CDK2, EGFR, MMP9 and PTGS2 as hub targets. The TCM-compound-target network and compound-target-pathway network together revealed that quercetin, diosmetin, enhydrin and luteolin were the main components of LSYQ Decoction. Finally, molecular docking showed the affinity between the key compounds and the hub target proteins to verify the accuracy of drug target prediction in the first place. The present study deciphered the core components and targets of LSYQ Decoction on the treatment of BCa in a comprehensive systemic pharmacological manner.Our goal was to explore the bioactive constituents of Longsheyangquan (LSYQ) Decoction and elucidate its mechanisms on the treatment of bladder cancer (BCa). A total of 38 compounds were selected based on their pharmacokinetic properties in three large traditional Chinese medicine (TCM) databases. 654 putative targets of LSYQ Decoction were predicted using a structure-based, reverse-docking algorithm online, of which 343 overlapped with BCa-related protein-coding genes. The protein-protein interaction (PPI) network was constructed to perform module analysis for further Gene Ontology (GO) annotations and Kyoto Encyclopedia Genes and Genomes (KEGG) pathway enrichment analysis, which identified  However, approximately 30% of non-muscle invasive bladder cancers would gradually progress into muscle-invasive ones during the course of the disease  Decoction whose composition is primarily M theory . While sNetwork pharmacology, first proposed by Dr. Hopkins, is an efficient tool for systemic pharmacological research in which the interactions among target genes, pathways, diseases, and drugs are studied in detail using a network-based method to unravel the mechanisms of complex preparations at multiple levels . Due to in-silico approach, as neither had been reported before. Initially, we screened out and selected the putative bioactive components of LSYQ Decoction. Then, we acquired the overlap between the drug targets and BCa-related target genes for further GO and KEGG enrichment analyses. Meanwhile, we visualized the TCM-compound-target network as well as the compound-target-pathway network to provide a general overview of the potential anti-tumor properties and molecular mechanisms of LSYQ Decoction. Finally, we employed molecular docking to confirm the affinity between the core components and the hub targets. The current study not only elucidated the possible mechanisms of LSYQ Decoction on the treatment of BCa, but also serve to promote more research into TCM in order to discover highly effective herbal medications with mild side effects and relatively low research and development investment.Herein, we aimed to comprehensively investigate LSYQ Decoction as well as its influence on BCa using an A systemic scheme of the pharmacological network for the present study is illustrated in https://tcmsp-e.com/tcmsp.php) was utilized in search of chemical constituents of LSYQ Decoction, which is composed of Solanum Nigrum Linn., Duchesnea Indica Focke, Herba Solani Lyrati, Spora Lygodii, Smilacis Glabrae Rhizoma and Juncus effusus L. The database integrates TCMs, chemical components and their pharmacokinetic properties, including oral bioavailability (OB), gastrointestinal absorption, blood-brain barrier permeability, drug-likeness (DL) etc. The active compounds were selected by criteria as follows: OB ≥ 30%, DL ≥ 0.18, molecular weight ≤500 Dal, hydrogen bond donors ≤5, hydrogen bond acceptors ≤10 and AlogP ≤5, with the last four corresponding to the “Rule of Five” first raised by Lipinski in 1997 (https://www.cnki.net/) for more possible constituents, especially in the case of Duchesnea Indica Focke whose components were re-input into the TCMSP to obtain pharmacokinetic properties for next-step selection. Additional compounds were input into SwissADME (http://www.swissadme.ch/)  online database ( in 1997 . We thendme.ch/) for pharmhttp://www.swisstargetprediction.ch/)  , which dCytoscape software (version 3.8.0) was used to vividly display the relationship between TCMs, potentially-bioactive compounds and targets associated with the compounds.in situ”. Targets were retrieved from GeneCards (https://www.genecards.org/), Therapeutic Target Database  and GenCLiP 3 . For GeneCards, only targets with relevance scores greater than the median were selected. The universal Protein Resources  was subsequently used to distinguish protein-coding target genes from non-coding ones.Human BCa-related targets were searched