Backgound: Pyeongwi-san (PWS) is a widely used formula for treating digestive disorders in Korea and China. Inflammatory bowel disease (IBD) is characterized by progressive inflammation of the gastrointestinal tract. Emerging evidence supports the protective effect of PWS against IBD, but specific mechanisms are still elusive. Methods: Active compounds of PWS were screened from the medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC) in the consideration of drug-likeness and oral bioavailability. Target candidates of active compounds were predicted using the ChEMBL database. IBD-related targets were obtained from the GeneCards and DisGeNET databases. The network of composition-targets-disease was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed. Molecular docking was used to simulate the binding affinity of active compounds on target proteins and molecular dynamics was used to validate the molecular docking result. Results: A total of 26 core target proteins of PWS were related to IBD. Enrichment analysis suggested that PWS is highly associated with tumor necrosis factor signaling pathway, apoptosis, and the collapse of tight junctions. Moreover, molecular docking and molecular dynamics simulation proposed β-eudesmol and -1,10-bisaboladien-3-ol to ameliorate IBD through the binding to TNF and MMP9, respectively. Conclusion: Present in silico analysis revealed potential pathways and insight of PWS to regulate IBD. These results imply that the therapeutic effect of PWS might be achieved via an inhibitory effect. Inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, is characterized by progressive intestinal inflammation. Although the incidence of IBD varies across countries, it has been increasing worldwide. The cause of IBD remains largely unknown; however, it is estimated that genetic and environmental factors are involved, and patients develop IBD in childhood or adolescence .Salvia miltiorrhiza Bunge in an acute kidney injury model through pregnane X receptor activation . Th. Th29]. Candidate target proteins of each compound were predicted using version 30 model of the ChEMBL database. SMILES of the compound were queried, and a total of of 290 targets have a probability over 0.7. After discarding duplicates and leaving those related to Homo sapiens, 257 target candidates of PWS remained . ComposiWe queried “inflammatory bowel disease” into the DisGeNET and GeneCards databases to identify disease-associated genes. As a result, 1391 IBD-associated genes were collected by filtering with over 0.7 of evidence index in the DisGeNET database. In the GeneCards database, 502 IBD-related protein-coding genes in the top 10% of the score are obtained. Consequently, 293 targets were overlapped and used for further analysis .The CTD network between PWS and IBD was constructed using Cytoscape software based on the STRING database A. CentraGO enrichment was analyzed to investigate the biological processes of 26 overlapping target candidates of PWS involved in IBD. We sorted the GO analysis results with a low false discovery rate and then high fold enrichment. The results showed that protein processing (GO:0016485), regulation of cysteine-type endopeptidase activity involved in the apoptotic process (GO:0043281), protein maturation (GO:0051604), cellular response to chemical stress (GO:0062197), and cellular response to organic cyclic compound (GO:0071407) were enriched in biological processes. In the case of cellular components, death-inducing signaling complex (GO:0031264), ripoptosome (GO:0097342), phosphatidylinositol 3-kinase complex, class IA (GO:0005943), phosphatidylinositol 3-kinase complex, class IB (GO:0005944), and phosphatidylinositol 3-kinase complex (GO:0005942) were predicted as candidate locations. Phosphatidylinositol-3,4-bisphosphate 5-kinase activity (GO:0052812), 1-phosphatidylinositol-4-phosphate 3-kinase activity (GO:0035005), phosphatidylinositol-4,5-bisphosphate 3-kinase activity (GO:0046934), phosphatidylinositol bisphosphate kinase activity (GO:0052813), and cysteine-type endopeptidase activity involved in the apoptotic signaling pathway (GO:0097199) were enriched in molecular function . We furtIn accordance with network and enrichment analysis which points to TNF, CASP3, and MMP9 as pivotal elements; we simulated molecular docking to find whether active compounds of PWS stably bind to the core target protein. Each ligand binding pocket of proteins was determined using the Protein Data Bank database and literature. We independently simulated the molecular docking of active compounds that target TNF, CASP3, and MMP9 ten times. The results of most simulations exhibited a stable binding pose consistent with already-known inhibitors. Generally, a pose with the lowest binding affinity or under −6 kcal/mol is regarded as stable binding. Molecular docking simulations almost showed a binding pose with binding affinity below −6 kcal/mol . Among tA molecular dynamics simulation is performed to verify the result of molecular docking using GROMACS with the Charmm36 force field. Prior to the simulation, unmodeled sequences that Pro182 to Ala187 (Pro-Glu-Gly-Ala-Glu-Ala) in the chain of B and Thr181 to Ala187 (Thr-Pro-Glu-Gly-Ala-Glu-Ala) in the chain C of TNF were filled with the template sequence of another TNF structure using the Modeller software of version 10.4. Without this procedure, the generation coordinate of the protein for the molecular dynamics would have failed.The simulation result of TNF showed stable binding of β-eudesmol to protein during 50 ns. The average RMSD of the distance between β-eudesmol and TNF was 0.11 nm with a standard deviation of 0.02 nm. Three snapshots were taken at 25 ns intervals to understand the trajectory of ligand and protein. β-eudesmol exhibited stability in the cavity between trimer of TNF during the simulation. The stably maintained hydrogen bond interactions also supported the binding pose of β-eudesmol to TNF .The binding pose of -1,10-bisaboladien-3-ol to MMP9 was stably maintained in the active site. Specifically, the planar conformation was bent quickly, but the structure binding to the pocket structure binding to the pocket next to the active site kept position throughout the simulation. The RMSD of the distance was 0.4 nm with the standard deviation of 0.05 nm. Hydrogen bond interactions were also stably maintained .However, the MD simulation of glabrocoumarin to CASP3 exhibited a different result from the molecular docking. The average RMSD was 3.5 nm with a standard deviation of 0.27 nm. The RMSD of glabrocoumarin to CASP3 was approximately 4 nm at the beginning of the simulation, followed by a decrease to 3.1 nm around 20 ns. Then RMSD fluctuated rapidly around 36 ns, and increased until the end of the simulation. Glabrocoumarin kept a binding position from the beginning of the simulation until about 25 ns, and slightly moved away from the catalytic domain .In the present study, we investigated the possible therapeutic targets and pathways of PWS involved in IBD using network pharmacology. Prior to analysis, metadata of PWS are required to identify the physicochemical properties of compounds contained in each herbal medicine. There are several databases of systemically organized information about traditional medicine, including TM-MC, traditional Chinese medicine integrated database, TCM database@taiwan, and traditional Chinese medicine systems pharmacology database analysis platform ,31,32,33To screen therapeutic compound candidates, drug-likeness and oral bioavailability must be considered. The definition of drug-likeness varies depending on the literature; nevertheless, therapeutic effects are expected in compounds with high drug-likeness in general. Oral bioavailability refers to the ratio of the amount that reaches the systemic circulation after oral administration of a drug .2 [Lipinski pointed out that a compound with poor absorption and permeation can be considered as low drug-likeness because it tends to fail early clinical trials . In his 2 . BOILED-2 . It can 2 . In part2 . Based o2 . As a re2 . This mo2 ,38. As aThe CTD network was constructed from the associations between predicted targets of active compounds of PWS and IBD-related targets. Overlapped targets between PWS and IBD in the network suggest that PWS is involved in IBD pathogenesis. A total of 26 pivotal candidates were derived from the network, and protein-protein interactions were investigated. Topological network analysis, which can be used for drug repositioning, provides a novel clue to the underlying biological complexity . CentralTNF, CASP3, and MMP9 showed high centrality in both degree centrality and eigenvector centrality. TNF is implicated in the inflammatory response. Infliximab, a monoclonal antibody against TNF, is used to treat inflammatory diseases . The antMolecular dynamics simulation is the computational method that tracks the trajectory of the molecules based on molecular mechanics . AlthougAs a result, β-eudesmol stably binds to the central cavity of the TNF trimer . Around Interestingly, the analysis of the molecular dynamics of glabrocoumarin to CASP3 showed a different implication than the result of molecular docking. Molecular docking simulation suggested the binding pose of glabrocoumarin to CASP3 as stable with the binding affinity of −8.131 kcal/mol. Nevertheless, molecular dynamics provided the trajectory that glabrocoumarin moves apart from the initial binding pose during the simulation around 37 ns .In summary, the inhibitory effects of β-eudesmol to TNF and -1,10-bisaboladien-3-ol to MMP9 were suggested by molecular docking and molecular dynamics simulation. These results also imply that the analysis of the binding of small molecules to proteins should be interpreted using molecular docking and molecular dynamics. Taken together, we suggest that PWS has a high potential to treat IBD by inhibitory effect on the inflammation, and the collapse of tight junctions.This study has some limitations. First, we focused on small-sized compounds that can undergo passive membrane transport because many criteria of physicochemical properties to determine good drug-likeness recommend molecular weight below approximately 500 Da ,54,55. TIn conclusion, we investigated the active compounds and candidate pathways of PWS involved in IBD using network pharmacology and molecular docking methods. As a result, 26 target proteins of PWS were identified and TNF signaling pathway and apoptosis were suggested as the highly enriched mechanism in the effect of PWS against IBD. Molecular docking simulation supports network and enrichment analysis through the showing stable binding affinity of active compounds to core target proteins. We suggest that β-eudesmol and -1,10-bisaboladien-3-ol in the PWS exhibiting the inhibitory effect to TNF and MMP9, could be a potential mechanism for ameliorating IBD. With further experimental validation, the results of this study will provide insight and evidence into the treatment of IBD in the future.


