Houttuynia cordata Thumb  is a traditional herbal medicine that possesses favorable antiviral properties. As a potent folk therapy used to treat pulmonary infections, further research is required to fully elucidate the mechanisms of its pharmacological activities and explore its therapeutic potential for treating pneumonia caused by SARS-CoV-2. This study explores the pharmacological mechanism of HC on pneumonia using a network pharmacological approach combined with reprocessing expression profiling by high-throughput sequencing to demonstrate the therapeutic mechanisms of HC for treating pneumonia at a systemic level. The integration of these analyses suggested that target factors are involved in four signaling pathways, including PI3K-Akt, Jak-STAT, MAPK, and NF-kB. Molecular docking and molecular dynamics simulation were applied to verify these results, indicating a stable combination between four metabolites  and six targets . These natural metabolites have also been reported to bind with ACE2 and 3CLpro of SARS-CoV-2, respectively. The data suggest that HC exerts collective therapeutic effects against pneumonia caused by SARS-CoV-2 and provides a theoretical basis for further study of the active drug-like ingredients and mechanism of HC in treating pneumonia.Used in Asian countries, including China, Japan, and Thailand,  Houttuynia cordata Thumb  is a traditional herbal medicine used in Asian countries, including China, Japan, and Thailand. It exhibits promising antiviral activities towards clinically enveloped viruses, such as influenza virus, herpes simplex virus-1(HSV-1), and human immunodeficiency virus-1 (HIV-1) in vitro [in vitro . This hein vitro ,4. As suin vitro . As a poHC consists of a large number of drug-like bioactive compounds that have been isolated in previous studies and could play a vital role in antiviral and anti-inflammatory medicine ,7, namelBased on the potential antiviral and anti-inflammatory properties of HC multi-compounds, HC has been used to treat different types of pneumonia caused by bacteria and viruses . CoronavAcute lung injury (ALI) was found to cause pneumonia, matrix lesions, and pulmonary fibrosis initiated by SARS-CoV-1 2003), avian influenza (2008), swine influenza (2009), and SARS-CoV-2 (2020) 003, avia. Both SASARS-CoV-2 infection is a critical factor in the onset of pneumonia and death. HC is a time-honored TCM herb widely used to treat both bacterial and viral respiratory diseases, and it was purported to prevent and treat SARS-CoV-1 infection in 2003. In light of this, effective herbal compounds for SARS-CoV-1 were posited to be beneficial in the treatment of COVID-19 caused by SARS-CoV-2 since homology between the above two viruses is around 80% . As a foHC was searched to identify its active ingredients and pharmacological targets using an in silico approach. As a result, the efficacy of active compounds of HC prediction was established in public databases, including the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), the TCM Integrated Database (TCMID), The Encyclopaedia of Traditional Chinese Medicine (ETCM), and the Bioinformatics Analysis Tool for Molecular Mechanism of TCM (BATMAN-TCM) . In addihttps://pubchem.ncbi.nlm.nih.gov/ (accessed on 1 August 2021)) to identify relevant targets, and (3) using the ETCM to acquire the target genes associated with the selected active compounds with a score >0.8. The target set was derived after combining the search results and removing duplication and certain bioactive components with suitable targets with a score >0.8 [The active components of a drug interact with respective targets to inhibit their biological function. An HC target gene set was acquired by searching several databases: (1) acquiring gene symbols and related information about HC targets from TCMSP; (2) importing selected candidate components into the PubChem database ). Finally, an intersection between genes retrieved through GeneCards and DEGs of reprocessed data series GSE152075 was obtained as pneumonia-related targets [Genes related to pneumonia caused by SARS-CoV-2 were screened, selected, and obtained from the Gene Expression Omnibus (GEO), GeneCards database, and other references. First, the dataset was mainly derived from reprocessing high-throughput sequencing data downloaded from the GEO database. The expression profile of GSE152075 involved 430 SARS-CoV-2 infection samples and 54 negative control samples, which were analyzed on the GPL18573 Illumina NexSeq500 platform (Homo sapiens) . The limtermined . The seled genes . Second, targets .The HC targets intersecting with pneumonia-related targets were taken as HC–pneumonia common targets, which can be visualized with Venn 2.0. The common targets were imported into the STRING platform (version 11.0). The species was then set to Homo sapience and the minimum required interaction score to the highest confidence of 0.9 in order to retrieve the concise protein–protein interaction (PPI) information for the next step of the analysis. The PPI network was also visualized with Cytoscape software .The Herb-Metabolites-Targets-Disease (HMTD) network was built on the interactions among drug (HC), ingredients, gene symbols, and disease (pneumonia) and then visualized by Cytoscape software. The nodes’ varying shapes represent common pneumonia and HC active ingredient targets. The nodes are linked by edges (lines), indicating interactions between nodes. As such, the topological features of a network can be used to predict the targets, while the candidate hub notes analyzed by Cytoscape’s Network analyzer tool can be identified by calculating the two topological features of betweenness and degree. Betweenness is the number of shortest paths through the node (the shortest distance between two nodes) and degree is the sum of the number of edges connected the node . Closenep-values from each of 20 clusters, wherein nodes that share the same cluster ID are typically close to each other. Enrichment analysis was also carried out in PaGenBase to demonstrate disease targets–organs location. In addition, a further enrichment analysis was performed in DisGeNET to identify the relevant diseases. The results of the KEGG enrichment analysis were used to construct a KEGG pathway network to determine the proteins involved in the treatment effects of HC. Based on the STRING results, the gene–pathway of HC against pneumonia was constructed to delineate the various pathways and key targets in order to explore the potential mechanisms underlying the effect of HC on the treatment of pneumonia [Depending on the hub genes (core targets), the Gene Ontology (GO) biological processes, and the Kyoto Encyclopaedia of Genes and Genomes (KEGG), metabolic pathway enrichment analyses were carried out on the pneumonia–HC common targets. GO source and GO enrichment can divide the functions and products of genes into three categories, namely, molecular function (MF), biological process (BP), and cellular components . The bioneumonia .https://www.wwpdb.org/ (accessed on 11 August 2021)). The key target proteins were purposefully selected with a resolution smaller than 2, and their crystals were imported into PyMOL 3.0 software [Molecular docking is a useful tool to predict and design new drugs. As such, the computational validation of ingredients–targets interactions were confirmed by exploring their binding modes via this process. The computational modeling of intermolecular combinational patterns between target proteins and herb ligands can predict the potential binding modes. Depending on the degree of 67 common targets in the PPI network and the important reference, four active ingredients, including afzelin, apigenin, kaempferol, and quercetin, and six targets, i.e., dipeptidyl peptidase-4 (DPP4), neutrophil elastase (ELANE), Heat shock protein (HSP90AA1), IL6, mitogen-activated protein kinase 1 (MAPK1), and Serpin Family E Member 1 (SERPINE1), were selected to simulate the ingredients–targets interactions for verification of molecular docking. Molecular 2D structures for the molecular ligands and active ingredients were downloaded from PubChem databases. The crystal structures of the key target proteins DPP4 (PDB ID: 4N8D), ELANE (PDB ID: 4WVP), HSP90AA1 (PDB ID: 5J2X), IL6 (PDB ID: 4CNI), MAPK1 (PDB ID: 6QAH), and SERPINE1 (PDB ID: 7AQF) were selected from RCSB PDB  and NPT  running for 100 ps. The system was well-equilibrated through NVT and NPT equilibration at 300 K and 1 bar. Finally, MD simulations of the complex were carried out for 100 ns. The Verlet cut-off scheme and a Leap-frog integrator with a step size of 2 fs were applied. The final analysis of molecular dynamics included the root mean square deviation (RMSD) of protein and molecule and the interaction energy between the protein and small molecules, which were calculated by GROMACS 2020.1.The molecular dynamics simulations of these complexes were performed using Gromacs 2020.1, in which the charm36-jul2020 force field was chosen. The complex was solved in TIP3P water and immersed in a dodecahedron box extending to at least 1 nm of the solvent on all sides. The system was neutralized by NaWhile 50 metabolites were shown in the TCMSP by searching the keywords “Houttuyniae Herba”, only 7 satisfied the criteria of OB ≥ 30% and DL ≥ 0.18. Other metabolites were obtained by searching ETCM, BATMAN-TCM, and TCMID. A number of metabolites had already been established as the most effective components of HC throughout the relevant literature . These wBioinformatics analyses on the expression profile microarray data GSE152075 and GSE1739 containing positive SARS-CoV-2 and negative control samples were performed to identify DEGs between SARS-CoV-2 infection and negative control by the limma package in R Bioconductor. This step identified 9685 DEGs from data series GSE152075 and 1791 DEGs from data series GSE1739. Other disease data sources, such as GeneCards and DisGeNET, were combined with the GEO results to remove duplicates, resulting in the identification of 11,027 targets related to SARS-CoV-2. Since SARS-CoV-2 causes not only pneumonia but also multiple organ failure, neutrophilia, and organ and coagulation dysfunction, pneumonia-related targets were acquired by searching GeneCards with the keywords “SARS-CoV-2” and “pneumonia”, resulting in 786 pneumonia-related targets. The intersection between SARS-CoV-2-related targets  and pneumonia-related targets (786) resulted in 739 elite targets related to pneumonia caused by SARS-CoV-2.The intersection between HC-related targets (463) and pneumonia-related elite targets (739) resulted in an HC–pneumonia common target set with 67 genes. This common target set was imported into Cytoscape v3.5.0 to construct an Herb-Metabolites-Targets-Disease (HMTD) network, as shown in p-values from each of 20 clusters, wherein nodes that share the same cluster-ID are typically close. The top signaling pathways mainly include interleukin-4 and interleukin-13 signaling, the AGE–RAGE signaling pathway in diabetic complication, positive regulation of cell migration, spinal cord injury, IL17 signaling pathway, reactive oxygen species metabolic process, leukocyte activation involved in immune response, Th17 cell differentiation, cytokines and inflammatory response, and epithelial cell migration  and the y-axis the GO term. The enrichment analysis in PaGenBase demonstrated disease targets-organs location, such as lung, smooth muscle, cardiac myocytes, bone marrow, bronchial epithelial cells, liver cells, and spleen. The analyses of the most-associated diseases showed immunosuppression, fatty acid disease, respiratory distress syndrome, pneumonitis, endothelial dysfunction, respiratory syncytial virus infection, liver failure, middle cerebral artery occlusion, bacterial infections, acute myocardial infarction, lung diseases, cardiac arrest, myocardial ischemia, and herpes simplex infections. To analyze the significance and importance of key targets in the pathways involved in the treatment effect of pneumonia, 10 key pathways, determined according to gene counts and adjusted p values from the KEGG enrichment analysis and related targets, were used to construct a KEGG key pathway network , kaempferol (CAS no. 520-18-3), afzelin (CAS no. 482-39-3), and apigenin (CAS no. 520-36-5). The protein structures of key targets were acquired online from RCSB PDB, including DPP4, ELANE, HSP90AA1, IL6, MARK1, and SERPINE1, based on STRING interaction analysis and importance reference. Docking analysis of the metabolites and proteins above showed the docking patterns and binding affinities . The docIn order to verify the stability of the docking structures, we selected DPP4-–kaempferol, MAPK1–afzelin, SERPINE1–apigenin and SERPINE1–quercetin complexes for dynamic simulation analysis. As shown in In conclusion, we used docking and dynamics simulation to explore ingredient-target prediction. Our results showed that the DPP4–kaempferol, MAPK1–afzelin, SERPINE1–apigenin, and SERPINE1–quercetin complexes had good docking fractions, that the protein and small-molecule positions were stable during the simulation process, and that the interaction energy was lower than −100 kJ/mol. Accordingly, our studies provide a reference for subsequent experimental design.