using the following terms: “bladder cancer” AND “bladder carcinoma” AND “malignant neoplasm of bladder” AND “bladder carcinoma http://jvenn.toulouse.inra.fr/app/index.html). An original PPI network involving candidate targets was created by the Search Tool for the Retrieval of Interacting Genes  in order to explore the relationship between the imported protein-coding genes. Afterwards, the original PPI network was analyzed by Cytoscape and its CytoNCA plugin. Nodes whose Betweenness, Closeness and Degree were less than the corresponding medians were filtered out. Furthermore, the Molecular Complex Detection (MCODE) plugin helped us to select and visualize significant modules with criteria as follows: degree cut off = 2, node score cut off = 0.2, K-core = 2 and maximum depth = 100. Modules with score ≥10 were included for further analyses.Overlapping targets of LSYQ Decoction and BCa were acquired and considered as candidate targets by means of a Venn diagram via an online plotting resource (https://metascape.org/gp/index.html#/main/step1) was applied to perform GO and KEGG enrichment analyses for two clusters with adjusted p values <0.01 as the cut-off.Gene ontology analysis is a widely-accepted approach to define and describe genes across species from three aspects, namely molecular function (MF), biological process (BP), and cellular component (CC). KEGG is in fact an enormous collection of databases involving genomes, drugs, enzymes, biological pathways etc. Herein, the Metascape , while the crystal structures of human proteins encoded by the hub genes were obtained from the RCSB Protein Data Bank . The protein structures were then pre-processed by removing the irrelevant water molecules and ligands, adding hydrogen atoms, and merging non-polar hydrogen atoms. After adding hydrogen atoms for the compounds, protein-compound docking was conducted with the method of Lamarckian genetic algorithm in Autodock Tools (version 1.5.6). The number of runs for each docking was set to 50 as officially recommended, in order to gain the best affinity , the conformation of which was then visualized in PyMOL software (version 2.2.0).Molecular docking could intuitively display the interaction between the compounds and their potential target proteins with the help of Autodock (version 4.2.6). The 3D structures of the core compounds were downloaded from PubChem  ≤ −2 as the cut-off. For Cluster One, the BP analysis revealed that the hub genes were enriched in cellular response to organonitrogen compound, response to reactive oxygen species, positive regulation of cell migration and so on , Epidermal growth factor receptor (EGFR), Matrix metalloproteinase 9 (MMP9) and Prostaglandin-endoperoxide synthase 2 (PTGS2) were among the highest, suggesting their roles as hub targets.Meanwhile, a network concerning the top 25 KEGG pathways in Cluster One was drawn along with the corresponding targets and drug compounds to accurately illustrate the underlying regulatory mechanisms of LSYQ Decoction for BCa . AccordiTo verify the accuracy of SwissTargetPrediction and to ascertain whether key molecules in LSYQ Decoction could directly bind to their protein targets, a widely-accepted molecular docking approach was performed via Autodock. Four typical critical compounds  attempted to dock with four hub target proteins . The lower the binding energy, the stronger the bond. The lowest binding energy, in other words, the best affinity, of 16 dockings were shown in a heatmap . BindingBladder cancer is the most common malignancy of the urinary system with painless gross hematuria as the cardinal symptom; however, patients may be asymptomatic in the early stage, leading to delayed diagnosis and eventual progression of the disease . While eSolanum Nigrum Linn., a herbal plant rich in flavonoids, is widely adopted in TCM for its antineoplastic effect  expression -6 in macrophages and inducing Th1 responses mediated by IL -2 and interferon-γ  could target TP53, EGFR, IL1B and NOS3 proteins for the treatment of laryngeal squamous cell carcinoma (Smilacis Glabrae Rhizoma, a member of the Smilacaceae family native to Southeast Asia, has long been utilized to treat sore throat and syphilis. It also exhibited an anti-tumor effect in a dose-dependent manner in human breast cancer cell line MCF7, colon carcinoma cell line HT -29, and gastric cancer cell line BGC-823 by inducing mitochondrial apoptosis (Juncus effusus L. has been attributed with a variety of pharmacological