart disease are the most common cardiovascular diseases, and traditional Chinese medicine is applied as an auxiliary treatment for common cardiovascular diseases. This study is based on 3 years of electronic medical record data from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine. A complex network and machine learning algorithm were used to establish a screening model of coupled herbs for the treatment of hypertension complicated with coronary heart disease. A total of 5688 electronic medical records were collected to establish the prescription network and symptom database. The hierarchical network extraction algorithm was used to obtain core herbs. Biological features of herbs were collected from public databases. At the same time, five supervised machine learning models were established based on the biological features of the coupled herbs. Finally, the K-nearest neighbor model was established as a screening model with an AUROC of 91.0%. Seventy coupled herbs for adjuvant treatment of hypertension complicated with coronary heart disease were obtained. It was found that the coupled herbs achieved the purpose of adjuvant therapy mainly by interfering with cytokines and regulating inflammatory and metabolic pathways. These results show that this model can integrate the molecular biological characteristics of herbs, preliminarily screen combinations of herbs, and provide ideas for explaining the value in clinical applications. Every year, 10.4 million people die of complications of hypertension worldwide . Due to Traditional Chinese medicine (TCM) is recommended as a complementary and alternative therapy in the treatment of hypertension and CHD in China. An existing systematic review shows that TCM herbs can improve the vascular endothelial function of patients with hypertension, inhibit inflammatory reactions, regulate blood lipids, and improve mood. Adjuvant TCM therapy more easily achieves the targeted blood pressure and improves the comfort of patients, protecting target organs and reducing cardiovascular events –9.In the theory of TCM, herbs are combined to form a prescription according to the principle of “Jun-Chen-Zuo-Shi.” Among them, the herbs of “Jun” that play a core role are often applied as drug combinations, usually no more than three herbs for the core disease. On the other hand, there are obvious individual differences in concurrent diseases. TCM theory emphasizes that medication for individual patients should have suitable herb pairing rules. But it is difficult to reflect its curative effect by relying on traditional randomized controlled trials (RCTs). To fully reflect the disease characteristics of the individual and groups of patients, it is necessary to analyze them on the basis of real-world data (RWD) . The traTherefore, we developed a mining method based on RWD to explore effective coupled herbs for the treatment of hypertension complicated with CHD by combining their symptom information and target information. Existing network pharmacology and bioinformatics techniques have been widely used to discover the core targets of herbs, and the understanding of multiple targets with TCM therapy has become increasingly profound , 12. HowSupervised machine learning can aggregate a variety of herb feature information to generate a model, matching the input features of the herbs with the expected output to form a learning function and to complete the classifier after adjusting the parameters by cross-validation. Commonly used models include K-nearest neighbor (KNN), support vector machine (SVM), gradient boosting decision tree (GBDT), Bayesian network (BN), and logistic regression (LR). KNN is more sensitive to the local information in the feature space of the input herbs, while SVM and others reflect its global characteristics.In this study, we established a prescription database and symptom database of patients with hypertension complicated with CHD. First, we used the hierarchical network extraction algorithm to extract the main herbs and symptoms from the database, collected biological information, established a biological network, including herbal compounds, targets, and related disease symptom information, and then used supervised machine learning models compared with the classical Apriori algorithm. The best model was used to evaluate the pertinence of each coupled herb in the treatment of hypertension complicated with CHD.In this study, 5688 electronic medical records (EMRs) of hypertension complicated with CHD collected from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine  were used to extract and standardize the symptoms in the prescription and medical history of TCM  to obtaiHomo sapiens was established.We identified compounds and targets of core herbs from online public databases: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) , SymMap The SymMap database contains TCM symptoms corresponding to herbs and their compounds, and the TCM symptoms of the core herbs can be obtained from the SymMap database and compared with the symptom database in EMRs to screen and determine the symptoms of hypertension complicated with CHD that can be effectively treated by each herb.Q < 0.05 and adj. P < 0.05 were analyzed by the GEO2R tool.The expression data of hypertension and CHD were retrieved from MalaCards  and the The coupling of herbs can increase the pertinence of disease treatment. In this study, the core herbs coupled with each other. The main clinical and biological features of the herbs were quantified to evaluate the action characteristics of