Fuzi decoction (FZD), a traditional Chinese medicine formula, was used to treat musculoskeletal diseases by warming channels, strengthening yang and dispelling pathogenic cold and dampness. In clinical practice, FZD has been used to treat rheumatoid arthritis and osteoarthritis. It alleviated osteoarticular disorders through ameliorating the degradation of cartilage and improving meniscal damage in osteoarthritis, while its roles and mechanisms in the treatment of bone loss diseases remain unclear. This study aims to investigate the underlying mechanisms of FZD in treating osteoporosis using an integrative method of network pharmacology and experimental study.In this study, network pharmacology was used to predict the core targets and potential pathways of the bioactive ingredients of FZD to attenuate osteoporosis. Molecular docking was performed to evaluate the interactions between core compounds and key targets. In addition, both cell and animal experiments were carried out to validate the role and potential mechanism in treating osteoporosis.In the present study, data revealed that kaempferol, beta-sitosterol, stigmasterol, fumarine, and (+)-catechin may be the primary bioactive ingredients of FZD in the treatment of osteoporosis, which were closely associated with the osteoporosis-related targets. And the KEGG results indicated that the NF-κB pathway was closely associated with the function of FZD in treating osteoporosis. In addition, in vivo demonstrated that FZD ameliorated osteoporosis. In vitro experiments showed that the pro-apoptotic factors indicators including CASP3 and BAX were decreased by FZD and the anti-apoptotic factor BCL2 was increased by FZD. In addition, FZD significantly suppressed the osteoclast differentiation in culture and the expression levels of osteoclast-related genes including TRAF6, CTSK, and MMP9. And the NF-κB pathway was confirmed, via in vitro experiment, to be involved in osteoclast differentiation.This study demonstrated that FZD played a pivotal role in suppressing the osteoclast differentiation via regulating the NF-κB pathway, indicating that FZD could be a promising antiosteoporosis drug and deserve further investigation.The online version contains supplementary material available at 10.1186/s13018-023-03842-1. Osteoporosis, a major health problem worldwide, is characterized by the loss of bone mass and the destruction of bone microstructure that can increase bone fragility . It was http://www.theplantlist.org. According to the records of Treatise on Febrile and Miscellaneous Diseases, the principal role of FZD is ameliorating the osteoarticular pain through warming channels, strengthening yang and dispelling pathogenic cold and dampness. In addition, it was reported that the monarch drug Fuzi could exert bone-protective effects because of its anti-inflammatory, analgesic, and chondroprotective roles [Traditional Chinese medicine (TCM) has gained popularity in recent years as a treatment for musculoskeletal disorders due to its mild side effects and obvious benefits. Several researchers have recently reported the application of TCM in treating osteoporosis , 9. Prevve roles , 15. Theve roles . In theive roles . The datve roles . The above roles . TheoretNetwork pharmacology, a method of drug research, has been used to analyze the relationship among biological systems, drugs and diseases . The conIn this present study, network pharmacology and experimental validation were applied to explore the role and potential mechanism of FZD in the treatment of osteoporosis. This study provided theoretical basis and experimental evidence for this strategy.https://old.tcmsp-e.com/tcmsp.php). Substances with oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 were identified as pharmacological compounds [FZD consists of five traditional Chinese herb medicines including Fuzi, Fuling, Renshen, Baizhu, and Shaoyao. The bioactive compounds of each herb in FZD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , the Online Mendelian Inheritance in Man (OMIM) database (https://www.omim.org/), the Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd), the PharmGKB database (https://www.pharmgkb.org/), and the DrugBank database (https://go.drugbank.com/). The databases were merged and the duplicate targets were removed. Then common targets were selected through intersection of the active ingredient targets of FZD and osteoporosis-related targets. The Venn diagram was plotted using the R software (R version R-4.2.0). It was considered that these common targets were the potential targets of FZD for treatment of osteoporosis.Through the keyword “osteoporosis”, osteoporosis-related targets were collected from five databases: the GeneCards database . A network topology analysis was performed to calculate the degree of each node through Network Analyzer (a Cytoscape plugin). And core targets were screened using Cytoscape. In the PPI network, the minimum required interaction score was medium confidence (0.4) and the disconnected nodes were hided in the network.p < 0.05) were shown in the plots.To further understand the possible biological functions and metabolic pathways involved in these genes, the Gene Ontology (GO) enrichment and the Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed with R software (R version R-4.2.0). The top 30 relevant KEGG pathways and the top 20 relevant GO enrichment results (https://pubchem.ncbi.nlm.nih.gov/), and Chem3D was used to minimize the energy of ligands. The PDB files of the proteins were retrieved from the PDB database (http://www.rcsb.org/pdb). Then water and ions of the structures were deleted by PyMOL software (version 1.7.1.0). The PDB files were transformed into the PDBQT format by the AutodockTools software (version 1.5.6) that can find the active pockets of the core proteins. The Autodock Vina-1.1.2 was applied to perform the molecular docking and to determine the binding energies. The molecules and proteins can form a stable complex if the binding energy ≤  − 5.0 kcal mol−1 and the RMSD value < 2.00. A binding value ≤  − 7.0 kcal mol−1 reveals a strong binding of the molecules to the receptor. The PyMOL software was used to visualize the results of molecular docking.To clarify the molecular mechanism, we analyzed the binding affinity, binding sites, and interactions between the core active compounds ((+)-catechin, beta-Sitosterol, Frutinone A, Fumarine, Kaempferol, and Stigmasterol) and the predicted core target . Ligand structures were obtained from PubChem database , Fuling (9 g), Renshen (6 g), Baizhu (12 g), Shaoyao (9 g). The drugs were soaked in 10 volumes of water for 30 min and boiled for 60 min. The decoction was filtered by medical gauze. Then the remaining herbs were boiled in 8 volumes of water and the process was repeated. The filtrates were pooled and concentrated to 1 g/ml and stored at 4 °C.The animal experiment was approved by the Ethical Committee of Shanghai Changzheng Hospital. Female Sprague–Dawley rats (6–8 weeks) were purchased from the Experimental Animal Center of the Zhejiang Academy of Medicinal Sciences  and randomly divided into sham, sham + FZD, OVX, and OVX + FZD groups. In rats anesthetized with isoflurane, bilateral ovariectomy was performed to induce osteoporosis in OVX and OVX + FZD groups. In the sham and sham + FZD group, the ovaries of rats were exposed but not resected. The rats in sham + FZD group and OVX + FZD group were given FZD (5.4 g/kg/day) by gavage twice daily (morning and evening) for 35 days as previous report , and theRats (6–8 weeks) were sacrificed through cervical dislocation after isofluorane-induced anesthesia. Then rats were sterilized with 75% alcohol immersion for five minutes. Bone marrow cells were harvested from the femur through flushing with phosphate buffer solution (PBS). The BMMs were cultured with alpha-minimum essential medium (α-MEM) containing 50 ng/ml M-CSF. Then 50 ng/ml receptor activator of NF-kB ligand (RANKL) was used to treat BMMs to induce osteoclast differentiation.g for 10 min, at 4 °C), the serum was filtered through a 0.22 μm membrane, which were then incubated at 56 °C for half an hour. Both the FCS and the BS were stored at − 80 °C.For in vitro experimental verification, the FZD-containing serum (FCS) and blank serum (BS) were prepared using TCM serum pharmacology methods. Rats in FCS group or in BS group received FZD (5.4 g/kg/day) or the same volume of sterile distilled water for seven consecutive days, respectively. Blood was collected via cardiac puncture. After centrifugation  analysis was carried out. The CCK-8 analysis was carried out according to the manufacturer instruction . Primary BMMs from bone marrow were directly seeded into 96-well cell plates. FZD was added to each well at concentration gradient after replacing the complete medium with serum-free medium. After 24 h, cells were rinsed and the CCK-8 solution was added into the plates. Absorbance density (OD) values at 480 nm measured through a microplate reader  was used to evaluate the cell viability. Cell viability (%) = (OD of test group/OD of control group) × 100%.The femurs were carefully collected after perfusion with 4% paraformaldehyde. A micro-computed tomography (micro-CT) system  was used to image the femora with the following parameters: voltage of 50 kV, current of 450–500 μA, and isotropic resolution of 9 μm. The images were built with NRecon software . The bone mineral density (BMD), the relative bone volume , bone surface density , connectivity density, total porosity, trabecular number, trabecular separation, and trabecular thickness of the micro-CT results were analyzed.MCSF and RANKL were used to induce osteoclast differentiation. BMMs were treated with FCS or PBS until the formation of multinucleated osteoclasts. Then cells were fixed with 4% paraformaldehyde and treated with 0.5% Triton X-100 for 5 min. Cells were then incubated with Phalloidin-iFluor 488 Reagent  in 1% BSA-PBS for 30 min at room temperature. Nuclear counterstaining was carried out using DAPI. The images of F-actin belt was captured with fluorescence microscopy and quantified by Image J software.