functions, including anti-inflammatory, anti-allergic, anti-cancer and others (Juncus effusus L., effectively induced endoplasmic reticulum stress, resulting in decreased proliferation of gastric cancer cells (From the perspective of TCM, the bladder is an organ responsible for eliminating the “damp evil” that arises daily in the human body. Should the bladder fail to eliminate it, it would accumulate and transform into a combination of damp and heat evils, eventually causing the cancer together, during which time the two evils could probably block neighboring vessels. This is how hematuria comes into being, according to TCM theory. The botanical medicine in LSYQ Decoction serve precisely the TCM principle of clearing the heat and prompting diuresis. c effect . Previoupression , while Srferon-γ . Extractus cells . As one arcinoma . Smilacipoptosis . Juncus d others . For exaer cells . Neverthvia a Venn diagram. The hub target genes, namely CDK2, EGFR, MMP9 and PTGS2, were then determined through the visualization of the PPI network and module analysis. Meanwhile, quercetin, diosmetin, enhydrin, as well as luteolin were regarded as core constituents in LSYQ Decoction through topological mining. The target genes in both of the two modules, whose cluster scores were greater than 10, have accumulated in significant pathways related to cancers, such as the PI3K-Akt signaling pathway, the ErbB signaling pathway, the VEGF signaling pathway, the MAPK signaling pathway, and the HIF-1 signaling pathway.Considering the composition diversity of this formula, we made use of network pharmacology to systemically explore it at herbal, molecular, and genetic levels in the present study. Upon strict screening, 38 active compounds were finally identified, among which 654 human proteins were predicted as potential targets. It again proved that TCM formulas possess the characteristics of multi-drug and multi-target. The TCM targets are intersected with BCa-related protein-coding genes MMP9 and PTGS2 were among the highest, suggesting their roles as hub targets. EGFR overexpression is found in various human malignancies (in-silico tool to preliminarily check the structural fitness between target proteins and small molecules regardless whether the latter were newly synthesized or repurposed for a different disease (The anti-tumor activity of flavonoids has been extensively described and widely accepted . Quercetgnancies , includignancies , and a fgnancies , making gnancies . Higher gnancies . PTGS2, gnancies . What’s gnancies . In addignancies . Molecul disease .Our study still leaves some room for improvement. First, both the components of LSYQ Decoction and BCa-related genes were retrieved from databases. We tried our best to search for all of them and set a rational threshold for filtration. Second, the targets of LSYQ Decoction were predicted by a reverse-docking algorithm at SwissPredictionTarget. We cross-validated them by molecular docking. Last but not least, the mechanisms discovered by systemic network pharmacology are still not complete and need further experimental exploration and verification.


To explain the potential mechanisms of Drynariae Rhizoma (DR) in the treatment of low back pain (LBP).  Network pharmacology was used to reveal the potential mechanisms including collecting the active ingredients of DR, analyzing the common gene targets of LBP and DR, constructing protein-protein interaction (PPI) network, collecting protein classification, performing Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and verifying significant gene targets.  234 different gene targets and 18 active compounds altogether were obtained. AKT1, VEGFA, and HIF1A were deemed to be major gene targets based on the degree values. According to GO analysis, steroid metabolic process involved 42 (18.10%) potential therapeutic LBP targets, neuronal cell body involved 24 (10.30%) potential therapeutic LBP targets, and protein serine/threonine kinase activity involved 28 (12.02%) potential therapeutic LBP targets in biological process (BP), cellular component (CC), and molecular function (MF), respectively. According to KEGG and pathway interaction analyses, the PI3K-Akt signaling pathway involved 34 (15.89%) potential therapeutic LBP targets, and PI3K-Akt signaling pathway played a significant role in the treatment of LBP. The mRNA expression levels of AKT1 and HIF1A were upregulated in healthy nucleus pulposus (NP) tissue than in degenerative NP tissue. In contrast, the mRNA expression level of VEGFA was downregulated in healthy NP tissue than in degenerative NP tissue.  