+ and CD8+ T cells in vitro [While previous studies demonstrated that HC exhibited antiviral activity through inhibiting SARS-CoV-1 3CLPRO and RdRp and also stimulated the proliferation of CD4in vitro , furtherTCM considers an individual or patient an integrative complex with dynamic states, demonstrating multiple biological targets and focusing on integral therapeutic efficacies . InflammDepending on the above active metabolites, target searching in the databases of TCMSP, PubChem, and ETCM resulted in the acquisition of 463 targets related to the above seven core active metabolites. The disease targets were obtained by identifying key genes in SARS-CoV-2 infection and uncovering their potential functions by re-processing the expression profiling of high-throughput sequencing of GSE152075 from the GEO database to form a solid basis for the ensuing analysis. The bioinformatics analyses generated 11,207 DEGs, contributing to our understanding of the molecular mechanism underlying the advancement of SARS-CoV-2 infection.Since SARS-CoV-2 causes multiple organ failure , the intThe pathogenesis of inflammatory diseases is associated with the overproduction of the above mediators. NO by endothelial nitric oxide synthase (NOS3) is implicated in vascular smooth muscle relaxation and mediates vascular endothelial growth factor (VEGF)-induced angiogenesis in coronary vessels, which may explain the many complications occurring in COVID-19 patients . The mamThe GO enrichment results confirmed that HC mainly regulates biological processes in response to stimulus, regulation of the cellular process, response to chemicals, response to stress, cell communication, signal transduction regulation of macromolecule metabolic process, and regulation of nitrogen compound metabolic processes. The results of KEGG enrichment uncovered the numerous signaling pathways involved in the development and progression of pneumonia, including PI3K-Akt, HIF-1, IL-17, TNF, TLR, JAK–STAT, NOD-like receptor, or MAPK. Previous studies further suggest that the anti-inflammatory properties of HC extract may arise from the inhibition of pro-inflammatory mediators by suppression of NF–κB, and MAPK signaling pathways by binding their key proteins in pathways with HC active metabolites, as outlined above ,39,41.Based on the PPI, HMTD, and KEGG enrichment analysis, 6 of 21 core targets, namely, DPP4, ELANE, HSP90AA1, IL6, MAPK1, and SERPINE1, were selected to dock with four main ingredients identified in a series of analyses of this study. As such, the docking results agreed with the intermolecular interactions. Despite extensive prior efforts to elucidate the metabolism and effects of oral administration of HC in patients in vivo, the mechanism remained largely unclear. DPP4, HSP90AA1, and SERPINE1 are related to immune response and linked to macrophages which promote the generation of inflammatory factors, such as TNF-α and IL6 . DPP4 isELANE genes can express up to a five-fold level against SARS-CoV-1 infection, causing lung proinflammatory cytokines . Recent Molecular docking of 6 out of 21 core targets and four screened active ingredients was applied to validate the results of the network pharmacology. The active ingredients which target these proteins include quercetin, kaempferol, afzelin, and apigenin. Docking within the docking pockets between active ingredients and target proteins was visualized by AutoDock Vina and PyMOL software. The binding affinities of the docking results ranged from −6.7 to −9.4 kcal/mol, indicating stable binding. Afzelin showed the greatest binding affinities with MAPK1 , followed by DPP4 . In contrast, MAPK showed a better average binding affinity with these four active ingredients, indicating that HC protected against acute lung injury, mainly through the suppression of the MAPK/NF–kB pathway . The docNetwork pharmacology has been widely used for TCM mechanism research owing to its efficacy in analyzing the complicated relationships among multiple ingredients and multiple disease targets. Docking technology can visualized the binding modes of active ingredients with disease-related key target proteins and provide guidance for researchers’ selections of active ingredients for in vivo or in vitro experiments . As showThis study confirmed the potent therapeutic effect of HC, a time-honored herb widely used in Asian countries to treat pneumonia. The potential mechanisms of HC were revealed by employing both network pharmacology and molecular computational analyses. Our results offer a very different perspective in terms of modern pharmacological mechanisms which may assist in the global fight against the COVID-19 pandemic. However, while HC is an effective herb for pneumonia treatment, the optimal dose for inducing remission with low toxicity needs to be determined. Moreover, further animal and cell models are necessary to verify the relevant pathways and targets.


Under average temperature and natural light, the groupers were cultivated with feeds of iso-nitrogen and iso-lipid for 10 weeks. The results showed that soybean meal feeds at all experimental levels had negative effects on fish gut physiology and growth performance. Typical enteritis features and fluctuations of immune system occur, which can be observed in the enzyme activities of total superoxide dismutase and lysozyme and in the contents of immunoglobulin M, complement 3 and complement 4. 16SrDNA high-throughput sequencing indicated that it greatly influenced the gut flora with the abundance of maleficent bacteria, like Vibrio, amplified with increasing dietary soybean meals. According to the “3 + 2” full-length transcriptome sequencing, soy meals at the three experimental levels inhibited the key gene expressions of tryptophan metabolic pathway in fish gut, however, there are some differences in the types of key genes that are inhibited. The canonical correlation analysis showed that the changes in key gene expressions in tryptophan metabolic pathway had a positive correlation with the expressions of pro-inflammatory genes (P < 0.05) and negatively correlated with the expression of anti-inflammatory genes (P < 0.05). It is speculated from this study that tryptophan metabolism is closely related to fish soy meal-related enteritis, and the abnormal tryptophan metabolism caused by intestinal flora imbalance may play an important role. In the future research, we can further study the tolerance of fish to soy meals feed from two aspects of tryptophan metabolism and intestinal flora changes.The substitution of high-level soy meals for fish meal (FM) generally leads to fish enteritis, accompanied by significant variations in gut flora. Relevant studies have pointed out a close relationship between tryptophan metabolism mediated by gut flora and vertebrate inflammatory bowel disease. Present study examines the role of tryptophan metabolism and gut flora profile in fish enteritis caused by different soybean meals. The 960 groupers were randomly assigned into 4 groups ( In recent years, the total amount of aquaculture has continued to increase, but the fish meal (FM) output almost remained unchanged. Since FM presents to be a key source of protein for aquaculture, aquaculture farmers urgently need new and sustainable alternatives for it . Thus, aAccording to former research, the influence of SBM on fish enteritis varies with its anti-nutritional factors (ANFs), including phytates, protease inhibitors, saponins, lectins, phytosterols, and oligosaccharides . The hisIn aquatic feed, soy products used mainly include SBM, soybean protein concentrate (SPC), as well as fermented soybean meal (FSBM). SBM, a by-product of soybean oil extraction, contained various ANFs mentioned above. SPC is a product made from soy by extracting oil and low molecular weight soluble protein components by alcohol. Compared with SBM, SPC removed most ANFs and is a good plant protein source in the fish and shrimp diet . HoweverPrevious studies on fish SBMIE mostly focused on the influence of ANFs in SBM on gut health. But researchers found that the role of ANFs is complex, such as fish enteritis induced by soy saponins may be related to the basal protein source ; or doseTryptophan is an essential amino acid. In recent years, lysine and methionine have been widely used in formula diets, making tryptophan the main limiting amino acid in diet . In aquaPearl gentian grouper is a typical marine carnivorous fish and is the important cultivated varieties in China and many places in the world. Its meat is tender, grows fast, has strong disease resistance, obvious hybridization advantages, and good market price. It can also be used as an ornamental fish, and has a broad market prospect. This study analysed the effects of different soy meals with high substitution levels on the growth physiology and gut tryptophan metabolism pathway of pearl gentian grouper, and preliminary analysed the gut flora profile variations. Present research offers a theoretical basis for the study of gut health problems caused by the substitution for FM with high level of plant proteins.The experimental diets and their chemical compositions are shown in –1, and the dissolved oxygen involved in this study was ≥7 mg L–1. Within the first 14 days, 60% of the water in each container was exchanged every day, and after that, nearly 100% of the water was exchanged every day.The healthy juvenile groupers, which weighed about 9 g, were bought from a commercial hatchery in Zhanjiang City, Guangdong, China. Before the experiment, 960 groupers were given 1 week to adapt to the experimental conditions, after which they were offered nothing to eat for a day. The researchers then anaesthetised the groupers with eugenol and divided them into four groups. According to size, fishes were randomly placed in 1,000 L cylindrical glass