+) osteoclasts were imaged with light microscopy and then counted with Image J software. TRAP+ cells with more than 3 nuclei were regarded as osteoclasts. In addition, TRAP staining was performed on the femoral sections. Paraffin embedded sections were dewaxed and rehydrated. Then the sections were stained using the TRAP staining kits . And the slices were imaged with the light microscope .To identify osteoclasts, TRAP staining was carried out. BMMs were used for TRAP staining when the fused cells could be observed with light microscope . The osteoclasts were fixed in 4.0% paraformaldehyde for 20 min before stained with TRAP working solution for half an hour at 37 °C. TRAP working solution was prepared according to the manufacturer guidelines . TRAP-positive , which were then quantified by the bicinchoninic acid (BCA) kit . SDS-PAGE was used to separate proteins that were then transferred to polyvinylidene fluoride (PVDF) membranes . The PVDF membrane was blocked with skimmed milk for 2 h and then incubated with primary antibodies and secondary antibodies. The following antibodies were used: GAPDH , CASP3 , BAX , BCL2 , MMP9 , TRAF6 , Cathepsin K (CTSK) , p-p65 , p65 , NFATC1 , c-FOS .Total RNA was extracted by the HiPure Total RNA Mini Kit . Real-time PCR was carried out using ChamQ TM Universal SYBR qPCR Master Mix. The levels of mRNA were presented with relative fold. The sequences of primers are provided in Table p < 0.05 was statistically significant.Experiments were performed for three times. All the quantitative data in the present study were expressed as the mean ± SD. Multiple group comparisons were carried out with ANOVA. Graphpad prism (version 8) and SPSS (version 24.0) were used to perform statistical analyses. The five herbs of FZD, which include Fuzi, Fuling, Dangshen, Baizhu, and Shaoyao, have their bioactive ingredients that were collected if the OB was ≥ 30% and the DL ≥ 0.18. A total of 77 active components of FZD  were obtained . The targets that were not associated with other targets in the network were hidden. The result of PPI network which include 40 nodes and 235 edges was shown  network was performed through STRING database  enrichment analysis was carried out based on the common targets between FZD and osteoporosis, which included 1275 entries in biological progress (BP), 12 entries in cellular components (CC) and 116 entries in molecular functions (MF) , Frutinone A versus PGR , Frutinone A versus PTGS1 , Frutinone A versus PTGS2 , Fumarine versus PGR , Fumarine versus RXRA , were visualized -catechin) and the five core targets  were obtained through the degree and the MCC methods. The binding energy was used to evaluate binding affinity among the components and the proteins. The results of molecular docking reveal that the binding energies among these core ingredients and core targets were all less than − 7 kcal molets Fig. A. Accordzed Fig. B–G.Fig. + OCs were red and distributed along the trabecular bone  has been proved to be crucial for the activation of receptor activator of nuclear factor-κB (RANK) in OCs , which cAccording to the results of KEGG enrichment results, NF-κB pathway was involved in the role of FZD against osteoporosis. And it was reported that the formation of OCs was inhibited by aspirin through regulating the NF-κB and Nuclear factor of activated T-cells cytoplasmic 1 (NFATc1) pathway . What’s Then the expression levels of the downstream factors including c-FOS and NFATc1 were evaluated. The western blot results showed that the expression of both c-FOS and NFATc1 were raised after the induction of OCs through the treatment of RANKL Fig. A, D, E, Osteoporosis, a common condition in the elderly, severely comprises the quality of life and has exerted tremendous burden on the society and families. Investigating the underlying mechanisms of osteoporosis will be greatly meaningful. Bone homeostasis is dependent on the balance between osteoblasts and osteoclasts. The disequilibrium between the activities of osteoblasts and osteoclasts tends to be closely associated with bone loss, which can be caused by the overactivation of osteoclasts. Therefore, suppressing the excessive differentiation of OCs is an important treatment strategy. At present, an increasing number of medicines aiming at inhibiting the excessive formation of OCs have been used to treat osteoporosis, such as cathepsin K inhibitors and alendronate , 31. How2 [Based on network pharmacology, core targets (PTGS1 and PTGS2) and key ingredients  that might play pivotal roles in treating osteoporosis were screened out. Previously reported study demonstrated that the decreased osteoclastogenesis might be associated with the expression of PTGS2  . And sev2 , 37. In 2 . TherefoAs for the key ingredients of FZD in treating osteoporosis, a series of studies have been conducted to explore the roles and potential mechanisms involved in the treatment of diseases via these ingredients. Kaempferol that presents in commonly used traditional Chinese medicines such as Ginkgo biloba, Moringa oleifera and propolis, is the first leading component of the key ingredients of FZD in this present study. Previous studies revealed that kaempferol could exert potent osteoprotective effects through complex and multifaceted mechanisms. For example, Trivedi et al. found that kaempferol prevented the femur and the vertebra from OVX-induced trabecular bone loss. And the BMMs isolated from the kaempferol-treated OVX animals had more mineralized nodules when compared with the BMMs from the control group . ProuillAccording to the experiments in this present study, FZD retarded the process of osteoporosis via suppressing the maturation of multinucleated OCs. The expression levels of the OCs-related indicators such as TRAF6, CTSK and MMP9 were lowered by FZD. TRAF6 has been proved to be essential for the activation of RANK that plays an important role in the differentiation and function of OCs . And preThe present study also has several limitations. Although the NF-κB signaling pathway was indeed regulated by FZD, rescue experiment should be carried out to further validate the role of the NF-κB pathway in retarding osteoporosis and this will be carried out in future studies. Many other predicted signaling pathways or targets were not identified and further researches will be conducted in the future.Collectively, the present study identified several core targets, ingredients and signaling pathways that might be closely associated with the therapeutic role of FZD in countering osteoporosis. Based on network pharmacology and experimental validation, this study provided insights into the potential mechanisms in the treatment of osteoporosis by FZD. The results revealed that FZD alleviated the OVX-induced bone loss. And FZD downregulated the expression of osteoclast function-associated genes including TRAF6, CTSK and MMP9. In addition, the RANKL treatment-induced activation of the NF-κB pathway and the downstream NFATc1 and c-FOS was inhibited by FZD, which demonstrated that FZD could exert inhibitory effects on the formation of OCs and bone resorption through regulating the NF-κB pathway. Taken together, the findings of this study suggested that FZD could be a strategy for the treatment of osteoporosis. This study laid a good theoretical foundation for further investigations.Additional file 1. The active components of FZD.Additional file 2. The targets of the herbs.Additional file 3. The gene targets of osteoporosis.Additional file 4. The common targets between FZD and osteoporosis.Additional file 5. The pathways associated with the common targets.Additional file 6. The details of both the NF−kappa B signaling pathway and the reactive oxygen species.


Perilla frutescens (L.) Britt. is extensively cultivated in East Asia as a dietary vegetable, and nutraceuticals are reportedly rich in bioactive compounds, especially with anticancer activities. This study explored the in vitro cytotoxic effects of P. frutescens parts’  extracts on prostate cancer cells (DU-145) and possible interactions of putative metabolites to related prostate cancer targets in silico. The ethanol extract of P. frutescens leaves was the most cytotoxic for the prostate cancer cells. From high-performance liquid chromatography analysis, rosmarinic acid was identified as the major metabolite in the leaf extracts. Network analysis revealed interactions from multiple affected targets and pathways of the metabolites. From gene ontology enrichment analysis, P. frutescens leaf metabolites could significantly affect 14 molecular functions and 12 biological processes in five cellular components. Four (4) KEGG pathways, including for prostate cancer, and six (6) Reactome pathways were shown to be significantly affected. The molecular simulation confirmed the interactions of relevant protein targets with key metabolites, including rosmarinic acid. This study could potentially lead to further exploration of P. frutescens leaves or their metabolites for prostate cancer treatment and prevention. Prostate cancer (PCa) affected 14.1% of men worldwide, particularly old-aged men, in 2020 . HoweverTaxus chinensis (Chinese yew) -CONH2. Prevalent drug–receptor interactions, such as hydrophobic contact, hydrogen bonding, and π-stacked interactions -CONH2 , alpelisractions , were obPF has a wide array of pharmacologically active compounds historically used in TCM. Phytochemicals, such as polyphenols, are secondary metabolites of plants that are generally responsible for their defense mechanism against external stresses, such as infection, predators, environmental changes, and, to some extent, their growth . Most po1, 5, 15, 23, 24, 32, 36, and 37 have the most relevant targets on PCa cytotoxicity.Cancer is a multifactorial disease triggered through genetic alterations and instabilities, epigenetics, weakened immunosurveillance, infection, and so on . HallmarPrevious studies revealed that many of the main compounds and such derivatives have been able to impact PCa cell proliferation and growth. It was known that apigenin targeted PI3K/Akt/Fox-O, β-catenin, and insulin-like growth factor-I signaling pathways, impacting PCa proliferation . Also, aIn this study, affected targets from PRAD with PF-L were subjected to a GO term, KEGG, and Reactome pathway enrichment analysis, which uncovered prospective cytotoxic mechanisms to PCa in silico. Relevant processes that could be involved in this phenomenon from GO enrichment are the response to oxygen-containing compounds, protein phosphorylation, cellular component disassembly, and to a lesser extent, angiogenesis. From pathway enrichment, PF-L metabolites potentially regulate the AMPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, miRNAs in cancer, prostate cancer, steroid and lipid metabolism, SCF-KIT and PIK3/Akt signaling pathways, EPH-ephrin mediated cell repulsion, and the VEGFA-VEGFR2 pathway. Collectively, these pathways were discovered to significantly affect the tumorigenesis, growth, progression, energy regulation, metastasis, and adhesion of PCa cells ,67,68,69One of the relevant protein-coding genes from the PPI network is Fyn, under the Src family kinases. This gene is typically overexpressed in PCa, a signaling protein typically attributed to tumor cell proliferation and metastasis ,71. It rVβ3 or vitronectin receptor revealed the most effective reduction in tumor cell angiogenesis and metastasis compared to the other classes of integrins [The molecular action of PF-L metabolites was also implicated with ITGB3, a transmembrane integrin receptor primarily responsible for cell adhesion, migration, and macrophage phagocytosis ability ,77. It wntegrins ,77,78,79ntegrins ,78. Howentegrins . This reAnother PPI network-significant protein-coding gene with PF-L is the PIK3R1 from the PIK3/Akt pathway, which typically regulates cell death, proliferation, metabolism, and angiogenesis . It is aLastly, PF-L metabolites were shown to have significant interaction with PDGFRs, which are transmembrane receptor tyrosine kinases that can initiate various pathways with the SH2 domain-containing signal transduction molecules as inducers of cell growth and division, such as in the PI3K/Akt pathway . DimerizMolecular docking was utilized to test whether the selected metabolites could interact with the key targets. This simulation revealed stable binding with respective proteins consistent with previous activities of their analogs. RA had the best activity, which could affirm the activity of PF-L on PCa. These results suggest that added structural features in the main compound may have increased their potency.Anti-PCa activity from PF-L extracts can be promising, especially as a preventative measure and an additional diet for individuals with a high risk of PCa development. However, pharmacokinetics and toxicity with normal cells in vitro and in vivo tests are required to successfully evaluate PF-L’s potential as an anticancer agent for PCa or other cancer types. Also, the polar and nonpolar metabolites present in PF-L could be synergistic or antagonistic for the anti-PCa activity of PF and could be addressed from compound isolation and comparative explorations. At the same time, predicted mechanisms and actions from bioinformatic analyses should be further validated and investigated with gene expression studies. Meta-analysis of PCa patients and people at risk with their diet on such TCMs could provide an expansive vantage point of its potential as a cancer curative or preventative agent. Interestingly, other environmental conditions for the plant may