In this study, we identified a potential relationship between LBP and DR in this work, as well as a synergistic mechanism of DR in the treatment of LBP, which serves as a benchmark for further in vivo and in vitro research. Low back pain (LBP) is a very common and high-impact health problem all over the world. The lifetime prevalence of LBP is 84%, the prevalence of chronic LBP is approximately 23%, and 11% to 12% of people are disabled due to LBP . LBP is α and MMP-3 and raising the expression of collagen II and aggrecan. This substance may also reduce local inflammation, which would slow intervertebral disc degeneration. The research also indicated that naringin may become an alternative therapeutic agent for pain associated with disc degeneration such as NP and LBP [Traditional Chinese medicine (TCM) is based on the fundamental theory of balance between yin and yang and five basic elements . TCM has and LBP . With th and LBP . NonetheNetwork pharmacology has played a significant role in modern TCM research, which has provided powerful theoretical evidence for the discovery of new therapeutic targets of TCM . Our stuWe screened for the active ingredients of Drynariae Rhizoma by searching for the database of traditional Chinese medicine system and analyzing platforms (TCMSP)  on the bWe downloaded the chemical structures of the active ingredients of DR from TCMSP or PubChem  and storhttp://bioinfogp.cnb.csic.es/tools/venny/index.html) to obtain the common gene targets of LBP and DR.There were several databases being used to search for the gene targets associated with LBP, including Genecards database , DisGeNEThe Cytoscape software (version 3.7.1) was used to construct the ingredient-target network of DR with the utilization of the obtained common gene targets. The Cytoscape software is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles, and other state data .http://string-db.org/, version 11.0) is based on completing a comprehensive and objective global network. Many available sources of protein-protein interaction information have been collected and integrated into the STRING database. The latest version (11.0) of the STRING database covers more than 5000 organisms [We used the STRING database to construct protein-protein interaction of the common gene targets of LBP and DR. The STRING database (rganisms . The spehttp://metascape.org). Metascape is a database providing a comprehensive gene list annotation and analysis resource. The database contains functional enrichment, interactome analysis, and gene annotation [P value was set to 0.01, and the min enrichment was set to 5. In particular, we selected GO biological processes (BP), GO cellular components (CC), GO molecular functions (MF), and KEGG pathway. Finally, we clicked the enrichment analysis and downloaded the data of GO and KEGG pathways. The GraphPad Prism 7.0 software was used to process the data for GO analysis, and an online analysis tool (http://www.aipufu.com/index.html) was used to process the data for KEGG pathways into bubble charts.To analyze the common gene targets of LBP and DR, we made use of Gene Ontology (GO) functional analysis and identified the important signaling pathway through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The processes above were performed with the help of the Metascape database (notation . The proWe obtained nucleus pulposus (NP) tissues from two patients with low back pain. According to the Pfirrmann classification score of magnetic resonance imaging, relatively healthy NP tissue was grades I~II, and degenerated NP tissue was grades III~V , 23. TheΔΔCt− way to count these relative expression standards.We extracted RNA from nucleus pulposus tissue as well as cells using TRIzol reagent  following the manufacturer's instructions. We apply PrimeScript RT Master Mix  to obtain first-strand cDNA of whole RNA, and for qPCR detection, we used One-Step SYBR PrimeScript RT-PCR Kit (Takara Bio). The primer sequences were designed as follows: AKT1: forward: 5′-TGGACTACCTGCACTCGGAGAA-3′, reverse: 5′-GTGCCGCAAAAGGTCTTCATGG-3′; VEGFA: forward: 5′-TTGCCTTGCTGCTCTACCTCCA-3′, reverse: 5′-GATGGCAGTAGCTGCGCTGATA-3′; H1F1A: forward: 5′-TATGAGCCAGAAGAACTTTTAGGC-3′, reverse: 5′-CACCTCTTTTGGCAAGCATCCTG-3′; and GAPDH: forward: 5′-TCCACTGGCGTCTTCACC-3′, reverse: 5′-GGCAGAGATGATGACCCTTTT-3′. We used 2t-test was applied to analyze the two