tanks. Every tank contained 60 fish. The experimental feeds were offered from 8:00 a.m. to 4:00 p.m. every day for 10 weeks, with four replicates. The uneaten feeds were siphoned to calculate the weight of the intake feeds, and the feed consumption ratio was determined . The feeThe study was supported by the Expert Committee of the Fisheries College of Guangdong Ocean University. The research methods followed all applicable regulations and guidelines. Before sampling, the studied groupers were starved for 24 h. Then, they were anaesthetised with eugenol . After counting and weighing all fish in each tank, their specific growth rate (SGR), weight gain rate (WGR), hepatosomatic index (HSI), SR, and feed conversion ratio (FCR) were determined. To determine the enzyme activity, the researchers randomly selected six groupers from each water tank, drew blood from their tail veins, and stored it at 4°C for a night, before using a centrifuge at 3,500 rpm for 10 min to obtain serum. The blood was then stored at −80°C, preparing for analysing the enzyme activity. The hindgut tissues were excised, and any mesenteric and adipose tissues were removed. Hindgut samples were cut into small pieces and put in test tubes filled with RNAlater at 4°C for a night before being stored at −80°C for gene expression analysis.For gut flora and transcriptome sequencing, the researchers randomly chose eight groupers. The hindgut tissue samples were obtained as mentioned above and placed in cryopreservation tubes, then directly put into liquid nitrogen, half of which was employed for 16S high-throughput sequencing, while the rest for transcriptome sequencing.After the diet experiment, three fish were randomly chosen from every container. The DI sample was stored in 4% paraformaldehyde universal tissue fixative, which belongs to the Servicebio Technology Company of Wuhan, from China, for 24 h before being H&E stained. The stained sections were observed and photographed under 10 × 10 times microscopically .In the present study, the researchers employed the BCA method to analyse the gut and serum samples .The enzyme activities of lysozyme (LYS) in serum and total superoxide dismutase (T-SOD) in hindgut were measured by fish ELISA kits, which were then employed to determine immunoglobulin M (IgM) content, complement 3 (C3) content, and complement 4 (C4) content in hindgut tissues. The testing kits were products of Shanghai Jianglai Biotechnology Co., Ltd. . The methods were performed according to the operating instructions in detail.PRJNA664623 and PRJNA66441. Primers carried out by Primer Premier 5.0 software for pro-inflammatory genes were interleukin (IL)1β, IL12, IL17, IL32, and tumour necrosis factor (TNF)α, and anti-inflammatory genes were IL4, IL5, IL10, and transforming growth factor (TGF)β1  were applied to test the total RNA of the hindgut tissues. An Evo M-MLV reverse transcription kit  was employed to prepare qualified RNA samples to cDNA. They were then stored at −20°C for use. The primers were designed by Shenggong Bioengineering Co., Ltd. . The researchers obtained the template sequences of these primers from the sequencing of the “3 + 2” full-length transcriptome of the hindgut tissues of the studied groupers within the lab. The original data were recorded in the NCBI Sequential Read Archive (SRA) with accession numbers  (TGF)β1 . The β-aPRJNA666309. The According to the operation instructions, E.Z.N.A.™ Kit  was applied to extract the total RNA from the DI flora. Gene Denovo Co., Ltd.  assisted in sequence detection and data analysis. The original data were recorded in the NCBI SRA, with the accession number P < 0.05 were classified as differential genes (DEGs). The researchers put the DEGs gene into the KEGG database and carried out an in-depth analysis of the significantly activated signal pathways, which were relevant to signal transduction, infectious diseases and immune diseases/systems (P < 0.05).Through the PacBio Sequel and Illumina HiSeq™ 4000 platforms, the “3 + 2” full-length transcriptome sequencing was carried out. Gene Denovo Co., Ltd.  completed the sequencing process. The original data of the sequencing are stored as mentioned above. The gene expressions of | log2FC| > 1 and AO, DDC, IDO2, AOX1, ALDH, DAO1, KYN, KAT1, CYP1A1, and LAAO. The primer templates were the same as those mentioned above, and the primers are presented in To validate the accuracy of the “3 + 2” full-length transcriptome data, 10 DEGs from the tryptophan metabolism pathway were chosen for RT-qPCR: The β-actin is presented to be the internal control gene. QRT-PCR  helped in detecting gene expression. The reaction environment of PCR and analysing approaches were identical, as mentioned above.AO, DDC, IDO2, AOX1, ALDH, DAO1, KYN, KAT1, CYP1A1, and LAAO; the inflammatory genes include pro-inflammatory genes  and anti-inflammatory- related genes (IL10 and TGFβ1) in the hindgut tissues.To explore the correlation between downstream inflammatory genes and tryptophan metabolism, the crucial DEGs in the signal pathway of tryptophan metabolism were studied using canonical correlation analysis (CCA) with the inflammatory genes in internal tissue in the SBM40, SPC40, and FSBM40 groups, respectively. The key DEGs selected in the tryptophan metabolism signalling pathway include 1. After homogeneity variance tests by SPSS 22.0 , the results were studied with one-way ANOVA and are shown as the mean ± SD, with P < 0.05 indicating a statistically significant difference.With the help of free online omicshare tools, the CCA method was carried out to process the original dataP < 0.05). Among the experimental groups, there was little difference in WGR and SGR (P > 0.05). On the contrary, FCR increased significantly in each experimental group (P < 0.05). Among the experimental groups, there was little difference in FCR (P > 0.05). In addition, there was little difference in HSI and SR among the groups (P > 0.05).As indicated in P < 0.05), among which the SBM40 and FSBM40 groups were more serious, and enteritis induced by SPC40 was significantly lighter than the SBM40 and FSBM40 groups (P < 0.05)  .P < 0.05), while the T-SOD and LYS enzyme activities increased substantially in all soy meal substitution groups (P < 0.05).As exhibited in P < 0.05) and there was a little difference in IκBα (P > 0.05) in the SPC40 group, the pro-inflammatory gene expressions of IL1β, IL8, TNFα, p65, and IκBα significantly increased in all soy meal substitution groups (P < 0.05). The anti-inflammatory-related gene expressions of IL5, IL10, and TGFβ1 were reduced considerably in all substitution groups (P < 0.05).As presented in P > 0.05), there were significant differences among the FM and SPC40 groups (P < 0.05). A Venn plot showed that 133 OTUs were among the groups  by sequences with similarity ≥97%, the researchers gained 899 OTUs. The average number of OTUs in the FM, SBM40, SPC40, and FSBM40 groups were 175.00 ± 32.79, 608.67 ± 56.84, 368.00 ± 42.58, and 717.33 ± 156.62, respectively. Except for a small significant difference between SBM40 and FSBM40 (e groups . The PCoe groups .P < 0.05), while the abundances of Firmicutes, Bacteroidetes, and Actinobacteria had a significant increase in all soy meal substitution groups (P < 0.05). There was little difference in acidobacteria abundance between the SBM40 group and the SPC40 groups, but it significantly increased in the FSBM40 group (The relatively abundant top six species at the phylum level are ranked as follows: (1) Proteobacteria, (2) Firmicutes, (3) Bacteroidetes, (4) Acidobacteria, (5) Actinobacteria, and (6) Cyanobacteria. The abundance of Proteobacteria significantly decreased in all soy meal substitution groups  Stenotrophomonas, (3) Vibrio, (4) Phyllobacterium, (5) Neisseria, and (6) Bacteroides. The abundance of Photobacterium experienced a significant decrease in all soy meal substitution groups (P < 0.05). The abundances of Phyllobacterium and Neisseria had little difference in the SPC40 group (P > 0.05), and the abundance of unidentified_Rikenellaceae had little difference between the SPC40 and FSBM40 groups (P > 0.05). The abundances of the left species significantly increased in all soy meal substitution groups  Compared to the FM group, there were 554 co-contained DEGs in the SBM40, SPC40, and FSBM40 groups, named profile A. The numbers of unique DEGs were 1,003, 2,254, and 1,656, named profile B, profile C, and profile D, respectively .P < 0.05). Among these pathways, there were 60 pathways relevant to nutrition metabolism, of which 12 pathways indicated significant enrichment (P < 0.05). Thus, within all the significant enrichment pathways, 40% (12/30) significant enrichment pathways were related to nutrition metabolism (P < 0.05). Among these pathways, 81 pathways were relevant to nutrition metabolism, of which 17 pathways indicated significant enrichment (P < 0.05). In other words, within all the significant enrichment pathways, 33.33% (17/51) pathways were related to nutrition metabolism (P < 0.05). Among them, 98 pathways were relevant to nutrition metabolism, of which 26 indicated a significant enrichment (P < 0.05). Hence, within all the significant enrichment pathways, 74.3% (26/35) significant enrichment pathways were related to nutrition metabolism (P < 0.05). Among them, 75 pathways were related to nutrition metabolism, of which six pathways indicated significant enrichment (P < 0.05). Thus, within all the significant enrichment pathways, 13.2% (5/38) were related to nutrition metabolism (P < 0.05).In profile A, the enrichment result showed that there were 238 pathways, of which 30 pathways indicated significant enrichment (tabolism . In proftabolism . In proftabolism . In proftabolism . The tryP < 0.05). However, there were some differences in the types of key genes of the tryptophan metabolism pathway affected in different treatment groups  found that 20% SBM substitution for FM  presented optimal growth performance  was 10%  substitution for FM at the experimental level obviously lowered the growth performance, gut physiology and immune state, and gut flora composition. Studies on formance . Studiesformance . SPC witformance , 29. Theformance , and FSBformance . The preformance . SBM, SPformance . PreviouThe present research also indicated that for pearl gentian groupers, the experimental level of soy proteins also had a significant impact on their gut enzyme activity. The LYS plays an important role in non-specific immunity in fish and IgM for specific immunity. Both C3 and C4 are crucial for activating their complement systems . In the Our previous analysis of the gut immune system of SBMIE pearl gentian groupers found that among the signal pathways significantly activated in the gut, 55.17% were related to intestinal immunity, contagious disease, and signal transduction; in the signal pathways greatly inhibited in the gut, nutrient metabolism-related pathways accounted for 67.44% . The res2 and ATP  can be found in the article/All experimental methods were approved by the Ethics Review Board of Guangdong Ocean University. All of the procedures were performed in accordance with the relevant guidelines and regulations.WZ and AP were engaged in the whole experiment and formulated the draft of this manuscript. BT designed the experiment and composed and amended the draft critically. YX and YL took part in the tests. RX amended the first draft. HZ and QY completed the data analysis. JD and SC approved the final version. All authors contributed to the article and approved the submitted version.