lead to unique secondary metabolites, hence different activity.P. frutescens stem (PF-S), leaves (PF-L), and seeds (PF-SD) were acquired from a local TCM store located in Tainan City, Taiwan. The identification of P. frutescens was authenticated by Dr. Chia-Jung Lee, Ph.D. Program in Clinical Drug Development of Herbal Medicine, College of Pharmacy, Taipei Medical University. A voucher specimen was deposited as #2023-CJCU-PF-001 at the Department of Medical Sciences Industry at Chang Jung Christian University, Taiwan.Dried The samples were collected from manually separated leaves and stems, where the petioles were included with the latter. These samples were mechanically ground for surface area enlargement and particle size reduction for effective extraction. For ethanol extracts, dried samples (50 g) were immersed with one-liter 95% ethanol under a two-hour 65 °C water bath and total reflux. For water extracts, dried samples (50 g) were leached with one liter of distilled and deionized water in a traditional Chinese decoction pot until reduced to ~200 mL. The crude extracts were vacuum-filtered with 0.45 µm filter paper, concentrated by vacuum evaporation, and lyophilized to obtain a solvent-free extract stored in a −20 °C refrigerator for further analysis. The yield from the extraction is reported in The total phytochemical contents of PF extracts were assessed according to Tsai et al.  with sliw/v) Na2CO3 was added. The mixture was incubated for 30 min at room temperature and transferred to a 96-well microarray plate. The absorbance of the mixtures was measured at 600 nm.Gallic acid standards were prepared for TPC analysis. The standard and sample solutions (80 μL) were added with 400 μL of 0.2 N Folin–Ciocâlteu reagent. After five-minute equilibration, 320 μL of 7.5% (w/v) AlCl3 reagent. The mixture was incubated for 15 min at room temperature and transferred to a 96-well microarray plate. The absorbance of the mixtures was measured at 430 nm.Rutin standards were prepared for TFC analysis. The standard and sample solutions (500 μL) were reacted with 500 μL of 2.0%  and incubated at 37 °C. After 24 h of incubation, the cells were treated with various concentrations (two-fold dilution from 5.00 to 0.625 mg/mL) of crude extracts for another 24 h. The positive control in this analysis was 1.00 mg/mL of 5-fluorouracil (5FU). Cell viability was determined using a WST-1 assay (AbcamTM) [DU-145 cells were cultured in 96-well microarray plates (2 × 10AbcamTM) . BrieflyThe major compound was separated and identified in the extracts with liquid chromatography  for verification through a Hypersil™ BDS C18 column  kept at 25 °C. Chromatography conditions were adapted from Lee et al.  with slip < 0.05 was set throughout the analysis.Microsoft Excel was used for data processing, and GraphPad Prism software was used for statistical analysis and data visualization. Experimental data are reported as mean ± standard deviation (SD). Multiple comparisons of means were conducted through one-way ANOVA and Dunnett’s pairwise comparison test. A statistical significance of A brief prediction of the mechanism of action of PF with PCa cytotoxicity was performed with network analysis adapted from Jin et al.  and Pengp < 0.01 and |log2 FC|> 1. Gene nomenclatures were standardized into their official gene symbols using SynGO  [The experimental and predicted gene targets of previously identified putative metabolites from PF extracts were identified from SuperPred . This target prediction tool utilizes logistic regression and 2048-long Morgan fingerprints with 94.1% target prediction accuracy . The defne 2023) . Matchedne 2023) ,105.Homo sapiens” species were considered significant. The PPI network is transferred CytoScape 3.9.1 for further analysis [The identified matched protein-coding genes were imported to the STRING database 11.5  for the construction of a PPI network ,117,118.analysis . Proteinanalysis . The metHomo sapiens” species. Secondary data filtering of GO terms was automatically performed from g:Profiler with a simple greedy search algorithm. Data visualizations were performed in Python 3.11.Functional profiling was performed with g:Profiler  for the exploration of the predicted pharmacological mechanism and signaling pathways involved in the action of metabolites to the selected targets ,121. Relwww.rcsb.org, accessed in June 2023). Afterward, water molecules and irrelevant heteroatoms were removed, and polar hydrogens were added to the protein structure. The proteins were prepared with the protein preparation protocol (at pH 7.4 and 0.145 M ionic strength) in Discovery Studio. These proteins were minimized until an energy gradient of 0.01 with CHARMm force field and the Momany-Rone partial charge estimation method. The defined binding sites were determined from the co-crystallized ligands volume or eraser algorithm in Discovery Studio [The molecular modeling and visualization software BIOVIA Discovery Studio was employed for the docking analysis. Three-dimensional (3D) structure-data files (SDFs) of ligand molecules were collected from PubChem . The ligands were prepared with the ligand preparation (at pH 7.5 ± 1.0) and minimization protocols in Discovery Studio. Experimentally elucidated structures of protein targets were collected in PDB file format from the Protein Data Bank (PDB, y Studio . The docy Studio . Receptoy Studio .Most PF extracts follow dose–response behavior for PCa cell cytotoxicity with DU-145 cells, where its ethanolic leaf extracts had the most cytotoxic activity. While characterized as having minimal activity as an anti-prostate cancer agent, the plant may have preventative potential for PCa development and progression. Regardless, bioinformatic analyses revealed that many minor metabolites and the major metabolite, RA, could have contributed to the cytotoxic potential of the mentioned extract, affecting the determined key targets FYN, PDGFRA, PDGFRB, PIK3R1, and ITGB3. Molecular docking of the compounds to the key targets consistently verified the protein–ligand interactions from network pharmacology results. Hence, the cytotoxicity of the leaf extract can be postulated from cell death induction and cell cycle arrest from action to the key targets. More advanced studies, such as in vivo trials, gene expression, or clinical trials, are recommended to verify activities and mechanisms of action from PF-L and their metabolites to PCa.


The findings of this study expand our theoretical understanding of embryonic hair follicle development in cashmere goats and serve as a premise for future research.The cashmere industry is a crucial resource and pillar of animal husbandry in the desert and semidesert regions of the central and western Inner Mongolia Autonomous Region. The development of embryonic hair follicles is vital in determining the quality and quantity of cashmere; however, the molecular mechanisms underlying embryonic hair follicle development remain unknown. This study employed proteomic and metabolomic techniques to analyse embryonic skin samples from cashmere goats. The results revealed that the oxytocin signalling pathway is crucial in embryonic hair follicle development by activating the MAPK and Ca2+ signalling pathways, and a related regulatory network map was constructed. This study provides a global perspective on the mechanism of HF development in cashmere goats and enriches our understanding of embryonic HF development.The hair follicle (HF) is the fundamental unit for fleece and cashmere production in cashmere goats and is crucial in determining cashmere yield and quality. The mechanisms regulating HF development in cashmere goats during the embryonic period remain unclear. Growing evidence suggests that HF development involves complex developmental stages and critical events, and identifying the underlying factors can improve our understanding of HF development. In this study, samples were collected from embryonic day 75 (E75) to E125, the major HF developmental stages. The embryonic HFs of cashmere goats were subjected to proteomic and metabolomic analyses, which revealed dynamic changes in the key factors and signalling pathways controlling HF development at the protein and metabolic levels. Gene ontology and the Kyoto Encyclopaedia of Genes and Genomes were used to functionally annotate 1784 significantly differentially expressed proteins and 454 significantly differentially expressed metabolites enriched in different HF developmental stages. A joint analysis revealed that the oxytocin signalling pathway plays a sustained role in embryonic HF development by activating the MAPK and Ca The cashmere goat industry is a crucial resource and pillar of animal husbandry in the desert and semidesert regions of the central and western Inner Mongolia Autonomous Region, playing a vital role in maintaining frontier stability, constructing ecological barriers, and promoting rural revitalisation ,2. InnerConversely, it is very difficult to stabilise cashmere traits due to factors such as population characteristics, rearing environments, and physiological diseases. The quality of cashmere is gradually declining. Although there are still a few populations of cashmere goats with a velvet fibre diameter of around 14.00 μm in the main production areas of Alashan, Inner Mongolia, and Ordos in the west, it is difficult to establish an industrial scale due to the small amount of ultra-fine-type cashmere goats.VEGF and Tβ4-overexpressing cashmere goats exhibited faster HF development and a higher cashmere yield than wild-type cashmere goats [The current traditional breeding methods are feasible to a certain extent; however, the insufficient diverse germplasm resources, long breeding time, and high breeding cost requirements make breeding high-quality-type cashmere goat populations extremely challenging. Genetic improvement programs for cashmere based on quantitative genetics also require the association of large amounts of genotypic and phenotypic data, and the process relies on phenotypic selection and empirical judgment, which does not accurately reflect the genetic level and potential of an individual or family line. Similarly, currently, molecular genetics is an important breeding tool. For example, Zhang et al. reported that melatonin promotes secondary hair follicle (HF) development in early postnatal cashmere goats and improves cashmere quantity and quality ,4,5. In re goats ,7,8. HowIn goat skin tissue, HFs are divided into primary HFs (PHFs) and secondary HFs (SHFs). PHFs produce coarse hair with a well-developed medulla; cashmere is the unmedullated fibre produced by SHFs. The development of HFs in cashmere goats primarily occurs during the embryonic stage ,10. The EDAR; this indicates that these signalling pathways are interlinked in HF development [The HF is a crucial micro-organ in mammals, consisting of a relatively stable upper permanent region, including the funiculus and isthmus, and a lower follicular region, comprising the hair bulb and suprabulbar region . The bulelopment ,17,18.Additionally, the pathway inducing HF atrophy can be inhibited by Dickkopf-associated protein 1 (DKK1): this phenomenon inhibits β-catenin phosphorylation, consequently inhibiting the Wnt signalling pathway . In the FGF10, while lnc-H19 functions in hair papilla cells through the chi-miR-19-214p/β-catenin axis [Furthermore, the BMP signalling pathway is involved in embryonic skin appendage morphogenesis and postnatal HF growth, with BMP2 and BMP4 genes inhibiting HF growth and development by blocking the entry of HF cells into the anagen phase ,22. In anin axis ,25.Despite numerous studies on the molecular mechanisms of HF development, the global developmental mapping of HFs for the unique structure of cashmere goats is lacking. Notably, the mechanisms of action at the protein and small-molecule metabolic