the coupled herbs.To evaluate the correlation of the two matched herbs in the prescription database, we established the frequency matrix of the herbs in the prescriptions. The Manhattan distance  between The Jaccard similarity coefficient is used to compare the similarity between the sample sets. We obtained the symptom set of hypertension complicated with CHD and the herbal regulation symptom sets by screening the information of the RWD symptom database and the SymMap database. The Jaccard similarity coefficient was used to compare the similarity between the sample sets. We obtained the TCM symptom information of compound-regulated hypertension complicated with CHD by screening the information of the EMRs symptom database and the SymMap database, which was used to calculate the Jaccard similarity coefficient to evaluate the closeness of the herb-related symptoms to the disease-related symptoms.Oral bioavailability (OB) represents the percentage of oral doses reaching the systemic circulation, and high OB is usually a key indicator for identifying bioactive molecules as having therapeutic properties. In this study, the OB values of the herbal compounds obtained from the SymMap database were added to evaluate the bioavailability of the coupled herb.PyRWR package (version 1.0.0) in Python 3.7.5. The summation was used to evaluate the regulation of disease-related proteins of herbs.A full understanding of human biological function cannot be realized by individual genes, only by the ubiquitous interaction between different genes. Therefore, the random walk with restart (RWR) algorithm was used in PPIN to evaluate the connection degree of herbal targets to disease-related genes. The disease-related genes in PPIN were used as seed node sets, and the restart probability was 0.75 . The staBioconductor and used the algorithm designed by Wang et al. [GOSemSim package [The semantic comparison of gene ontology (GO) annotations provides a method to calculate the similarity between genes and genomes. To measure the similarity between herbal targets and disease-related genes, GO biological process semantic similarity (GoSim) was used to evaluate its effectiveness. We relied on the annotated data provided by g et al. , impleme package  (versionkknn (version 1.3.1), e1071 (version 1.7-8), gbm (version 2.1.8), and klaR (version 0.6-15) packages in R 3.6.3 to complete the model training of KNN, SVM, GBDT, BN, and LR. Ten cross-validations were used to evaluate the performance of the models. We, in particular, randomly divide the performance data of the coupled herbs into 10 subsets, selected one as the test set in turn, and repeated the other 9 training sets 10 times. According to the verification of a large amount of data [We took the lift value of the coupled herbs calculated by the Apriori algorithm as the classification criterion and the evaluation value of the effectiveness of the coupled herbs as the input information for the machine learning tasks  and used of data , 10% canclusterProfiler package [org.Hs.eg.db (version 3.10.0) and analyzed the related genes of the effective coupled herbs by KEGG enrichment analysis. Based on the hypergeometric distribution, Q < 0.05 was considered a significant enrichment pathway, and the same method was used for GO enrichment analysis, Q < 0.05 and adj. P < 0.05 as significant enrichment. Hierarchical clustering (HCT) was used to classify the herbs and pathways to distinguish the biological processes of intervention. At the same time, we introduced genes into Metascape [We used the  package   in the standardized electronic medical record data from the Affiliated Hospital of Shandong University of Traditional Chinese Medicine. GEO2R was used to analyze GSE99325 to obtain HN-related genes. The final AUROC obtained using the KNN model was 94.2% with parameter  studies –32, all The targets of the core herbs were analyzed by GO and KEGG enrichment analysis, and the pathways in which the number of enriched genes for each herb was greater than the quartile of related genes of the herb were retained.P=0.04). In addition, the number of pathways in which the number of enriched genes was greater than the average number of herbs coupled with herbs in different clusters was larger (P < 0.01), indicating that the biological processes of the pairing of herbs in different clusters were more extensive.A total of 215 KEGG signaling pathways are obtained. We established an 18 × 215-dimensional feather profile of the core pathways. HCT divided the KEGG signaling pathways into 7 parts. As shown in For example, Atractylodis Macrocephalae Rhizoma, Ophiopogonis Radix, Radix Paeoniae Alba, Chuanxiong Rhizoma, and Ziziphi Spinosae Semen in K1 regions have fewer enriched genes in the inflammatory pathways, such as the HIF-1 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, and 31 coupled herbs related to them; 16 were combined with herbs in the K5 region to supplement the regulation of the inflammatory pathway. At the same time, compared with the K5 region, the K1 region had fewer enriched genes in glucose and lipid metabolism, such as insulin resistance, insulin secretion, cholesterol metabolism, and the regulation of lipolysis in adipocytes, so it was necessary to combine the K5 region herbs. A total of 37.5% of the coupled herbs contained herbs in K2 and K4, and the herbs in K4 were mainly enriched in leukocyte transendothelial migration, the calcium signaling pathway, platelet activation, and other pathways related to the formation of coronary artery plaques.P=0.04), and the molecular