Astragalus membranaceus  and Angelica sinensis  are common Chinese herbal medicines and have historically been used in spinal cord injury (SCI) therapies. However, the underlying molecular mechanisms of AM&AS remain little understood. The purpose of this research was to explore the bioactive components and the mechanisms of AM&AS in treating SCI according to network pharmacology and the molecular docking approach.  AM&AS active ingredients were first searched from Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Traditional Chinese Medicine Information Database (TCM-ID). Meanwhile, we collected relevant target genes of SCI through the GeneCards database, OMIM database, PharmGkb database, DurgBank database, and TDD database. By utilizing the STRING database, we constructed a network of protein-protein interactions (PPIs). In addition, we used R and STRING to perform GO and KEGG function enrichment analyses. Subsequently, AutoDock Vina was employed for a molecular docking study on the most active ingredients and most targeted molecules to validate the results of the network pharmacology analysis mentioned above.  The overall number of AM&AS active compounds identified was 22, while the number of SCI-related targets identified was 159. Then, the 4 key active ingredients were MOL000098 quercetin, MOL000422 kaempferol, MOL000354 isorhamnetin, and MOL000392 formononetin. A total of fourteen core targets were TP53, ESR1, MAPK1, MTC, HIF1A, HSP90AA1, FOS, MAPK14, STAT1, AKT1, EGFR, RELA, CCND1, and RB1. The KEGG enrichment analysis results indicated that lipid and atherosclerosis, PI3K-Akt signaling pathway, human cytomegalovirus infection, fluid shear stress, and atherosclerosis, etc., were enhanced with SCI development. Based on the analyses of docked molecules, four main active compounds had high affinity for the key targets.  Altogether, it identified the mechanisms by which AM&AS was used for SCI treatment, namely, active ingredients, targets and signaling pathways. Consequently, further research into AM&AS treating SCI can be conducted on this scientific basis. Spinal cord injury (SCI) results from damage to the central nervous system caused by various factors, and this disease is one of the major causes of irreversible nerve injury. Depending on the extent of the injury, SCI may result in motor and sensory dysfunction beneath the interface of the injury and the autonomic nerves impaired and bowel, bladder, and sexual dysfunction . Across The traditional Chinese medicine (TCM) is also playing crucial role in treating a variety of diseases as a complementary and alternative medicine . Using TPharmacology of networks is a cross-discipline based on systems biology and combines systems biology, polypharmacology, computational biology, network analysis, and other disciplines . In addihttps://tcmsp-e.com/tcmsp.php) and Traditional Chinese Medicine Information Database . TCM was screened using the oral bioavailability (OB ≥30%) and drug-likeness (DL ≥0.18). PubChem (https://pubchem.ncbi.nlm.nih.gov/) was searched for two-dimensional structures of selected compounds. Compound molecular files were downloaded from PubChem, converted into SDF format, and saved as mol2 files. The target genes were obtained.Drug main active ingredients and targets are obtained by using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , OMIM (https://www.genecards.org/), PharmGkb (https://www.pharmgkb.org/), DurgBank (https://go.drugbank.com/), and TDD (http://db.idrblab.net/ttd/) [https://www.uniprot.org/). After retaining the common targets for disease from five database and removing duplicate targets, SCI relevant targets are acquired. By utilizing R package, the AM&AS and SCI targets are drawn in a Venn diagram, and then, the core targets are derived.We inserted “Spinal cord injury” as keywords. The known therapeutic targets in SCI were also acquired from GeneCards (et/ttd/) . Targetshttps://cn.string-db.org/) [Common targets for AM&AS and SCI were imported into STRING database (db.org/) . As longGenes involved in drug-disease crossover were screened. A number of targets corresponding to AM&AS active ingredients were imported into Cytoscape, as well as predicted targets for SCI disease, and visualization of the network was achieved by creating a network diagram of drug-target-disease interactions.http://www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (https://www.genome.jp/kegg/) were conducted. Data analysis for GO terms and KEGG pathways was carried out by Cytoscape software and plotted by using the “ggplot” R package. The GO enrichment analysis considers cell components (CC), molecular functions (MF), and biological processes (BP). The results were expressed in the form of a bar graph and bubble maps. Statistical significance was expressed by a p-value of <0.05.Core targets were analyzed using GO enrichment analysis and KEGG pathway analysis and the key AM&AS pathway against SCI in order to better understand their functions. A Genome Ontology (GO) knowledgebase (https://www.rcsb.org/) provided crystal structures. After that, PyMOL (Version2.4.0) was run to eliminate small molecules of protein ligands and water molecules, and the PDB format was saved. In the AutoDock Tools, the Lamarckian genetic algorithm was implemented for molecular docking purposes. Using docking free energy as a metric, we organized the docking results. A higher affinity occurs when binding free energies are lower.By using AutoDock Tool software (version 1.5.6), the connective validity of active ingredients was verified through molecular docking. Structures of ligands used in docking were downloaded from PubChem database; then, all the structures were converted to the mol2 file format. The hydrogen atoms were added to all the ligands and receptor by using AutoDock Tools and conversion structures to PDBQT format. Protein Data Bank (RCSB PDB)  contain 158 nodes and 208 edges . In the Using Cytoscape software, network of drug ingredients, targets, and diseases was built; the results are displayed in p < 0.05, were obtained. For each of the top 10 GO items, a bubble chart and a histogram were drawn. There were GO terms associated with response to molecule of bacterial origin and response to metal ion, response to xenobiotic stimulus, response to lipopolysaccharide, etc., in the biological process. Cellular component revealed that coaction targets were mainly enriched in membrane raft, membrane microdomain, postsynaptic membrane, etc. As for molecular functions, targets were mainly related to DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, etc.  of the 14 core active components is selected from the A&S compound-SCI-target interaction network based on their degree over 10. As part of AutoDock's docking process, the active components  are docked molecularly. According to Recent studies have shown that a global increase in socioeconomic and medical costs has been reported for SCI; it is estimated that there are 10.4–83 million cases of SCI annually, and the disease is burdensome to patients and health management organizations . PathogeOur study used network pharmacologic analysis method to screen the active components of AM&AS. Genes and mechanisms linked to potential pharmacological effects were predicted and elucidated. This resulted in 132 pharmacological targets of AM&AS for SCI treatment, and a total of ten key genes were identified and included TP53, RELA, CTNNB1, MAPK1, and AKT1. Further, our GO and KEGG enrichment analysis demonstrated that AM&AS is effective at treating SCI by regulating inflammation, apoptosis, etc.β, IL-17, and TNF-α, are integral to the inflammatory response to SCI [A gene functional classification system based on GO annotations is an international standard for gene classification . As a ree to SCI . Target e to SCI . Therefo−1); additionally, kaempferol and ESR1 showed good binding energy (affinity = −7.3 kJ·mol−1). According to these findings, although TP531 and ESR1 are likely to be the primary targets in AM&AS for treating SCI, quercetin and kaempferol could also have potential for treating SCI.In molecular docking, docking scores are calculated as a result of calculating binding affinity correlations between four main active ingredients and five key targets. Quercetin and TP531 showed the greatest affinity, according to our results (affinity = −7.4 kJ·molIn summary, from an active ingredient and network pharmacology perspective, this article mainly indicates potential active components and mechanism of AM&AS on SCI treatment. Their possible target genes and related signaling pathways were also preliminarily discussed. However, a simple component-target-pathway analysis may not fully reflect the role of TCM synergistic regulation. Moreover, the research has not yet carried out experimental verification on the key targets and pathways screened by network pharmacology, which is also the limitation of the study. In future work, based on the information provided by network pharmacology, we also plan to explore the key targets and pathways will be further verified at the cellular and animal levels, so as to provide a more sufficient basis for the treatment of SCI with AM&AS.