levels during HF development remain unclear, limiting the effective integration of various aspects of research and affecting the further development of molecular biology breeding in cashmere goats. Therefore, combining proteomic and metabolomic approaches is necessary to elucidate the molecular mechanisms underlying cashmere genes. This study systematically analysed the proteome and metabolome of skin samples from the embryonic stages  of cashmere goats. Our data reveal the regulatory processes of proteins and metabolites in HF development, expounding the pathways in HF development and revealing unique differences between proteomes and metabolomes; additionally, a combined analysis of the proteome and metabolome provided relevant evidence for regulating HF development during the embryonic period. Constructing a global view of HF development during the embryonic period of the cashmere goat could facilitate a more comprehensive understanding of HF development in cashmere goats, especially SHF development.This study selected and grouped 24 female cashmere goats with clear pedigrees and similar production functions. After the oestrous cycle had commenced, artificial insemination was performed by using semen from the same ram and was recorded as embryonic development day 0 (E0). Pregnant ewes were then kept in a combined flock, with the temperature of the goat house maintained at 15–21 °C to ensure good ventilation and air quality. The grazing time was ≥12 h daily, with water consumed thrice. Each ewe was supplemented with at least 150 g of nutritious feed daily, including 1.5% salt, 1.0% trace elements, and 0.5% vitamins. When embryos reached E75, E85, E105, and E125, at least six goat foetuses were collected at each stage by using veterinary anaesthesia and inverted surgery. Samples were rapidly placed in liquid nitrogen for storage after collection, and the skin of cashmere goat foetuses was used for a subsequent histological analysis and morphological observation.2 skin sample was obtained from the posterior side of the scapula on the somatic side of the foetus at the point where it crossed the midline of the abdomen and was used for morphological observations.After obtaining a goat foetus from the E75, E85, E105, and E125 stages, a 1 cmThe skin samples were fixed with 4% paraformaldehyde for 24 h. The samples were then subjected to sequential ethanol gradient dehydration, xylene infiltration, paraffin embedding, and preparation as 7 μm sections by using a paraffin slicer. The sections were then sequentially stained by using haematoxylin, a differentiation solution, and eosin before being sealed with neutral resin and observed under a microscope.Total protein was extracted from 12 skin samples . The BCA working solution and standard protein solutions of different mass concentrations were prepared by using a BCA kit , with mass concentrations ranging from 0 to 2.0 mg/mL. Each protein sample (2 µL) was diluted 10 fold with ultrapure water, and then 200 μL of BCA working solution was added. The reaction was carried out at 37 °C for 30 min, and the absorbance was 562 nm.Subsequently, 100 μg of the protein sample was taken, and the volume was supplemented with lysate to 90 μL. Tris(2-carboxyethyl)phosphine (TCEP) reductant was added at a 10 mmol/L final concentration. The reaction was carried out at 37 °C for 30 min. Iodoacetamide was added at a final concentration of 40 mmol/L, followed by incubation for 40 min at room temperature while protected from light. TCEP reductant was added at a final concentration of 10 mmol/L, and the mixture was incubated for 60 min. The precipitate was separated by adding precooled acetone and fully solubilised with Tetraethylammonium bromide(TEAB) before digestion with trypsin at 37 °C overnight.The Tandem Mass Tag (TMT) labelling reagent (Thermo Fisher Scientific) was removed from −20 °C storage and thawed to room temperature. Acetonitrile was added, and the mixture was vortexed. One tube of TMT reagent was added for every 100 µg of peptide. The mixtures were then incubated at room temperature for 2 h. Hydroxylamine was added, and the reaction was carried out at room temperature for 15 min. Afterwards, an equal amount of labelled product was combined in a tube, and the mixture was dried with a vacuum concentrator.The peptide samples obtained were solubilised and separated on a reverse-phase C18 column for high-pH liquid-phase separation by using a UPLC buffer  at a UV detection wavelength of 214 nm with a volumetric flow rate of 200 μL/min and an elution time of 66 min. Based on their peak shapes and times, 20 fractions were collected from each group and combined into 10 fractions. They were concentrated by using vacuum centrifugation and dissolved with a mass spectrometry upsampling buffer for subsequent analysis.A second-dimensional analysis was performed by using nanoscale liquid chromatography–tandem mass spectrometry (LC-MS/MS) with an EASY-nLC™ 1200 system (Thermo Fisher Scientific) and a Q Exactive mass spectrometer (Thermo Fisher Scientific). The peptides from the previous step were solubilised in the mass spectrometry buffer  and separated on a C18 column for 120 min at a flow rate of 300 μL/min. Automatic switching between MS and MS/MS acquisition was performed with mass spectrometry resolutions of 70 K and 35 K, respectively. Full-scan MS was performed (m/z 350–1300), selecting the top 20 peptide parent ions for secondary fragmentation, with a dynamic elimination time of 18 s.m/z: 50–1000. The ion spray voltage was set at 5000 V for positive ions and 4000 V for negative ions, with a declustering potential of 80 V, spray gas pressure of 50 psi, an auxiliary heated gas pressure of 50 psi, curtain gas pressure of 30 psi, ion source heating temperature of 500 °C, and cyclic collision energy of 20–60 V.According to the manufacturer’s instructions, 24 samples were prepared for subsequent experiments . For each sample, 50 mg of the skin samples was obtained, and 400 μL of the extraction solution (acetonitrile:methanol = 1:1) was added. The supernatant was pipetted for subsequent LC-MS analysis by using a Triple TOF5600 Triple Quadrupole Mass Spectrometer . An equal volume of all the sample metabolites was mixed to prepare a quality control (QC) sample, with one QC sample inserted into every six samples before mass spectrometry detection. Mobile phase A consisted of water (containing 0.1% formic acid), and mobile phase B consisted of acetonitrile/isopropanol (1/1) (containing 0.1% formic acid). Next, mass spectrometry signal acquisition of the sample was performed in positive and negative ion scanning modes, with a mass scan range of Raw proteomic data were analysed by using ProteomeDiscovererTM Software 2.2. The search species was set as Capra_hircus, and the database used was Capra hircus (goat)|Proteomes|UniProt. During the process, the false discovery rate (FDR) for peptide identification was set to FDR ≤ 0.01, with proteins containing at least one specific peptide. The information of the identified proteins was compared with databases , with an annotation rate of 96.55–100%.https://hmdb.ca/, accessed on 25 March 2023) and Metlin  to obtain metabolite information.The raw metabolomic data were imported into the metabolomics processing software Progenesis QI  for analysis to obtain a data matrix. The data matrix was filtered, complemented, normalised, and log10 transformed to obtain the final data matrix for subsequent analysis. MS and MSMS information were annotated by using the public metabolic databases HMDB  for data analysis. The t-test function in R was used to calculate the p-value of significant differences between samples, and the fold change (FC) was calculated. The screening criteria for significantly differentially expressed proteins (DEPs) were as follows: upregulated proteins with p < 0.05 and FC > 2 and downregulated proteins with p < 0.05 and FC < 0.67. Afterwards, the differential proteins screened by the Student’s t-test method were subjected to an FDR correction, and each protein was arranged from small to large according to its p-value. Then, each protein multiplies its p-value by the total number and divides it by its number of arranged positions to obtain the FDR-corrected p-value. The calibration standard for this experiment is FDR ≤ 0.01.The preprocessed proteomic data were uploaded to the Majorbio Cloud platform . The R package ropls (Version 1.6.2) was used to perform a principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), with seven cycles of cross-validation used to assess the model stability. Additionally, a Student’s t-test and multiplicative analysis of variance were performed. The selection of significantly different metabolites was determined based on the variable importance in projection (VIP) values obtained from the OPLS-DA model and the p-value of the Student’s t-test, with metabolites having VIP > 1 and p < 0.05 considered significantly different. The p-values were corrected for multiple testing by using FDR ≤ 0.01.For data analysis, preprocessed metabolomic data were uploaded to the Majorbio Cloud platform , a comprehensive web service for biomedical data analysis and visualisation.An analysis of variance was performed by using visualisation tools in Hiplot Pro  was used for a functional clustering analysis of significant DEPs, and the Kyoto Encyclopaedia of Genes and Genomes  pathway database was used to analyse the metabolic pathways involved in the differentially expressed proteins.Gene ontology  to identify the pathways involved in significant differentially expressed metabolites (DEMs). The Python package scipy.stats was used for pathway enrichment analysis, and Fisher’s exact test was used to identify the biological pathways significantly relevant to the experimental treatments.Metabolic pathway annotation was performed by using the KEGG database  was preliminarily formed from E105 to E125. The structural characteristics of samples within the same stage were uniform, with significant structural differences between samples from different stages. In summary, three skin samples from each stage were selected for proteomic analysis, and six skin samples were selected for metabolomic analysis in this study a.p < 0.05 and fold change cutoffs of 2 for upregulation and 0.67 for downregulation. Most DEPs were observed at the E125 stage. After removing duplicates, 1784 significant DEPs were identified  revealed significant differences between the E75–E85 and E85–E125 periods, indicating two distinct stages of HF development with different biological functions . In the In KEGG enrichment analysis, the top 20 KEGG pathways were identified and annotated for enrichment in the six pairwise comparisons between the HF developmental stages b. The ret-test. A total of 454 DEMs were identified  were annotated. Module 3, with 28 enriched DEMs, showed high similarity and involved pathways such as apoptosis (map04210) and the sphingolipid signalling pathway (map04071). Two other modules, with 12 and 6 enriched DEMs, respectively, showed high similarity and were enriched in two signalling pathways. Finally, one module with three DEMs showed a consistent similarity, suggesting similar functional roles  in proteomic and metabolomic data  and neuroactive ligand–receptor interaction (chx04080 and map04080). As an upstream pathway of the MAPK and CaFurthermore, protein digestion and absorption (chx04974 and map04974), being significantly enriched in proteomic and metabolomic data (among the top 20 enriched pathways) and present in three pairwise comparisons in the joint KEGG analysis, showed a broad distribution of roles; this suggests an essential role of the protein digestion and absorption pathway during HF development. Additionally, several signalling pathways, such as the arachidonic acid metabolism pathway (chx00590 and map00590), were