function (MF) and cellular component (CC) categories were not different. From the results of GO enrichment in GO enrichment found that the number of enriched genes in the biological process (BP) category of the KNN-coupled herbs was different from that of the deleted coupled herbs  is overactivated, which induces an inflammatory reaction of target organs such as blood vessels, myocardium, and kidney, resulting in the upregulation of inflammatory cytokines. The coronary artery induces vascular endothelial injury, promotes lipids to enter the intima, causes platelet aggregation, and accelerates the growth of plaques , 37–39. The analysis results of GO and KEGG enrichment showed that the herbal combinations with synergistic effects could be correctly identified by a machine learning model with the quantitative indicators of herbal effectiveness used in this study, but this study also contains many limitations. First, the herbal-related compounds and target information come from public database. Although we used a variety of models to compare and verify to find the best model, the data deviation of the database may have a potential impact on the research results. Second, although the TCM symptom information of patients has been included in the evaluation system, it fails to make full use of the clinical information of individual differences in EMRS. Third, the sample size for model training is still limited, and information on the interaction between compounds has not been added, so future research needs to improve the performance of the model to design a more effective adjuvant therapy for TCM.This study was based on data from 5688 EMRs of hypertension complicated with CHD in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from 2014 to 2017, and a patient symptom dataset and prescription dataset were established. Eighteen commonly used herbs were obtained, and their biological network was established. Using the interaction information between nodes in the network, we established quantitative data on the effectiveness of coupled herbs. An effective coupled herb screening algorithm based on a machine learning model was proposed, and a total of 70 coupled herbs were obtained. Based on the analysis of various herbs at the pathway level, they can play a multilevel biological regulatory role in controlling the inflammatory response and regulating energy metabolism for hypertension complicated with CHD and its TCM symptoms. Combined with complex networks and machine learning, this study explored the potential law of herbs in the treatment of hypertension complicated with CHD and predicted the curative effect of coupled herbs, providing a direction for summarizing the TCM EMRs and explaining the TCM rules.


Many databases focusing on pathway mining have been released with one major deficiency, as they lack to develop marker sets that target only genes controlling a specific pathway or certain biological process. Herein, we present the PlantPathMarks database (PPMdb) as a comprehensive, web-based, user-friendly, and interactive hub for pathway-based markers in plant genomes. Based on our newly developed pathway gene set mining approach, two novel pathway-based marker systems called pathway gene-targeted markers (PGTMs) and pathway microsatellite-targeted markers (PMTMs) were developed as a novel class of annotation-based markers. In the PPMdb database, 2,690,742 pathway-based markers reflecting 9,894 marker panels were developed across 82 plant genomes. The markers include 691,555 PGTMs and 1,999,187 PMTMs. Across these genomes, 165,378 enzyme-coding genes were mapped against 126 KEGG reference pathway maps. PPMdb is furnished with three interactive visualization tools  to visualize, map, and compare the developed markers over their KEGG reference pathway maps. All the stored marker panels can be freely downloaded. PPMdb promises to create a radical shift in the paradigm of the area of molecular marker research. The use of PPMdb as a mega-tool represents an impediment for non-bioinformatician plant scientists and breeders. PPMdb is freely available at  Inspiration drives scientists to develop intelligent approaches to extract hidden knowledge behind these massive amounts of data. Biodata mining attempts to find novel, reliable, useful, and meaningful insights from these vast amounts of data. Over the last decades, the scope of biodata mining has expanded from genome-mining to phenome-mining approaches2. Its applications outside of basic research have become extensive to study plants’ genetic diversity and improve economic crop breeding programs. Generally, plants produce various chemical compounds involving nutritional or medicinal benefits3. Our understanding of plant natural products’ biological pathways is still insufficient; however, scientists assume that genomic and metabolomic information can provide clues about unidentified enzymes and reactions involved in a particular biosynthesis process4.Today, an enormous amount of released biodata collections stimulates the development of computerized applications worldwide3. This duplication drives the availability of genes either as homologous or paralogous. Due to the phylogenetic relationship of homologous genes, they continue to preserve their core functions. Gene products are often mapped to many pathways revealing the same catalyst properties under different reaction conditions or showing the same reaction but in distinct subcellular locations5. Such an association provides a mechanism to study genes’ roles; their expression may be