UHPLC-Q-Orbitrap HRMS analysis and in vivo experiments further validated the results of data mining, network pharmacology, and molecular docking. Our study could uncover the components, mechanisms, and functions of TCM prescriptions against MI and develop a new integrative approach to demonstrate TCM’s multi-component, multi-target, and multi-pathway approach to disease treatment.Male infertility is a major and growing health problem with an estimated global prevalence of 4.2%. The current therapy is limited by the unknown etiology of MI, emphasizing the critical requirement forward to a more efficient method or medication. Through thousands of years, Traditional Chinese Medicine (TCM) has been shown to be effective in treating MI effectively. However, the components, mechanisms and functions of TCM prescriptions on MI are still obscure, severely limiting its clinical application. In order to discover the molecular mechanism of TCM against MI, our study presents a comprehensive approach integrated data mining, network pharmacology, molecular docking, UHPLC-Q-Orbitrap HRMS, and experimental validation. Here, we begin to acquire 289 clinical TCM prescriptions for MI from a TCM hospital’s outpatient department. Then, Core Chinese Materia Medica (CCMM) was then retrieved from the TCM Inheritance Support System (TCMISS), which was utilized to discover the underlying rules and connections in clinical prescriptions. After that, 98 CCMM components and 816 MI targets were obtained from ten distinct databases. Additionally, the network pharmacology methods, including network construction, GO and KEGG pathway enrichment, PPI analysis, were utilized to reveal that kaempferol, quercetin, isorhamnetin, and beta-sitosterol are the core components of CCMM in treating MI. The mechanisms and functions of CCMM against MI are hormone regulation, anti-apoptosis, anti-oxidant stress, and anti-inflammatory. Furthermore, the strong connections between four core components and six key targets were verified using a molecular docking method. Following that, the core components of the CCMM extract were identified using UHPLC-Q-Orbitrap HRMS analysis. Finally,  Male infertility (MI), characterized by impaired sperm function , has becTCM has been shown to cure various diseases, such as MI, for thousands of years . AccordiData mining is a computational method that automates the extraction of information from large amounts of data to discover innovative insights . Recent in vivo experimental validation. Firstly, the clinical TCM prescriptions in treating MI came from the outpatient department of a TCM hospital. Secondly, TCM Inheritance Support System (TCMISS) was used for uncovering the underlying rules and connections in clinical TCM prescription and retrieving Core Chinese Materia Medica (CCMM). Thirdly, various databases were conducted to collect the components and targets of CCMM and MI. Then, we performed the network pharmacology methods, such as network construction, GO and KEGG pathway enrichment, PPI analysis, to explore the core components, key targets, and molecular mechanisms of CCMM in TCM prescriptions on MI. After that, the interactions between the core components and key targets were explored utilizing molecular docking. Next, the core components in CCMM of TCM prescriptions against MI were identified by UHPLC-Q-Orbitrap HRMS. Finally, a mouse model of MI induced by CP was established to research the components, mechanisms, and functions of CCMM in TCM prescriptions in treating MI, and further estimate the results of data mining, network pharmacology, and molecular docking.Therefore, in this study, we adopted a comprehensive approach integrated data mining, network pharmacology, molecular docking, UHPLC-Q-Orbitrap HRMS, and The outpatient department of a TCM hospital affiliated to Beijing University of Chinese Medicine provided the TCM prescriptions used to treat MI. The following inclusion criteria apply to TCM prescriptions: (1) the patient was initially diagnosed with MI, which includes azoospermia, oligozoospermia, asthenospermia, and teratospermia; (2) the patient was over the age of 23; (3) the patient has been married for more than one year and had normal sex without contraception for 12 months, but the woman was unable to conceive due to male factors; (4) There is no family history of MI in the patient’s family. The exclusive criterion is that the patient’s wife suffers from an illness that makes conception difficult.TCMISS software (V2.5) comprises six functional modules: clinical collection, platform management, data management, knowledge retrieval, statistical report generation, and data analysis. Three graduate students were in charge of the clinical TCM prescriptions gathering process. One of them utilized the “clinical collection” feature to gather prescriptions, while the others checked the data using the “platform management” function. After analyzing the frequency of Chinese materia medica (CMM), the combinations of CCMM were obtained using the “data analysis” function. The principle of screening CCMM by using TCMISS software is association rules analysis and complex system entropy methods. The degree of support was 140, and the level of confidence was more than or equal to 0.95.http://119.3.41.228:8000/tcmid/) (http://tcmspw.com/tcmsp.php) (https://pubchem.ncbi.nlm.nih.gov/)    and TCMSmsp.php)  (Supplemmsp.php)  and two msp.php) , drug-limsp.php) , were usih.gov/)  and ALOGalogps/)  to obtaihttp://www.swisstargetprediction.ch/)   (Swiss Target Prediction (ion.ch/)  was usedot.org/)  standardn 3.8.0) . The degn 3.8.0) , a Cytoshttp://omim.org/) (http://ctdbase.org/) (https://www.disgenet.org/) (https://www.genecards.org/)  , the Comse.org/) , the Diset.org/) , and Gends.org/) . The tarThe Venn diagram was used to determine the common targets of CCMM and MI, and then a CCMM-MI common-target network was constructed using the Cytoscape program. Additionally, Network Analyzer was used to determine the network’s topological properties (Supplement 8).https://david.nicifcrf.gov/, version 6.8)  enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on the CCMM targets, MI-related targets, and CCMM-MI common targets, respectively. The database is the Database for Annotation Visualization and Integrated Discovery  , and theion 6.8)  (Supplemhttp://string-db.org) (http://apps.cytoscape.org/apps/bingo) (http://apps.cytoscape.org/apps/cluego) (The PPI parameters for the common targets between CCMM and MI were acquired from the STRING database v11.0 (-db.org) , with a s/bingo)  and Clue/cluego)  plugins https://www.rcsb.org/) (https://pymol.org/2/) (The Protein Data Bank (PDB) (sb.org/)  was util.org/2/) . AutoDoc.org/2/)  were conCCMM was supplied by Tongrentang Pharmacy in Beijing . The following steps were taken to prepare the CCMM extract : (1) Aftm/z 100–1,500 range for the MS study. Other operating MS characteristics were a sheath gas flow rate of 40 arb, an auxiliary gas flow rate of 15 arb, a capillary temperature of 320°C, an aux gas heater temperature of 350 °C, a positive spray voltage of 3.2 kV, and a negative spray voltage of 3.0 kV. The resolution of the MS is 70,000, while the resolution of the MS/MS is 17,500. Compound Discover and Xcalibur software were used to gather and analyze data. Kaempferol, quercetin, isorhamnetin, and beta-sitosterol standards were purchased from Shanghai yuanye Bio-Technology Co., Ltd.The extracts were analyzed using different methods. The instrumentation consisted of an ultimate 3,000 liquid chromatography system coupled to a Q-Orbitrap mass spectrometer equipped with dual ESI/APCI interfaces. An autosampler, a diode array detector, a column compartment, and two pumps comprised the chromatography system. Waters ACQUITY UPLC HSS T3 column  was used for the chromatographic separation. UV detection of UHPLC fractions was performed using a DAD detector with wavelengths ranging from 200 to 400 nm. At a temperature of 30 °C, the analytical column was injected with a volume of 5 L. Gradient elution was performed using 0.1% (v/v) formic acid in water (solvent B) and acetonitrile (solvent A). Kaempferol, quercetin, isorhamnetin, and CCMM extract were analysed in negative mode using an ESI probe. The flow rate was set to 0.3 ml/min, and the gradient elution procedure followed the following: 0–1 min, 95%B; 1–3 min, 95%–85% B; 3–10 min, 85%-75%B; 10–20 min, 75%–68% B, 20–30 min, 68%–50% B; 30–32 min, 50%–0% B; 32–35 min, 0% B; 35–35.1 min, 0%–95% B; 35.1–38 min, 95%B. Beta-sitosterol was analyzed in positive mode using an APCI probe. The flow rate was set to 0.4 ml/min, and the gradient elution procedure followed the following: 0–5 min, 20%–0% B; 5–10 min, 0% B; 10–12 min, 0%–20% B; 12–18 min, 20% B. Positive and negative ion modes were placed in the The Vital River Laboratory Animal Technology Co., Ltd.  provided 64 Kunming male mice weighing 22–25 g. Mice were acclimated to conventional housing circumstances, which included an ambient temperature of 23 ± 2°C, a relative humidity of 23 ± 2°C, and a 12-h light-dark cycle. The Beijing University of Chinese Medicine’s Institutional Animal Care and Use Committee approved the experimental methods and ethics.The most common causes of MI are spermatogenesis dysfunction and a defect in the testicular . Accordi2 anesthetized following their last treatment, then their left testes and epididymides were taken and weighed using laparotomy.The mice were split into eight groups, each consisting of eight animals. In the normal control (NC) group, physiological saline was consistently given. The remaining seven groups were model control (MC), kaempferol (50 mg/kg), quercetin (50 mg/kg), isorhamnetin (50 mg/kg), beta-sitosterol (50 mg/kg), low-dose, and high-dose CCMM. The present study calculated the low dosage for mice by converting the dose to a human equivalent dose (HED) based on the body surface area of the mice. The mice were given CP intraperitoneally to induce MI. The model control group received physiological saline, the low-dose CCMM group received 10 g/kg CCMM, and the high-dose CCMM group received 20 g/kg CCMM once a day for three weeks. Each group received a single dosage of 20 ml/kg through oral gavage. The mice were weighed and CO6/ml), sperm motility (a + b %), sperm viability (%), sperm malformation rate (%), and Lecithin corpuscle density were used as main indicators  was added to the tube, the epididymal tissue was chopped into tiny pieces and placed in a warm water bath set to 37°C for 30 min. Following sperm extraction from the epididymis, the sperms’ quality was assessed using semen analysis equipment and a BK-FL fluorescence microscope . Sperm density  after being fixed in 4% paraformaldehyde for 24 h. Cell morphology was observed using a microscope .Testis tissue were lysed in lysis buffer and sonicated. The protein concentration was determined using a BCA protein assay kit . Approximately 30 μg of protein from each sample was separated by 10% sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a polyvinylidene fluoride (PVDF) membrane. Membranes were blocked with 5% skimmed milk in TBST and incubated with primary antibodies overnight at 4°C. Antibodies obtained from Abcam  were as follows: MAPK3/1 (ERK1/2) (ab184699), TNF-α (ab205587). Antibodies purchased from Cell Signaling Technology  included those against Akt (2938S) and EGFR (2646S). GAPDH  was regarded as the internal reference. Membranes were incubated with the corresponding secondary antibody  for 1 h at room temperature and washed in TBST. Protein signals were detected using ECL Detection Reagent kit . Images were captured by ChemiDoc MP Imaging System .p < 0.05.All of the data is presented as mean ± SD. One-way analysis of variance (ANOVA) with GraphPad software (version 8.0) was used to perform comparisons of multiple groups and pairwise comparisons. Statistical significance was defined as Terminalia chebula Retz., Cyperus rotundus L., Grona styracifolia (Osbeck) H. Ohashi and K. Ohashi, Hordeum vulgare L., Endothelium corneum, Crataegus monogyna Jacq., Glycine max (L.) Merr., Hippocampus japonicus, Fraxinus excelsior L., Tragopogon porrifolius L., Placenta Hominis, and Panax ginseng C.A.Mey. The frequency of the top six CMM is greater than 170, with a percentage greater than 60%, indicating that they are CCMM in the prescriptions on MI. The combinations of CCMM are shown in We collected 289 TCM prescriptions and 149 CMM used to treat MI. The top 20 high frequent CMM of the prescriptions are shown in TCMSP, TCMID, and Swiss Target Prediction databases were used to gather information on the components and targets of CCMM in the treatment of MI. A total of 20 components and 359 targets form Gynochthodes officinalis (F.C.How) Razafim. and B. Bremer, 13 components and 302 targets from Cuscuta chinensis Lam., 47 components and 446 targets from Lycium barbarum L., seven components and 157 targets from Morus alba L., six components and 296 targets from Angelica sinensis (Oliv.) Diels, 22 components and 436 targets from Hedysarum Multijugum Maxim, were obtained. Then, we constructed a CCMM component-target network using 98 components and 816 targets . We founThe investigation of GO enrichment is divided into three parts: biological process, cellular component, and molecular function. We found that CCMM could inhibit apoptosis, promote cell proliferation, and regulate the cytosolic calcium ion concentration through negative regulation of apoptotic process (GO:0043066), positive regulation of cytosolic calcium ion concentration (GO:0007204), positive regulation of cell proliferation (GO:0008284). Additionally, Prostate cancer (hsa05215), HIF-1 signaling pathway (hsa04066), Progesterone-mediated oocyte maturation (hsa04914), and Acute myeloid leukemia (hsa05221) are related to male reproductive function, which was shown in A total of 671 targets of MI were collected from four different databases. Among these, 225 targets were from CTD, 210 targets were from DisGeNET, 197 targets were from GeneCards, 181 targets were from OMIM . FollowiBased on the results of the Venn diagram, we obtained 127 common targets of CCMM and MI . Then, wvia steroid hormone receptor activity (GO:0003707), and thyroid hormone receptor activity (GO:0004887). The main KEGG signaling pathways were PI3K-Akt signaling pathway, Estrogen signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway, demonstrating that CCMM could treat MI via regulating hormones, reducing apoptosis, oxidant stress, and inflammatory . In addition, CCMM can reduce oxidant stress through oxidation-reduction process (GO:0055114), and positive regulation of nitric oxide biosynthetic process (GO:0045429). CCMM can also promote cell proliferation for treating MI, through positive regulation of cell proliferation (GO:0008284). Besides, CCMM can regulate male reproductive function on MI, through positive regulation of ERK1 and ERK2 cascade (GO:0070374). As shown in ammatory .A PPI network was created to evaluate the CCMM-MI common targets’ core proteins . Under t−10) and the largest number of associated core proteins . Therefore, Estrogen signaling pathway is the most significant KEGG signaling pathway , kaempferolO5: Leu195N (2.8 Å), kaempferolO4: Phe161N (3.1 Å), kaempferolO4: Thr160N (2.8 Å)] (O5 and Met793N (3.0 Å), kaempferolO6 and Asp855OD2 (2.9 Å), kaempferolO6 and Asn842OD1 (2.8 Å) formed the hydrogen bonds, so the interaction between the ligand and the EGFR protein was strengthened. , AKT1, MAPK3, MAPK1, EGFR, GAPDH, and TNF as proteins to perform the molecular docking . In AKT1(2.8 Å)] ,9A. Addi(2.8 Å)] ,9B. Kaem(2.8 Å)] ,9C. Addigthened. ,9D. Moregthened. ,9E. Kaemgthened. ,9F.O4: Ser263OG (2.7 Å)). O5: Asp800OD2 (2.9 Å), quercetinO7: Met793N (3.1 Å) and Gln791O (2.7 Å). Therefore, the interactions between the ligand and the EGFR protein are enhanced. As shown in O4: Glu234OE2 (2.8 Å), isorhamnetinO7: Gly162N (3.0 Å) and Phe161N (3.2 Å)]. As shown in OD2 (2.8 Å), Asp104O (2.6 Å)]. Moreover, isorhamnetin could interact with EGFR via Leu792, Gly796, Leu844, Cys797, Arg841, Ala743, Met790, and Val726 and formed three hydrogen bonds with the residues Met793N (3.1 Å), Asn842OD1 (2.9 Å), Asp855OD2 (2.8 Å) (According to  (2.8 Å) ,9P. Besi (2.8 Å) ,9Q. Isor (2.8 Å) ,9R.via Asn65, Ala62, Phe60, Asp93, Ser72, Leu71, Leu67, Lys32 and a hydrogen bond with His66 (3.1 Å) (The action modes of beta-sitosterol and AKT1 are shown in  (3.1 Å) ,9X.2 fragment ions using their precise mass measurements. 2 fragment ions in negative and positive modes. As illustrated in 2 spectrum of kaempferol, quercetin, isorhamnetin, beta-sitosterol standards are shown in 2 spectrum are shown in The active components of CCMM were analyzed using a Q-Exactive orbitrap mass spectrometer. We determined the element compositions of kaempferol, quercetin, isorhamnetin, beta-sitosterol, and MSp < 0.01). This demonstrated that the OA mouse model had been constructed efficiently . Compared to the MC group, the kaempferol, quercetin, isorhamnetin, beta-sitosterol, and low- and high-dose CCMM groups enhanced the density, motility, viability of sperm, and lecithin corpuscle density, while reducing the rate of sperm malformation . In contrast to the NC group, CP dramatically lowered the density, motility, and viability of sperm, and Lecithin corpuscle density in the MC group, while increasing the sperm malformation rate . In comparison to the MC group, the kaempferol, quercetin, isorhamnetin, beta-sitosterol, and CCMM enhanced the protein expressions of AKT1 (p < 0.01) and EGFR (p < 0.05), while reducing the protein expressions of MAPK3/1 (ERK1/2) (p < 0.01) and TNF-α (p < 0.01)  and TNF-α, compared to the NC group  .Infertility affects approximately 15% of reproductive-aged couples globally, of which MI accounts for approximately 50% of instances . Howeverin vivo experiments  Razafim. and B. Bremer, Cuscuta chinensis Lam., Lycium barbarum L., Morus alba L., Angelica sinensis (Oliv.) Diels, Corethrodendron multijugum (Maxim.) B.H.Choi and H. Ohashi By using the network pharmacology methods, 98 components and 816 targets of CCMM, and 671 MI-related targets were obtained from 10 databases. 127 common targets between CCMM and MI were obtained by the Venn diagram. The results of network pharmacology showed that kaempferol, quercetin, isorhamnetin, and beta-sitosterol are four core components. AKT1, MAPK3, MAPK1, EGFR, GAPDH, and TNF are six key targets. Estrogen signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway are four vital signaling pathways of CCMM of clinical TCM prescriptions on MI. The key targets and vital signaling pathways of CCMM for treating MI are mainly related to hormone regulation, anti-apoptosis, anti-oxidant stress, and anti-inflammatory. Based on the molecular docking strategy, we verified the strong interactions between four core components and six key targets. By UHPLC-Q-Orbitrap HRMS analysis, four core components of CCMM were successfully identified. In a mouse model of MI, we found that CCMM and four core components could improve the density, motility, viability of sperm, lecithin corpuscle density, decrease the rate of sperm malformation and testis tissue damage, and regulate the proein expressions of AKT1, MAPK3/1, EGFR, and TNF-α. It showed that the results of data mining, network pharmacology, and molecular docking were further validated by UHPLC-Q-Orbitrap HRMS analysis and eriments .in vitro research  Razafim. and B. Bremer, Cuscuta chinensis Lam., Lycium barbarum L., Morus alba L., Angelica sinensis (Oliv.) Diels, Corethrodendron multijugum (Maxim.) B.H.Choi and H. Ohashi are CCMM of clinical TCM prescriptions for treating MI. The core components are kaempferol, quercetin, isorhamnetin, and beta-sitosterol. The mechanism and functions of CCMM for treating MI are hormone regulation, anti-apoptosis, anti-oxidant stress, and anti-inflammatory. Molecular docking was used to confirm that four core components and six key targets had strong interactions. UHPLC-Q-Orbitrap HRMS analysis was used to recognize the core components in CCMM extract. Finally,


The study aimed to explore the active ingredients, targets, and mechanism of action of Yiqi Jianpi recipe (YQJPR) in the treatment of COPD based on the network pharmacology and COPD rat models.  The active ingredients and targets of YQJPR were collected by TCMSP. Disease-related protein targets were obtained from GeneCards. The Venn diagram was used to show the key therapeutic targets of COPD in YQJPR. The PPI network was established by STRING, and cytoHubba plug-in was used to screen the core targets within the network. GO functional enrichment and KEGG pathway enrichment analysis were performed to describe the functions and pathways of the core targets. Cytoscape software was used to construct the ingredient-target network and the core target-enrichment pathway network. The chemical constituents of YQJPR were analyzed by HPLC-MS/MS. p < 0.05). HE staining showed that, compared with the model group, YQJPR significantly improved lung tissue morphology and reduced lung inflammation in rats.  The network pharmacology showed 152 active ingredients and 225 targets in YQJPR for the treatment of COPD. The key active ingredients were quercetin, luteolin, kaempferol, tanshinone IIA, and baicalein. The contents of quercetin and luteolin in YQJPR were quantitatively measured by HPLC-MS/MS. 22 core genes were screened, including AKT1, IL-6, JUN, VEGFA, and CASP3, which were mainly involved in BPs such as cell proliferation and differentiation, oxidative/chemical stress, and regulation of DNA-binding transcription factor activity and regulated viral infection, tumor, HIF-1, MAPK, TNF, and IL-17 pathways. Animal experiments showed that YQJPR could significantly reduce the expression of p-ERK1/2, p-Akt, c-Myc, cleaved caspase-3, and p-Stat3 in lung tissue ( The effects of YQJPR on COPD may involve multiple components, pathways, and targets. This study provides new ideas for further and more comprehensive exploration of the therapeutic effect of YQJPR on COPD in the future. Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death in the world after cardiovascular disease and stroke , which bAstragalus), Dang Shen (Codonopsis), Bai Zhu , Fu Lin (Poria cocos), Zhu Li (Bamboo Juice), Ban Xia (Pinelliae Rhizoma), Chen Pi (Pericarpium Citri Reticulatae), Qian Hu (Peucedani Radix), Dan Pi (Cortex Moutan), and Gan Cao (Glycyrrhizae Radix). Although YQJPR has a curative effect on COPD, its composition is complex and its pharmacodynamic mechanism is not yet clear. Therefore, there is great interest in studying active compounds and molecular targets for the treatment of COPD in YQJPR.Yiqi Jianpi recipe (YQJPR) is an empirical prescription for the clinical treatment of COPD by Professor Wang, a famous Chinese medicine doctor from the Zhejiang Provincial Hospital of Traditional Chinese Medicine. It is mainly composed of Huang Qi  by combining systematic network analysis and pharmacology to elucidate the interactions between compounds, genes, proteins and diseases . Therefohttps://old.tcmsp-e.com/tcmsp.php). This database provides 12 pharmacokinetic properties , and secondary antibodies .n = 20) used cigarette smoke exposure (CSE) combined with intratracheal instillation of LPS (Sigma) to prepare a COPD model for 12 weeks. During this period, 20 rats passively inhaled smoke  in a self-made smoking box , except for the intratracheal instillation of 0.2 ml (200 ug) LPS (no smoke exposure) on days 1 and 14. From Week 9 onward, the model establishment group was randomly divided into the model group (n = 10) and the YQJPR group (n = 10). The model group continued to passively smoke until the end of 12 weeks, and the YQJPR group started TCM intervention and continued to passively smoke 1 hour after gavage. The YQJPR group continued the TCM intervention after the 12th week of modelling, and the whole TCM intervention lasted for 12 weeks. The dose of YQJPR to rats was calculated from the dose conversion method between humans and rats [After three days of adaptive feeding, ten of the 30 SD rats were randomly selected as the control group, and the rest were selected as the model establishment group . Referriand rats . The adug, 10 min, 4°C) to collect total protein. After denaturation with SDS-PAGE Sample Loading Buffer , proteins were separated by 10% or 12% SDS-PAGE and transferred to PVDF membranes . The membranes were blocked with 5% nonfat dry milk  at room temperature. After 1.5 h, they were rinsed with TBST and incubated overnight at 4°C with primary antibodies against p-ERK1/2 , p-Akt , c-Myc , cleaved caspase-3 , p-Stat3 , and GAPDH . Then, the blots were washed with TBST (10 min∗3). The blots were subsequently incubated with a secondary antibody  at room temperature for 1.5 h. Finally, the blots were washed with TBST (10 min∗3) and visualized with enhanced chemiluminescence reagent  and analyzed with Bio-Rad Machine (USA).The lung tissue was homogenized with RIPA lysis buffer  and centrifuged  analyzed the gray value of the blots and took the ratio with the gray value of GAPDH as the relative expression level, and then, the ratio was normalized.p < 0.05 indicates a statistical difference.GraphPad Prism 9 was used for statistics. All experimental data were obtained from at least three repeated independent trials and expressed as mean ± SD. Statistical differences between the groups were identified by one-way ANOVA. Astragalus, 21 in Codonopsis, 7 in Atractylodes macrocephala Koidz, 15 in Poria cocos, 13 in Pinelliae Rhizoma, 5 in Pericarpium Citri Reticulatae, 24 in Peucedani Radix, 11 in Cortex Moutan, and 92 in Glycyrrhizae Radix. Bamboo juice was filtered out during this screening process  and inhibit inflammatory factors  to exert anti-inflammatory effects [κB pathway activation and inhibit apoptosis and inflammation in vitro and in vivo by downregulating miR-132 [α pathway, effectively alleviating the progression of COPD [pneumoniae infection by reducing the permeability of the alveolar capillary membrane, inhibiting pulmonary inflammatory response and apoptosis [In this study, 152 ingredients and 225 targets of YQJPR related to COPD were identified, indicating that YQJPR is a treatment of COPD through combined regulation of multiple ingredients and multiple targets. Among them, quercetin, luteolin, kaempferol, tanshinone IIA, and baicalein regulated at least 30 targets and were considered as key active ingredients. And it was verified that quercetin and luteolin did exist in YQJPR by HPLC-MS/MS. The above five compounds are all flavonoids with various pharmacological effects, which include antibacterial, anti-inflammatory, antioxidant, and anticancer. Studies have shown that a diet rich in quercetin can reduce the risk of asthma, bronchial hyperresponsiveness, and COPD , 18. In exposure , 20. Lut effects . Luteoli miR-132 . Tanshin of COPD , 25. Baipoptosis . Baicalepoptosis .Through constructing PPI network of intersection targets between YQJPR and COPD, 22 core genes, including AKT1, IL-6, JUN, VEGFA, CASP3, IL-1B, PTGS2, MYC, MAPK3, and STAT3, were identified. IL-1 and IL-6 are both secreted proteins associated with inflammation and immune responses, with IL-1 mainly being produced by pathogens that stimulate cells of the innate immune system, such as monocytes and macrophages. IL-1 is highly expressed in small airway epithelial cells, which inhibits CSE-induced cell proliferation and promotes apoptosis, but can also promote inflammation and aggravate cell damage , 29. IL-α, the most important cytokine in the TNF pathway, is elevated in COPD and can induce the release of other pro-inflammatory factors [α expression. The HIF-1 pathway is a major regulator of angiogenesis, an adaptive response to tissue hypoxia [α accumulation directly upregulates VEGF levels, which contribute to airway and vascular remodeling, to improve the oxygen supply to the pulmonary circulation [To understand the molecular mechanism of YQJPR in the treatment of COPD, GO analysis and KEGG enrichment analysis of 22 core genes were performed. GO analysis revealed that core target genes were strongly associated with the following BPs: cell proliferation and differentiation, oxidative/chemical stress, glandular development, regulation of DNA-binding transcription factor activity, and response to metal ions. The analysis of the KEGG pathway was primarily related to TNF, IL-17, HIF-1, MAPK, AGE-RAGE, virus infection, and cancer signaling pathways. MAPK is mainly involved in cell proliferation, differentiation, and immune regulation. It has been shown that the p38 MAPK pathway can be activated by CSE to stimulate pro-inflammatory cytokine expression . Studies factors . IL-17A  factors . IL-17,  factors . The pul factors , leading hypoxia , 45. HIFculation . The resWestern blotting was used to verify five key targets  that were predicted in the PPI network. According to the results of the BPs and the KEGG analysis, these five genes were mainly enriched in the virus infection, cancer, MAPK, HIF-1, TNF, and IL-17 signaling pathways. It is closely related to biological processes such as cell proliferation and differentiation, oxidative/chemical stress, response to metal ion, and regulation of DNA-binding transcription factor activity. The results showed that compared with the model group, YQJPR significantly downregulated the expression of p-ERK1/2, p-Akt, c-Myc, cleaved caspase-3, and p-Stat3 in lung tissue. Therefore, the molecular mechanism of YQJPR treatment of COPD has been more clearly verified.In this study, the key active ingredients, key targets, and pathways of YQJPR in the treatment of COPD were selected by network pharmacology. The results show that YQJPR can interfere with the development of COPD through multiple ingredients, targets, and pathways. In conclusion, YQJPR effectively improved lung inflammation in COPD rats. Especially, quercetin, luteolin, kaempferol, tanshinone IIA, and baicalein were the main effectors in YQJPR, which primarily downregulated the expressions of five key targets , and subsequently inhibited the activation of virus infection, cancer, MAPK, HIF-1, TNF, and IL-17 pathways, thereby alleviating CS/LPS-induced lung inflammation.The disadvantage of this study is that only one detection method was used in the COPD rat model, and the pathways identified above were not verified, so further in-depth and comprehensive studies are needed. This study provides ideas and a basis for exploring the therapeutic effect of YQJPR on COPD in the future.


This study sought to clarify the mechanism of action of Miao medicine Tongfengting decoction in the treatment of gout through network pharmacology and molecular docking by searching for its key targets and related pathways.The active ingredients of Miao medicine Tongfengting Decoction were obtained from the TCMSP data platform, searched the relevant databases for gout-related targets,using String and Cytoscape 3.9 to build a “compound-cross-target-disease” network diagram,performed gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis in the DAVID database, and performed the docking analysis using PyMoL 2.3.0 and AutoDock.After screening, 298 main targets of the Miao medicine Tongfengting decoction for gout were identified. The target network is established, and the topology of protein-protein interaction (PPI) network is analyzed. The enrichment analysis of KEGG pathway showed that these targets were related to Pathways in cancer, PI3K Akt signaling pathway, MAPK signaling pathway and other pathways. Molecular docking showed that the target protein had good binding power with the main active components of the compound of Miao medicine Tongfengting Decoction.Miao medicine Tongfengting decoction probably regulates immune mechanism using a multi-component, multi-target, and multi-pathway strategy to reduce inflammatory response and exert its therapeutic effect on gout. It typically presents with local redness, swelling, heat, and pain in the joints and usually occurs in the first metatarsophalangeal joint. Arthrocentesis targeted at finding specific urate crystals is the gold standard for the diagnosis of gout. Currently, the treatment options in modern medicine are mostly western medicine-based for the treatment of a single target. Drugs, such as ebuxostat, benzbromarone, and allopurinol, reduce uric acid level. Other drugs, such as colchicine, glucocorticoids, and non-steroidal anti-inflammatory drugs, reduce inflammation. Analgesic and symptomatic treatment options are accompanied by certain adverse reactions, such as gastrointestinal reactions and liver and kidney function damage. Moreover, they have single therapeutic targets and are associated with easy relapse after drug withdrawal.–5 Therefore, the search for drugs with fewer side effects is a current research focus. In recent years, traditional Chinese medicine (TCM) has gradually been acknowledged to have advantages in the treatment of gout. Research in this area is also deepening, but its therapeutic mechanism needs to be further verified.Gout is a common inflammatory arthritis known as the “King of Diseases or King’’s Disease.” It results from a long-term purine metabolism disorder that causes an elevation in uric acid level and deposition of urate crystals around the joints. The disease is most common in adult males, with a male-to-female ratio of 10:1, and its incidence and prevalence are on the rise.–8 Further studies revealed that Miao medicine Tongfengting decoction has anti-inflammatory effects by its ability to reduce serum uric acid level and inhibit cyclooxygenase-2 (COX-2) and xanthine oxidase (XOD) activities.,10 Currently, TCM uses a multi-component and multi-target approach; its mechanism of action in the treatment of gout remains unclear, requiring further clarification.Gout is categorized as “arthralgia” and “joint-running wind” in TCM. This disease is mostly caused by the patient’s yang exuberance, damp-heat brewing internally, plus feeling of exogenous pathogens, damp heat stasis toxin blocking joints, and poor qi and blood. Therefore, treatment should focus on heat clearance, detoxification, blood circulation promotion, and turbidity removal. Miao medicine Tongfengting decoction clears heat, detoxifies, dispels wind and dampness, promotes blood circulation, and dredges collaterals. The prescription is composed of Reineckia carnea (Andr.) kunth, Musa basjoo, Luoshiteng, Sargentodoxae caulis, gypsum, Anemarrhena spp. and other medicines. Miao medicine Tongfengting decoction is a complementary and alternative treatment method for gout. Consistent with traditional Chinese medical theory, it has been used in the undergraduate room for more than 10 years, and more than 5000 individuals have visited the clinic. In previous clinical study, Miao medicine Tongfengting decoction had a definite curative effect on the clinical symptoms of patients with gouty arthritis. Moreover, it reduced TCM syndrome scores and laboratory indicators and had no obvious adverse reactions. Therefore, based on network pharmacology analysis and molecular docking technology, this study explored the molecular mechanism of Miao medicine Tongfengting decoction in the treatment of gout and provided a theoretical basis for later clinical application and novel ideas for further studies.Network pharmacology is an emerging research method that explores the relationship between drugs and diseases through active ingredients, diseases, and targets of TCM. Based on these, the molecular mechanism of drug treatment of diseases is explored. Its holistic system concept is consistent with the holistic view of TCM and principles of syndrome differentiation and treatment.The chemical components of the Miao medicine Tongfengting decoction were obtained from the TCMSP database. The screening criteria for active pharmaceutical ingredients and protein targets were drugs with bioavailability (OB) ≥ 30% and drug similarity (DL) ≥ 0.18. Using the Uniprot Protein Database, the species parameter was set as that of humans and protein targets were normalized as gene names.