present in at least two pairwise comparisons, indicating that their roles warrant further investigation.2+ signalling pathways through the actions of OTR and Gq, consequently affecting HF development. These results suggest that the oxytocin signalling pathway (chx04921 and map04921), an upstream of the MAPK and Ca2+ pathways, may be involved in HF development by activating these pathways.This study constructed a functional pathway map for the oxytocin signalling pathway based on the results of combined proteomic and metabolomic analyses of embryonic HF development . The mapInner Mongolia white cashmere goats are selectively bred for their unique genetic characteristics and adaptability . They arThe HF is the basic unit responsible for fleece and cashmere production in goats. Based on the existing research, this study provides further details on HF development during the embryonic period in cashmere goats. Morphological observations revealed that the period from E75 to E85 is characterised by the genesis of SHFs, with the most significant morphological feature being the emergence of SHFs from the roots of PHFs and their extension into the dermis, consistent with previous studies. From E85 to E105, Some SHFs form independent HF structures, separating from the root of PHFs and forming independent follicular structures associated with the epidermis. This phenomenon helps us understand the developmental process of SHFs during the embryonic period. From E105 to E125, a preliminary trichome-type structure was formed due to rapid follicle differentiation. Significant morphological changes occurred during this period, with PHFs essentially complete and primary hair shafts beginning to break through the body surface. Additionally, SHFs develop rapidly, forming a preliminary trichome-type structure along with the PHFs. In summary, these observations can enable a better understanding of the HF development process and further clarify the stages of HF development in cashmere goats.The results of the present study provided insights into the expression of proteins and metabolites in the HF development stages of the embryonic period in velvet goats. Among them, the COL coding family proteins  were expressed at significantly higher levels in E75 than in the other three periods. The COL coding family proteins are the main components constituting the extracellular matrix (ECM), which enables the aggregation of the dermal papilla cells through their effects on cell adhesion , which iConcerning metabolites, prostaglandin H2 (PGH2) had high expression throughout HF development, and its expression was significantly higher during E125 than during the other periods. PGH2 is produced in large quantities downstream of the MAPK signalling pathway, converted to prostaglandin E2 (PGE2) and prostaglandin F2α(PGF2α), which alters intracellular signal transduction through specific binding to G-protein-coupled receptors (GPCRs), in turn altering cellular function. PGH2 may have an important role in the subsequent development of HF cells. Diacylglycerol (DAG) expression also changed significantly, and its expression was significantly higher in the period from E85 to E105 than in the E125 period. Some studies have shown that DAG is an important lipid second messenger, which can activate a variety of signalling cascades, such as protein kinase C (PKC) and Ras/Raf/MEK/ERK, and regulate various cellular functions, such as proliferation, differentiation, migration, and apoptosis .S. aureus infection (chx05150) pathway. The pathway is usually associated with soft-tissue skin infections and pneumonia, among others, and its action is accompanied by drastic changes in the keratin family [The regulatory mechanisms of embryonic HF development in cashmere goats are not comprehensively understood. However, several signalling pathways and transcription factors play essential roles. In the present study, a KEGG enrichment analysis revealed that the most significant enrichment was observed in the complement and coagulation cascade (chx04610) pathway, which mainly plays a role in immune defence, coagulation, and the control of vascular permeability . The patn family . This isInterestingly, the significant enrichment of differentially expressed proteins (DEPs) was observed in the dilated cardiomyopathy (chx05414), hypertrophic cardiomyopathy (chx05410), and protein digestion and absorption (chx04974) pathways. The present study does not seem to be able to link them to HF development; however, all three signalling pathways have important roles in angiogenesis and altered permeability, as well as in inflammatory responses. We speculate that they may, in turn, influence HF development through their involvement in cutaneous angiogenesis and development. In contrast, producing different G-protein isoforms in adrenergic signalling (chx0426) alters cellular function by affecting downstream effector enzymes. This may be related to the strong cell proliferation and differentiation during embryonic life .2+ signalling cascade response, affecting processes such as intracellular gene expression and protein translation, affecting cellular functions. The G-protein isoform Gq can also directly act on Ras and MEK5 in the MAPK signalling pathway to activate the MAPK signalling pathway. The MAPK signalling pathway regulates cell proliferation, differentiation, apoptosis, and other biological processes. It is closely related to Wnt/β-catenin, a key signalling pathway in HF development. The two pathways can regulate each other by directly or indirectly affecting the phosphorylation or degradation of β-catenin. In different cells and tissues, MAPK and Wnt/β-catenin signalling pathways can synergistically or antagonistically affect cell proliferation, differentiation, migration, and other functions. We speculate that during the embryonic HF developmental stage in velvet goats, the oxytocin signalling pathway may activate Ca2+ and MAPK signalling pathways. This, in turn, affects the downstream Wnt/β-catenin signalling pathway and ultimately impacts HF development.The present study showed that the oxytocin signalling pathway and neuroactive ligand–receptor interaction signalling pathway were enriched in pairwise comparisons between different developmental stages. The oxytocin signalling pathway typically plays a role in secretion, lactation, and regulating social behaviours and affective states such as prosociality and intimacy . Oxytoci2+ concentration, and gene expression. The link between this pathway and HF development remains unclear; however, it has been suggested to be associated with the presence of HF-associated pluripotent (HAP) stem cells. These cells may be the most primitive stem cells in the skin and can differentiate into neurons, glial cells, keratin-forming cells, and other cell types [The neuroactive ligand–receptor interaction pathway is a signal transduction pathway involving neuroactive ligands and receptors that can regulate cellular physiological functions such as neurotransmitter release, intracellular Call types .2+ signalling pathways. This study provides a global view of the mechanisms underlying HF development in cashmere goats and enriches our understanding of embryonic HF development.This study used proteomic and metabolomic techniques to construct a developmental atlas of HF development during the embryonic period in cashmere goats. A total of 6384 proteins were identified, with 1784 DEPs obtained from pairwise comparisons between developmental stages. Additionally, 789 metabolites were identified, with 454 DEMs obtained from different pairwise comparisons. GO and KEGG enrichment analyses were performed to provide comprehensive annotations of the developmental processes of HFs. A joint KEGG analysis revealed that the oxytocin signalling pathway plays a vital role in embryonic HF development by activating the MAPK and Ca


PanomiR integrates gene expression, mRNA–miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an  MicroRNAs (miRNAs) are small noncoding RNAs that act as potent regulators of cellular functions and molecular pathways . They poCurrent best practice for the transcriptomic study of miRNA regulation focuses upon miRNA–gene or one-to-one miRNA–pathway relationships. Widely used miRNA–pathway analysis techniques such as gene set enrichment and co-expression analysis primarily detect whether a single pathway is potentially regulated by a miRNA , 21. EnrWe have developed a framework to address the existing limitations of miRNA–pathway analysis from a systems perspective, to uncover how functional groupings of pathways are coordinated by miRNAs to form gene expression programs. Our system, Pathway networks of miRNA Regulation (PanomiR), discovers central miRNA regulators based upon their ability to target coordinate transcriptional programs. It determines if a miRNA concurrently regulates and targets a coordinate group of disease- or function-associated pathways, as opposed to investigating isolated miRNA–pathway events. PanomiR derives these multi-pathway targeting events using predefined pathways, their dysregulation in disease states, their relative co-activation, gene expression and annotated miRNA–mRNA interactions.PcXN.org), to define broad-scale differentially regulated pathway groups . Pa. Pa46]. Building upon our previous methodology, Pathprint , PanomiRWe capture pathway activity profiles in a two-step process: (a) we rank genes in each sample in descending order, according to their expression, i.e. the highest expressed gene gets the largest rank-score, and (b) we calculate the average squared ranks of genes that belong to a pathway as the activity score. Formally, for a pathway X with n genes, where  samples . PanomiRLimma package  . In. InLimmate (FDR) .Dysregulation of an individual pathway is rarely an isolated event since pathways share activity and are often co-regulated. PanomiR accounts for co-regulation to place differentially regulated pathways into groups that represent high-level disease programs by exploiting the PCxN : a referPanomiR masks PCxN to contain only the subnetwork of differentially regulated pathways that were identified from the two-group data analysis in the previous step. In the masked network, nodes represent differentially regulated pathways and edges activity-correlation of pathways. PanomiR subsequently identifies densely interconnected differentially regulated pathway subnetworks using graph clustering algorithms . The defPanomiR exploits the concept that a coordinate group of disease-associated pathways has common miRNA regulators. Using annotated miRNA–mRNA interactions and an empirical statistical test , it analP-values from Fisher’s exact test, capturing overrepresentation of targets of a specific miRNA in each individual pathway. To make analysis disease-, condition-, tissue- or cell-type- specific, PanomiR calculates overlap scores using only the genes expressed in the input experiment. In the second step, an overall targeting score for a given cluster of pathways  with 368 primary tumor samples and 49 controls . The sensitivity of miRNA prioritization to parameters of predicted miRNA–mRNA interactions was assessed using Jaccard similarity analysis (step 6). All steps of PanomiR’s framework  were evaP-value aggregation: using Fisher’s method and/or Stouffer’s method to obtain one single P-value that combines enrichment P-values of a miRNA within all pathways in a cluster. In addition to evaluation of miRNA prioritization, PanomiR’s pathway activity profiles were compared to MITHRIL’s results for assessment of disease-associated pathways. PanomiR was compared to other tools in terms of number of miRNAs detected, biological relevance of miRNAs attributed to their respective pathway groups and bias in prioritization of miRNAs with a large number of targeted genes.PanomiR’s miRNA prioritization