regulated spatially or temporally due to evolutionary implications on plants, gene products, gene functions, and, finally, developmental stages1.Plants have undergone complicated evolutionary events that have resulted naturally or are due to human-made plant breeding experiments and finally lead to polyploidy or genome duplication6. Nowadays, pathway analysis is a flourishing research area in systems biology. It relies on extracting knowledge from raw data generated from high-throughput sequencing technologies by building a model that describes and summarizes underlying biological processes. These high-throughput technologies mostly provide a list of differentially expressed genes (DEGs) between a control and a case of interest. Unfortunately, generated DEG data are usually removed from their biological context, causing the resultant genes to require further validation against biological pathways to prove their biological significance7. Notably, each of these genes may be a part of many pathways, which usually start with the expression of a gene of a particular ligand and is terminated with an alteration in the concentration of a specific signaling micro-molecule8.A biological pathway is broadly defined as a series of actions among cell molecules that lead to a particular product or cellular change. It can also trigger novel molecule assembly, turn genes on and off, or drive a cell to move. Any biological pathway comprises a cluster of genes that exhibit identical or complementary biological functions9. For decades, massive studies have aimed to determine a few differentially expressed marker genes participating in certain natural products’ biosynthesis processes or conferring a particular phenotype12. Nevertheless, these methods may not be accurate and may lack in capturing an in-depth snapshot of biological processes. Therefore, studies focusing on gene sets belonging to a particular pathway to investigate and explain phenotypic changes have gained increased popularity. These methods are more acceptable, as they assess the significance of a group of predefined genes with shared biological functions to explain specific phenotypes15.The dilemma of plant trait/metabolite characterization and improvement based on single or few genes targeting marker selections remains challenging, and many approaches have been developed to tackle these issues5. Therefore, instead of focusing on specific gene-based marker development, our proposed pathway gene set mining (PGM) approach is critically more appropriate and promises to create a radical shift in the paradigm of molecular markers as a research area. The dramatically increasing number of sequenced plant genomes is supposed to reveal more basic architectural principles of biosynthetic pathways for generating chemical diversity of natural products. Based on released knowledge on interactions between genes and their phenotypic reflections, many publicly available databases have been developed for storing this pathway mining information16.In contrast to traditional gene-based methods, pathway-based methods combined with biological knowledge can help gain a better understanding of functional insights into phenotypic differences. Pathway-mining approaches attempt to rectify such drawbacks by utilizing the available biological knowledge base about the structures and operations of biological pathways accompanying computational methods3. Common types of analyses include pathway enrichment analysis and the analysis of gene ontology and gene–gene interactions. Over the last decade, many powerful resources for studying metabolic pathways in plants, such as KEGG17, Gramene18, Plant Reactome19, MapMan20, MetaCyc21, Plant Metabolic Network22, and BioCyc23, have been developed. These databases try their best to integrate the most available information when they were initially launched. Their construction process includes manual curation and computational efforts, regular screening of newly released publications, and updating of interactors. Despite extraordinary efforts, no single database is fully equipped with all the preferred information and may not accommodate all the necessary bioinformatics tools. Notably, all these resources have the significant limitation of missing genome-scale marker sets that target only the genes responsible for a specific pathway or that are involved in/control a particular biological process at the level.After numerous plant genomes are fully sequenced, scientists often search for an efficient scheme that can analyze their data and answer certain biological questionsGene sets involved in a particular pathway often work together in an orchestral pattern to reflect a specific biological function. Herein, we proposed a novel PGM pipeline/approach to develop a specific marker set for each pathway at the genome-scale level in plant genomes.25. Due to their unique characteristics , as well as their essential roles in many biological functions , microsatellites have become one of the most preferred choices among all genetic markers because they afford a molecular basis for plant genome evolution and demonstrate fast acclimatization to many abiotic stresses26. Therefore, we proposed a mining schema of microsatellites within gene sets involved in all plant genome pathways as a robust and functional approach to develop a novel marker system called pathway microsatellite-targeted markers (PMTMs).Microsatellites, also known as simple sequence repeats (SSRs), are a set of one to six nucleotides that are repeated tandemly and classified either