“Gout” and “gout arthritis” were used as search words to find confirmed gout-related genes on GeneCards, TTD, and OMIM human gene platforms. The Excel format of the 3 databases were downloaded and then merged together after unique gene symbol. Additionally, the Bioinformatics platform was used to obtain the potential targets of Miao medicine Tongfengting decoction in the treatment of gout, as well as the cross targets of disease and drugs.The target of the intersection between Miao medicine Tongfengting decoction and gout were imported into the string database. The species was set to “homo sapiens,” and the minimum interaction score correlation was set to “highest confidence” (≥0.400). The drug-disease PPI network diagram was obtained initially. The TSV file was downloaded and processed using the Cytoscape 3.90 software. The protein interaction network diagram was obtained, and the core targets were filtered by analyzing parameters, such as network degree, using the Network Analyzer plug-in.The compounds of the Miao medicine Tongfengting Decoction and their targets were classified. The active ingredient of the Miao medicine Tongfengting Decoction-target network for the treatment of gout was constructed using the Cytoscape 3.90 software. Using the built-in tools of the software (degree), betweenness centrality and other network topology parameters were used to screen the key active ingredients of the drug.The shared core targets were imported into the gene function annotation analysis platform, Metascape. The species was set as the human species, and the KEGG pathway and GO enrichment analyses were submitted to establish a “core target-biological pathway” network to screen out the main pathways. The KEGG enrichment bubble chart and GO term enrichment biological process, cellular compartment, and molecular function 3-in-one histogram were generated online. The results of the analysis by Bioinformatics were used to reflect the interaction between drug targets and gout disease target-related pathways.The 2-dimensional structure of the key compounds were downloaded from the PubChem database. The OpenBabel software was used to convert the pdf structural formula of the compound into Mol2 format. Using the String online platform, the best structural formula of the protein was found. Thereafter, the 3-dimensional structural formula was downloaded from the PDB database. The PyMol software was used to convert the water molecules and compounds of the protein. Finally, the structural formulas of key compounds and proteins are imported into AutoDOck software for docking.http://links.lww.com/MD/I131, Supplemental Content, illustrate Basic information of 183 active compounds from TCMSP) and 3102 targets were identified for the remaining components. Using the Uniprot Protein Database to transform gene names to obtain 410 target genes and exclude the active ingredients that had no targets, 142 active ingredients were obtained. According to literature,–17 gypsum, Musa Basjoo, and Reineckia carnea (Andr.) kunth have 26 active ingredients (see Table The names of the constituents (including Reineckia carnea (Andr.) kunth, Musa Basjoo, Luoshiteng, Sargentodoxae caulis, gypsum, Anemarrhena spp, Phellodendri chinesis, smilax tuber, coix seed) were entered into the TCMSP database. Pharmacokinetic parameters with OB ≥ 30% and DL ≥ 0.18 were set as screening conditions, and a search for the active pharmaceutical ingredients was conducted. No active ingredients were obtained from gypsum, Musa basjoo, and Reineckia carnea (Andr.) kunth on the platform. A total of 183 active ingredients  -1, 7-bis(4-hydroxy-3-methoxyphenyl)hepta-4, 6-dien-3-one,and the results are detailed in Table Previous clinical and experimental studies verified Miao medicine Tongfengting decoction has a significant effect on the treatment of gouty arthritis. This compound can significantly improve the clinical symptoms of patients, including pain relief. However, its mechanism of action remains unclear. Therefore, through network pharmacology and molecular docking technology, this study explored the molecular mechanism of Miao medicine Tongfengting decoction in the treatment of gouty arthritis to provide a good theoretical basis for clinical treatment. It is caused by hyperuricemia or persistently elevated serum uric acid levels that promote the deposition of urate crystals in the joint cavity, resulting in inflammatory arthritis. The disease is characterized by recurrent episodes in the acute phase. Gout presents as redness, swelling, heat, and pain in the affected joints with limited joint mobility. NLRP3, a member of the NOD-like receptor (NLR) family, is a multimeric protein. It plays an important role in innate and adaptive immune responses, evidenced by its contribution to autoimmune diseases, such as rheumatoid arthritis, systemic lupus erythematosus, and Sjögren’s syndrome. NLRs are the main regulators of immune response. When the NLPR3 inflammasome is overactivated, it induces the activation of caspase-1, resulting in the high expression and secretion of IL-1β and IL-18 into the extracellular space. The secretion aggravates the inflammatory response and increases the degree of joint redness, swelling, heat, and pain. In addition, Toll-like receptors (TLRs) play an important role in the regulation of the immune system and are involved in the process of inflammation and apoptosis. The TLR family recognizes specific signatures of invading microbial pathogens and danger-associated molecules. Its signaling pathway is divided into the MyD88-dependent pathway and TRIF-dependent pathway. The receptor activates downstream inflammatory cytokine production through signaling. Urate crystals, as danger signaling molecules, induce activation of the TLR4 receptor when it is recognized by TLR4 and interact with the MyD88 adapter to promote the activation of signaling cascades, such as NF-KB and MAKP. Moreover, they produce pro-inflammatory cytokines, such as IL-1, IL-6, and TNF-α; induce NLRP3 inflammasome expression; and release more inflammatory mediators.,25 Miao medicine Tongfengting decoction dispels pathogens and detoxifies, promoting blood circulation, removing turbidity, dispelling wind, and removing dampness. In preliminary clinical studies, Miao medicine Tongfengting decoction was found to effectively relieve joint pain, reduce inflammatory indicators, and significantly reduce the expression of TLR2/TLR4/MyD88 and other proteins in the joint synovium of gouty arthritis rats, reducing the inflammatory response.,27Gout is more of an autoinflammatory disease than a purely metabolic disease. AKT can regulate the secretion of inflammatory cytokines in neutrophils, and TLR2 is a receptor for Gram-positive bacteria-derived peptidoglycan and lipoproteins, which can be expressed in neutrophils. In the signal transduction pathway of TLR2, Activated AKT results in the rapid release of proinflammatory cytokines and chemokines.,30 That is to say, activated AKT participates in inflammatory response by phosphorylating various downstream signaling molecules in neutrophils.Through network topology parameter screening, the key targets of Miaoyao Tongfengting Decoction in the treatment of gout are AKT1, TNF, ALB, IL6, TP53, VEGFA, EGFR, SRC, JUN, STAT3, IL1B, EGF, MAPK3, MYC, HSP90AA1, PPARG, HRAS, TLR4, PTGS2, IL10, MMP9, ERBB2, PTPRC. The target with the highest Degree value was selected for molecular docking verification, and the results showed that the active ingredient could bind well to the target. AKT1 has the highest degree score in PPI and the strongest binding ability to key compounds, and is considered to be the pivotal target of Miaoyao Tongfengting decoction in the treatment of gout. AKT1 is a serine/threonine protein kinase and is a member of the AKT family. At present, 3 different isoforms of the AKT family have been found, namely AKT1, AKT2 and AKT3. AKT has been shown to play a key role in tumor cell proliferation, apoptosis, and protein synthesis. In addition, current studies have found that this target also plays an important role in the development and function of innate immune cells, and essential for the regulation of inflammatory responses.–33 Its anti-inflammatory effect is mainly through inhibit the activation of NLRP3 inflammasome by inhibiting the production of cytokines, such as tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, and regulating Th17/Treg balance. thereby inhibiting the activation of NLRP3 inflammasome and reduces the inflammatory response of joints. The active ingredient can not only reduce the expression level of inflammatory cytokines but also promote the production of anti-inflammatory cytokines.According to the DC value of the main active ingredients, quercetin, bis-demethoxycurcumin, ferulaic acid,  -1, 7-bis(4-hydroxy-3-methoxyphenyl)hepta-4, 6-dien-3-one, 2— hydroxy — 9 — phenyl — phenalen — 75 — one, 3-hydroxy- oleanene core compounds. Among them, quercetin had the highest degree value and largest number of targets. Quercetin is a natural flavonoid compound that exists in the form of glycosides in fruits, herbs, and vegetables, after clinical trials, no obvious toxicity or side effects occurred, and it has various biological properties, such as anti-inflammatory, antioxidant, neuroprotective, and immunomodulatory properties.Through network pharmacology and molecular docking, the potential targets and related pathways of Miao medicine Tongfengting decoction in the treatment of gout were studied. The results showed that a close relationship existed between the active compounds screened by Miao medicine Tongfengting Decoction and each target. Additionally, the active compounds had good binding activity. The study provides a novel idea of the mechanism of action of Miao medicine Tongfengting decoction in the treatment of gout. Additionally, it reflects the characteristics of a multi-target, multi-component, and multi-pathway strategy in the treatment of diseases by TCM, which is in line with the characteristics of TCM treatment based on dialectical treatment and overall concept.Conceptualization: Cong Huang.Data curation: Xin Peng, Nannan Zhang, Yuepeng Cao.Investigation: Zhigang Chen.Supervision: Wukai Ma.Writing – original draft: Xin Peng.Writing – review & editing: Zhengqi Liu.