was compared with widely used miRNA–pathway analysis tools DIANA-miRPath v4.0, MIENTURNET and MITHRIL  and primary solid tumors  from liver cancer gene expression RNA-Seq data and using the MSigDB pathway database. PanomiR detected 428 upregulated and 397 downregulated pathways in TP compared to NT .We generated and compared pathway activity profiles from normal tissues adjacent to tumor . PanomiR detected liver cancer pathways that were missed by enrichment analysis . Enrichment analysis (overrepresentation test) prioritizes pathways with more DE genes than expected by chance. This means that enrichment analysis misses pathways with differential activity between disease and controls subjects that do not have increased DE gene proportions. These results highlight two main advantages of PanomiR over pathway enrichment analysis: (a) the ability to detect significant functional dysregulation in disease even in absence of significant differential gene expression and (b) the ability to determine the direction of differential pathway regulation, i.e. upregulation or downregulation.We compared the pathway readouts of PanomiR with pathway enrichment analysis of DE genes from the same data set. Enrichment analysis (Fisher’s exact test) identified 51 enriched pathways . First, we asked to what extent PanomiR detected differentially regulated pathways in a random assignment of samples to case and control groups in liver cancer . PanomiR found a very small number of differentially regulated pathways  via randomized case/control sample assignment . This mez-test P-value <3.34 × 10−5; mean = 693.785 pathways at an FDR < 0.01; SD = 39.1). To assess PanomiR’s sensitivity irrespective of P-values cut-offs, we also compared the distribution of adjusted P-values of differentially regulated pathways between MSigDB and randomized pathway collections. A one-sided Kolmogorov–Smirnov test showed a significant difference between the randomized gene sets and known pathways  demonstrated any advantages over using randomly assigned gene sets. We generated randomized pathways by permuting gene labels to conserve the pathway overlap structure of the original MSigDB data set. We found that annotated gene sets generate a significantly larger number of differentially regulated pathways than randomized ones  and predicted miRNA–mRNA interactions  to detect miRNAs that target each cluster of differentially regulated pathways  and S6. −5, PanomiR detected 202, 104 and 1 miRNA regulators in clusters A, B and C, respectively . The jackknifed P-values perfectly correlated with the original PanomiR P-values . We deployed four strategies to generate lists of predicted miRNA targets. These strategies either used a combination of conserved miRNA families along with prediction scores or solely used various cut-offs for miRNA-mRNA binding prediction scores . Using rP-values . However, in contrast to PanomiR that aims to prioritize transcriptional programs spanning multiple pathways controlled by specific miRNAs, miRPath’s aggregation statistic returns whether the selected miRNA significantly targets at least one of the identified pathways [We compared PanomiR with widely used miRNA–pathway analysis tools including DIANA-miRPath v4.0, MIENTURNET and MITHRIL v2.1 , 24, 33.P-value aggregation’. Specifically, we extended the enrichment analysis to a group (cluster) of pathways by interrogating the number of pathways within a given cluster that were significantly enriched for targets of a miRNA (miRNA hits). This ‘miRNA hits’ approach is similar to the functionalities within miRPathDB v2.0 and miTalos [P-value aggregation’ approach, we used Fisher’s method to obtain one single P-value that combines enrichment P-values of a miRNA within all pathways in a cluster. This approach has been previously implemented in DIANA-miRPath v4.0 for multi-pathway targeting. We also analyzed Stouffer’s P-value aggregation method within clusters to combine miRNA–pathway enrichment P-values as an alternative of Fisher’s method.For a fair comparison using the same background of genes and pathways, we assessed two adaptations of existing miRNA–pathway analysis approaches in the PanomiR’s ecosystem: ‘miRNA hits’ and ‘ miTalos , 27. ForP-value aggregation approaches  and proaches  and S5. proaches . Fisher’ectively . When usectively . It is wectively  and 4 usP-value aggregation methods also showed a strong correlation between the number of miRNA targets and the P-value ranking in cluster A of the TCGA data. These results suggest that current commonly available approaches for multi-pathway targeting are biased toward prioritizing miRNAs with more targets. In comparison, PanomiR did not show a significant correlation between ranking of a miRNA  and the number of its targets  by querying the list of DE genes in the online portal . MIENTURPanomiR is a framework to determine miRNA regulation of multiple coordinately regulated pathways. Most of the existing tools for miRNA–pathway analysis are focused on one-to-one miRNA–pathway relationships and lack the ability to infer relationships between miRNAs and their groups of co-regulated pathways. Approaches to address this problem have used standard enrichment analysis as a basis for interrogation of multi-pathway targeting. However, this approach does not take into account the interaction between pathways and their relative expression dynamics . AlternaThe use of pathway activity profiles is a key component of PanomiR. It sensitively detects differentially regulated pathways and provides granular definition of coordinate functional groups , Table 2PanomiR’s multi-pathway approach provided unbiased detection of miRNA regulatory events in liver cancer that was not detectable by conventional analyses , Table 3PanomiR provides an advancement over the current practice of studying static, isolated miRNA–pathway interactions. It is the first systems biology framework to study multiple differentially regulated pathways, their co-activity and their regulating miRNAs. The model achieves its broad-scale inference by accounting for co-expression of pathways and disease-specific expression dynamics to identify miRNA-regulatory events.PanomiR provides multiple functionalities that are not available through established miRNA–pathway analysis tools. These include pathway dysregulation analysis, identification of groups of co-expressed pathways for miRNA prioritization and granular assignment of disease-associated miRNAs to specific groups of disease pathways. These features enable PanomiR to produce unbiased miRNA prioritization results, which, in turn, sensitively determine key disease miRNAs and their targeted pathways even when there are only few known gene targets or significant differentially expressed genes.Limitations of the PanomiR approach include (1) PanomiR detects one-to-many miRNA–pathway relationships and does not provide the analysis of many-to-one relationships; (2) PanomiR’s infers miRNA from gene expression data. In addition, PanomiR does not provide co-expression analysis between pathways and miRNAs. Additional cross-examination with miRNA expression data may be necessary to make the results more actionable, representing a potential area for future expansion; (3) PanomiR primarily works with bulk RNA-Seq data and is not designed for single-cell/spatial transcriptomic data sets. For users who are interested in single-cell/spatial transcriptomic analysis of miRNA–pathway relationships, we suggest adapting a pseudo-bulk transformation prior to using PanomiR; and 4) the complete landscape of miRNA–mRNA binding relationships is unknown. This gap can drive discrepancy in miRNA prioritization based on different background data sets of miRNA–mRNA interactions (examples in  the compCharacterizing the roles of miRNAs in health and disease requires charting of coordinate co-regulation across heterogeneous molecular cascades and pathways. PanomiR is the first-of-its-kind method aiming to capture the effects of miRNAs in coordinating multi-pathway transcriptional programs.PanomiR enables the detection of differential pathway regulation events between two groups. It uses pathway activity profiling to identify phenotype/group-associated transcriptional programs using a co-expression network of disease-associated pathways.PanomiR achieves sensitive and unbiased prioritization of disease-regulating miRNAs using statistical tests for analyzing one-to-many miRNA–pathway relationships within groups of coordinate disease functions.PanomiR is available as a user-friendly, open-source software package. It enables using a diverse range of clustering algorithms, background pathways and miRNA–mRNA interaction databases.supplementary_material_2023_10_30_final_bbad418Click here for additional data file.supplementary_tables_bbad418Click here for additional data file.


Uveitis is an eye disease with a high rate of blindness, whose pathogenesis is not completely understood. Si-Ni-San (SNS) has been used as a traditional medicine to treat uveitis in China. However, its mechanism of action remains unclear. This study explored the potential mechanisms of SNS in the treatment of uveitis through network pharmacology and bioinformatics.Using R language and Perl software, the active components and predicted targets of SNS, as well as the related gene targets of uveitis, were mined through the Traditional Chinese Medicine Systems Pharmacology, Therapeutic Target, Gene Expression Omnibus, GeneCards, and DrugBank databases. The network diagram of active components and intersection targets was constructed using Cytoscape software and the String database. The CytoNCA plug-in was used to conduct topological analysis on the network diagram and screen out the core compounds and key targets. The genes were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. Chemoffice, Pymol, AutoDock, and Vina were used to analyze the molecular docking of key targets and core compounds of diseases through the PubChem database.JUN, RELA, and MAPK may play important roles in the treatment of uveitis by SNS. Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that core genes were mainly concentrated in MAPK, toll-like receptor, tumor necrosis factor, and nucleotide oligomerization domain-like receptor signaling pathways. In addition, molecular docking results showed that the bioactive compounds  exhibited good binding ability to JUN, RELA, and MAPK.Based on these findings, SNS exhibits multi-component and multi-target synergistic action in the treatment of uveitis, and its mechanism may be related to anti-inflammatory and immune regulation. The use of modified Si-Ni-San (SNS) in the treatment of uveitis has achieved good clinical efficacy.–9 SNS is composed of 4 herbs, Radix Bupleuri (Chaihu), Fructus Aurantii (Zhiqiao), paeony (Shaoyao), and licorice (Gancao) and was first referred to in a treatise on febrile diseases. It has a soothing effect on the liver and gallbladder, regulates Qi activity, and has been highly praised by physicians. However, despite its confirmed therapeutic effect as a TCM, its mechanism of action is poorly understood, the chemical composition is complex, and systematic analysis has been inadequate.The clinical treatment of uveitis in China is mainly symptomatic, using a combination of traditional Chinese and Western medicine. The use of classical Chinese prescriptions often results in a better therapeutic effect and fewer adverse reactions.