into perfect, imperfect, or composite repeatsUltimately, this study aims to develop a comprehensive hub for two novel pathway-based marker systems coined pathway gene-targeted markers (PGTMs) and pathway microsatellite-targeted markers (PMTMs) in plant genomes. Furthermore, the developed markers were mapped against reference KEGG pathway maps interactively. The use of these powerful portal represents an impediment for non-bioinformatician plant scientists and breeders.ftp://ftp.ncbi.nlm.nih.gov/). Furthermore, we downloaded all KEGG gene annotations available on the KEGG database of these plant genomes  in addition to all KEGG pathway reference maps (https://www.genome.jp/kegg/).To build a comprehensive pathway-based marker database in plants, we retrieved complete genome sequences and annotations of 82 various plant species, including 72 eudicots and 10 monocots. The sequences and annotations were downloaded as GenBank files from NCBI’s FTP site  splitting the retrieved genomic data into sequence and annotation files, (b) classifying genomic sequence into genic or intergenic, (c) sub-classifying genes into enzyme coding or non-enzyme coding, (d) mining microsatellites on enzyme-coding genes only (this step exclusive to PMTM), (e) designing primers and classifying markers into PMTM or PGTM, (f) mapping all designed primers against KEGG reference maps17 besides linking associated information (sequence and annotation) to all pathway-mapped markers, and (g) data transforming into the PPMdb database format and implementing all datasets into the PPMdb web interface  core database, we integrated two powerful tools called MIcroSAtellite identification (MISA)For effective marker development, we used a straightforward strategy to boost data analysis and marker generation processes. Before we began, we established in-house “Perl and Shell” scripts to convert all retrieved genome sequence and annotation data and unify input and output files used for the development of the PPMdb database.https://www.genome.jp/kegg/ genes.html) to sub-classify genes into enzyme coding or non-enzyme coding. Thereafter, perfect and compound microsatellite motifs were identified using the MISA tool27 according to the following parameters: mono- (≥ 9), di- (≥ 4), tri- (≥ 3), tetra- (≥ 3), penta- (≥ 2), and hexa-nucleotide (≥ 2). The parameter was set as ≥ 2 repeats interrupted by ≤ 20 bp for the characterization of compound microsatellite motifs29.Initially, the data retrieved from the 82 plant genomes were dissected to classify each genome into genic and intergenic according to the genome’s annotation information. This classification step was achieved with the aid of the gene-finding format (GFF) file for each genome. The gene ID list for each plant was further searched against the KEGG database (28 according to the following criteria: a) optimal primer length of 20 bp; b) optimal melting temperature of 55 °C; c) product size range of 90–500 bp for PMTM primers and 100–1000 bp for PGTM primers; and d) a 50% G/C content. Then, e) a unique primer ID was assigned.PMTM and PGTM primers were then designed for all enzyme-coding genes for all plant genomes using the Primer3 softwarehttps://www.genome.jp/kegg/pathway.html) by using in-house “Perl and Shell” scripts to integrate and localize our developed PMTM and PGTM primers and build a map set of marker-enriched pathways for each plant. Each map contains a pathway ID, pathway image, IDs of mapped enzymes (highlighted), PMTM/PGTM markers associated with mapped enzymes, annotation information of each marker, and other information related to this primer . All developed maps were visualized in an attractive user form using Jquery ImageMapster Plugin30.All developed PMTM and PGTM primers were mapped against the KEGG pathway reference maps . The PPMdb database was developed with aid of the LPPM (Linux + Perl + PHP + MySQL) web application platform; finally, JavaScript, CSS, and HTML languages were used to design a user-friendly interactive web interface.All generated data of PMTMs, PGTMs, and maps were further processed to build a standard information platform for all marker types before integrating them into the PPMdb SQL database. With the aid of the in-house “Perl and Shell” scripts, we batch processed all developed markers, maps, and any associated data and converted them into a consistent format. Additionally, we uploaded these scripts to online open-source housing website (GitHub) to make it available for all users at the following link , Database Search, Map Browse, JBrowse, Statistics, Data Resources, Bulk Download, Species Comparisons and Manual. Under these pages, plants are taxonomically grouped or sorted alphabetically to improve the convenience of exploration and selection.The Home page introduces PPMdb as a comprehensive database along with its PGM pipeline by describing the PPMdb database sections, in addition to providing users with a “PPMdb Quick-Access; Analyzed Genomes in PPMdb Database Section” option for all analyzed plant genomes. In the “PPMdb Quick-Access” section, plant genomes analyzed in the PPMdb database are categorized according to plant type/class (dicot plants or monocot plants). Under each group, interactively, the plants are sorted alphabetically to enable straightforward access and searches within each plant genome independently through “Map Browse” or “JBrowse” quick-access links Fig. .Figure 2Arabidopsis lyrata), from the available drop-down menus to effortlessly access and retrieve all marker and map data stored in the PPMdb database. (b) The