–13 In this study, the effect of treatment interventions with SNS on uveitis was revealed from different perspectives through data mining using network pharmacology and molecular docking technology. Potential targets and mechanisms of action of SNS in the treatment of uveitis were determined to provide a scientifically based reference for further research and clinical application of this formula. The overall flow chart of this study is shown in Figure Network pharmacology systematically determines the influence of drugs on diseases based on disease–genetic or disease–drug interaction networks to characterize the multi-component, multi-target, and multi-pathway mechanisms of action of TCMs.https://tcmsp-e.com/) with Radix Bupleuri, Fructus Aurantii, paeony, and licorice as keywords. The target active ingredient groups were identified with an oral bioavailability ≥ 30% and drug-like activity ≥ .18 as screening criteria. Next, the active ingredients of Chinese medicines were paired with potential targets individually, according to the TCMSP database. The UniProt database (https://uniprot.org/) was used to standardize the targets, and Homo sapiens was selected as the target of the active ingredients of SNS. Genetic targets of the active components of SNS were obtained using a Perl script (https://www.perl.org/).The active ingredients of SNS were searched from the traditional Chinese medicine systems pharmacology (TCMSP) database (https://db.idrblab.net/ttd/), DrugBank (http://www.drugbank.ca/), and GeneCards (https://www.genecards.org) databases, the keyword was set to “uveitis” and species set to “Homosapiens” to retrieve disease targets. In the Gene Expression Omnibus database (https://www.ncbi. nlm.nih.gov/geo), “uveitis” was searched as a keyword, the species was set as “Homosapiens,” and GeneChip data from the GSE66936, GSE18781, and GPL570-55999 files were used. A Perl script was used to merge data when 2 genes in different datasets were identical. Since there are batch differences in the data from different experimental environments, R  software was used for batch correction of the combined data using the SVA package, and the data were further corrected by the “limma” package with the following filter conditions: |LogFC| ≥ 1.5, P < .05. Uveitis genes were obtained from the GEO database. Subsequently, the “VennDiagram” package in R was used to merge disease-related genes obtained from the above databases, remove repeated targets, and integrate uveitis-related targets.Using the Therapeutic Target (https://string-db.org/), the species was set as “Homosapiens,” the lowest interaction threshold was set as “highest confidence (.9),” and protein-protein interaction (PPI) network information was obtained. Afterward, using Cytoscape 3.8.2, the CytoNCA plug-in was used to carry out a topological analysis of the PPI network, including degree intermediary betweenness centrality value (BC), intermediate degree centrality value (DC), proximity closeness centrality (CC), feature centrality of the eigenvector (EC), local average connectivity (LAC), and network centrality scoring conditions for filtering. The screening was conducted to calculate the value corresponding to the median of nodes. Nodes higher than this value were defined as representing compounds with a significant effect and key targets.Venn analysis was performed on the gene targets of the effective components of SNS and the disease targets of uveitis using the “VennDiagram” package in R. Once the target SNSs in the treatment of uveitis were obtained, Cytoscape 3.8.2 software was used to construct the network maps of active ingredients and intersection targets. The intersection target was inputted into the String database , cellular component, and molecular function (MF) categories was carried out using the R packages “clusterProfiler” and “enrichplot.” The KEGG pathways analysis was carried out at the same time, and the threshold was set at https://Pubchem.Ncbi.Nim.Nih.Gov/) in the structure of the 2D mol format. Using Chemoffice software, the 2D structure was converted to a 3D structure. The Pdb format files of the 3D structures of the core target proteins were downloaded from the RCSB Protein Data Bank (http://www.rcsb.org/), and water molecules and small ligand molecules were removed using Pymol. AutoDock was used for molecular docking based on human protein. Vina and Pymol were used to plot the results with the lowest binding energy for each target.Small molecule ligands were downloaded from the PubChem database  represented the interaction between TCM, active ingredients, and targets, while edges (414) represented the interaction between active ingredients and target diseases  were selected for analysis of their molecular interconnection with 4 key pharmacodynamic components  for SNS treatment of uveitis. The lower the energy of the stable conformation of ligand and receptor binding, the more likely the interaction will occur. In this study, the molecular docking results showed that the binding energy of JUN and quercetin was − 8.6 kcal/mol, that of JUN and luteolin was − 8.8 kcal/mol, and that of JUN and kaempferol was − 8.7 kcal/mol. The binding energy of MAPK1 to quercetin was − 8.4 kcal/mol, that of MAPK1 to luteolin was − 8.6 kcal/mol, and that of MAPK1 to naringin was − 8.4 kcal/mol. The binding energy of NFKB3 to luteolin was − 7.7 kcal/mol, that of NFKB3 to quercetin was − 7.7 kcal/mol, and that of NFKB3 to naringin was − 7.4 kcal/mol  prescription composed of 4 medicinal ingredients that elicit an effective treatment response. SNS functions in immune regulation, sedation, hypnosis, liver protection, and lipid-lowering, anti-inflammatory, and antidepression responses and can be used to treat uveitis.CCL2, RELA, FOS, JUN, IL1A, IL4, CXCL10, IL1B, CXCL8, STAT1, MAPK1, and IFNG. These genes were involved in BPs such as LPS reactions, responses to bacteria-derived molecules, reactive oxygen species biosynthesis, and regulation of steroid metabolism. In addition, studies have shown that MAPK1, RELA, and JUN can effectively control the progression of uveitis.,19,20By analyzing the PPI network of SNS in the treatment of uveitis, we found that the core therapeutic genes were –23 Luteolin has neuroprotective effects by reducing oxidative stress and antioxidant activity. Luteolin also inhibits the LPS response, inhibits the activation of JNK, P38, ERk, NF-κB, and STAT3, and modulates transcription factors such as STAT3, NF-κB, and AP-1 to achieve anti-inflammatory effects.–26 Luteolin enhances the kaempferol-inhibited expression of drug-metabolizing enzymes, and co-treatment with kaempferol and luteolin was previously found to increase the cellular levels of kaempferol without affecting the levels of luteolin. Quercetin can inhibit the activation of P38, ERK1/2, and NF-κB; reduce the expression of cyclooxygenase and lipoxygenase; maintain the stability of mast cells; reduce the production of IL-1β, IL-6, and TNF-α; inhibit Lyn/PLCγ/IP3R-Ca2+, Lyn/ERK1/2, and Lyn/NF-κB signaling pathways; and achieve anti-inflammatory effects.–30 Naringin can reduce the release of IL-1β, IL-6, and IL-18 and inhibit the high expression of NF-κB-P65. Inflammation is inhibited through PI3K/Akt and MAPK/ERK signaling pathways.,32Kaempferol, luteolin, naringin, quercetin, and other main components of TCM docked with the molecule also exhibited anti-inflammatory and immunosuppressive effects. Many herbs in SNS contain kaempferol, which has anti-inflammatory, antioxidant, neuroprotective, and other pharmacological effects.–36 Luteolin regulates signaling pathways associated with TNF, lipids, atherosclerosis, NF-κB, Toll-like receptors, and Nod-like receptors.–39 Quercetin regulates signaling pathways associated with NOD-like receptors, MAPK, and TLR.–42 Naringin regulates signaling pathways associated with NOD-like receptors, MAPK, and TLR.,44 According to our PPI network and KEGG enrichment analysis, kaempferol was associated with the most regulatory pathways and the most potential targets, indicating that the pharmacological results of the SNS network are consistent with the clinical treatment results of uveitis.According to the KEGG enrichment analysis, the important pathways involving immune regulation in the SNS treatment of uveitis include the TLR and NOD-like receptor signaling pathways and Th17 cell differentiation. Pathways involved in the inflammatory response include IL-17, TNF, MAPK, and NF-κB signaling pathways, the AGE-RAGE signaling pathway with diabetic complications, and cytokine–cytokine receptor interaction. Kaempferol regulates signaling pathways associated with TNF, Toll-like receptor, NF-κB, NOD-like receptor, rheumatoid arthritis, IL-17, MAPK, lipids, and atherosclerosis.,46 In addition, TLR enrichment and IL-17 signaling pathways have been extensively studied,47–49 TLR protein expression was activated, and CXCL1 and CXCL2 proteins were significantly increased in both time- and dose-dependent manners. The enhanced activity of the IL-17 pathway is associated with the proliferation and pathogenicity of Th17 cells, and evidence from animal models has suggested that the development of pathogenic Th17 cells is responsible for experimental autoimmune uveitis. The helper T cells of IL-17, specifically Th1 and Th17 cells, can promote the proliferation of Tregs and T cells, which are most abundant in MAPK and TNF signaling pathways. They regulate the immune response and the expression of inflammatory factors caused by uveitis. The main components observed in the present study also intervene in inflammatory and immune-related pathways.,43,50–53 Kaempferol anti-inflammatory effect is mediated by the inhibition of MAPK-related extracellular signal-regulated kinase and the P38 signaling pathways. Kaempferol inhibits TNF-α-induced MAPK activation without affecting TNF-α receptor expression. Luteolin inhibits IL-1β, IL-17, and TNF-α and regulates NF-κB, JAK-STAT, and TLR signaling pathways. It also inhibits the activation of NLRP3 inflammasomes and promotes the differentiation of macrophages into the M2 phenotype to achieve the anti-inflammatory effect. Quercetin ameliorated neuro-inflammation in mice by modulating the NLRP3 pathway in NOD-like receptor signaling, which was demonstrated by reducing IL-1β and IL-18 levels. Naringin inhibits NF-κB activation and the NOD-like receptor family in NLRP3 inflammasomes. The key targets and pathways screened in the current study are potential targets and pathways for the therapeutic efficacy of uveitis. We also confirmed the characteristics of multiple components acting on different targets through various channels to provide auxiliary treatment ideas for SNS in the clinical treatment of uveitis.The inflammatory immune response is an important factor in uveitis. According to GO and KEGG enrichment analyses, the potential target of the main components of SNS in the treatment of uveitis is inflammatory immune-related pathways. Among them, the MAPK signaling pathway, which was associated with abundant targets in the current study, is an important target pathway. Studies have shown that the T-cell receptor signaling pathway is involved in immune defense through the p38 MAPK signaling pathway.In summary, we explored the potential mechanisms of SNS in the treatment of uveitis based on network pharmacology and molecular docking. The potential functional components kaempferol, luteolin, quercetin, and naringin were identified. Cumulatively, the components may regulate BPs such as immune and inflammatory responses and atherosclerosis through molecular docking targets and enriched pathways. These experiments provide a reference of the therapeutic effects of SNS on uveitis in the absence of clinical trials. In future research, we will provide biological experimental evidence for the therapeutic effects of SNS on uveitis according to the results of this study.www.editage.cn) for English language editing.We would like to thank Editage (Conceptualization: Ning Zhang.Data curation: Ning Zhang, Ruisu Zhang, Qian Zhang, Jing Yao.Formal analysis: Ning Zhang.Methodology: Dandan Zhang, Liu Hong, Ruisu Zhang.Software: Dandan Zhang, Qian Zhang.Writing – original draft: Ning Zhang, Dandan Zhang, Liu Hong.Writing – review & editing: Ning Zhang, Dandan Zhang, Jiadi Wang.