KEGG Pathway Maps Overview section, which provides users the necessary information for each pathway, such as pathway ID (hyperlinked to KEGG ref. pathways), pathway class , pathway sub-class , and pathway map. The search results of this section are designed in a user-friendly style containing essential descriptive information for each marker   the Search section, which enables users to obtain results by selecting the following types of interest keywords: pathway name , marker type , and organism name  Fig. .Figure 3In addition, general statistic tables are presented to summarize the total number of designed primers, number of PGTM primers, number of PMTM primers, and number of failed designed PMTM primers within the selected pathway of a particular plant. All search results can be downloaded freely for each plant straightforwardly as a CSV file Fig. .The Map Browse page offers users with searching utility in two styles inside the same page: (a) selecting the plant of interest from the page-side plant list for a direct access of pathways and markers, (b) selecting the plant of interest from drop-down menus to directly access and obtain all markers and maps. In both styles, the search results are presented in a convenient, visualization-supported, and well-dissected manner involving essential information for each pathway, such as pathway ID (hyperlinked to KEGG ref. pathways), pathway class , pathway sub-class , pathway map browse , pathway description, and pathway map Fig. .Figure 4The “JBrowse” page enables the users to visualize and map all identified microsatellite motifs besides all developed PMTM and PGTM markers against the Refseq CDS for each plant genome by selecting the plant of interest from the page-side plant list. The mapped microsatellite motifs and developed PMTM/PGTM markers were linked to its essential information Fig. .Figure 5The ‘Statistics’ page was designed to provide users a primary indication of the taxonomy ID, number of mapped pathways, number of mapped enzymes, number of designed primers, number of PGTM primers, number of PMTM primers, and number of failed designed PMTM primers for each plant species stored in the PPMdb database.The Data Resources page provides users with hyperlinks of all types/formats of the data utilized to build the PPMdb database. The page includes hyperlinks of taxonomy ID, Genbank file, Fasta file, GFF file, and KEGG gene annotation for each plant species analyzed within the PPMdb.Arabidopsis lyrata), pathway name , and marker type , from the drop-down menus straightforwardly in a convenient manner.The Bulk Download page enables users to download all developed primers and their secondary information under a specific pathway in the organism of interest through the selection of organism name  rather than the effect of distinct genes3. Based on this deep understanding, this study's scientific vision provides a platform for the development of pathways-based markers toward designing future studies that aim to disentangle the causal biological pathways and their phenotypic reflections.Genes or gene families involved in a biological pathway are often switched on or off together to reflect a particular biological function or elucidate specific phenotypes34, were successfully implemented in our developed PGM pipeline to develop the PMTM system as a novel class of pathway-targeted markers. In silico microsatellite mining on a plant genome scale is expected to advance our understanding and elucidate the functional impact of microsatellites within biological pathways and, consequently, in the context of systems biology35. This study also aimed to develop and map pathway-based genetic marker panels that support and boost molecular breeding programs, genetic diversity, and genetic characterization studies. From this point of view, the value of our developed marker panels will need to be assessed in the context of availability and mining of biological pathway information to generate knowledge that is more actionable rather than more complex.As a part of this vision, microsatellites have been utilized as one of the most common sources of genetic markers, which have served as a keystone for massive genetic studies due to their robust and unique features. Microsatellites, which demonstrate their efficiency in broad applications, such as diversity studies, genome mapping, molecular breeding, and molecular phylogenyIn summary, we present PPMdb as a comprehensive database for pathway-based markers in plant genomes. To our knowledge, PPMdb is the first portal providing unique pathway-targeted marker panels not presented in any previous database. PPMdb classifies developed marker panels based on their biological functions. This advantage allows researchers to deeply focus on the functional roles of utilized markers and may explain many phenotypic variations in the future. Our developed PPMdb is substantially different from similar plant genetic marker databases. The PPMdb will regularly be updated by integrating any newly released plant genomes.Furthermore, the PPMdb graphical user interface and functionality will always be enhanced and continuously supported with new tools and technologies. Overall, we believe that the PPMdb hub will serve as a starting point or cornerstone for pathway-targeted marker research. In addition, we believe that PPMdb will catch great attention from a wide range of plant scientists in different disciplines, including genetic diversity, species characterization, population genetics, genome mapping, and targeted trait improvement.