sets of parameters. Statistical significance was P < 0.05.Data are presented as mean ± standard deviation (SD). Statistical analysis was performed by the SPSS 26.0  and GraphPad Prism 7.0 software. Each experiment was performed at least three times. Multiple group outcomes were tested by one-way analysis of variance (ANOVA) test followed by Tukey's post hoc test. The Student's We obtained 71 ingredients of DR and 18 active ingredients from TCMSP. After excluding the invalid ingredients including (+)-catechin, (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one,22-stigmasten-3-one, and beta-sitosterol_qt, we finally selected 18 active ingredients of DR including (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one, aureusidin, eriodyctiol (flavanone), stigmasterol, beta-sitosterol, kaempferol, naringenin, (+)-catechin, eriodictyol, digallate, luteolin, 22-stigmasten-3-one, Cyclolaudenol acetate, cycloartenone, cyclolaudenol, davallioside A_qt, marioside_qt, and xanthogalenol. The basic information of 18 active ingredients is shown in We obtained 264 gene targets associated with DR and 8409 gene targets associated with LBP after excluding invalid and duplicate gene targets. A total of 233 common gene targets are shown in The Cytoscape software was used to construct the ingredient-target network as shown in The network of PPI analyzed 231 common protein targets as shown in P < 0.05), we obtained a total of 2947 enriched results, including 2478 results of biological processes (BP), 111 results of cellular components (CC), and 221 results of molecular functions (MF). We selected the top several enrichment results as shown in After GO functional analysis of the common gene targets of DR and LBP through the Metascape database (P < 0.05), we obtained a total of 136 enriched results. We selected the top 10 enrichment results as shown in After KEGG pathway enrichment analysis of the common gene targets of DR and LBP through the Metascape database  induced neuropathic pain, which is mediated by enhancing the expression and colocalization of VEGFA, p-AKT, and TRPV1 in a SNI-induced neuropathic pain model, which also improves expression of VEGFA, VEGFR2, p-AKT and TRPV1 in the spinal cord –29. PaskWe performed GO functional analysis to analyze the common genes of LBP and DR. According to analysis results, we found that these common genes have multiple functions in BP, CC, and MF. In the BP, the steroid metabolic process involved 42 (18.10%) gene targets, while organic anion transport involved 39 (16.81%) gene targets, promoting cell proliferation in the treatment of LBP. A study has proposed that epidural steroid injections are one of the most common nonsurgical treatments for low back pain. In general, corticosteroid treatment often results in bone loss and osteoporosis. Neuronal cell body involved 24 (10.30%) gene targets, nuclear envelope and vesicle lumen involved 17 (7.30%) gene targets, and cytoplasmic vesicle lumen involved 16 (6.87%) gene targets in the CC, which reveals that these gene targets may make effects through plasma membrane. Protein serine/threonine kinase activity involved 28 (12.02%) gene targets, protein tyrosine kinase activity involved 21 (9.01%) gene targets, and steroid hormone receptor activity involved 19 (8.15%) gene targets in the MF, which reveals that ribonucleotide binding may play a significant role in the regulation of gene targets for DR.We summarized the pathway enrichment analysis through the KEGG database to clarify the mechanism between DR and LBP. PI3K-Akt signaling pathway involved 34 (15.89%) gene targets, microRNAs in cancer and proteoglycans in cancer; Ras signaling pathway involved 23 (10.75%) gene targets; neuroactive ligand-receptor interaction involved 22 (10.28%) gene targets; prostate cancer, Rap1 signaling pathway, Alzheimer disease, and MAPK signaling pathway involved 20 (9.35%) gene targets and so on. In these pathways, PI3K-Akt signaling pathway and Ras signaling pathway play a significant part in the development of LBP. Xu et al. found that the activation of PI3K-Akt signaling pathway is associated with high expression of inflammatory-related factors in intervertebral disc herniation . RadiculIn conclusion, we used network pharmacology to indicate the potential association between DR and LBP and synergistic mechanism of DR in the treatment of LBP through prediction of gene and protein targets, which provides a reference for future study in vivo and in vitro.


Overall, the integrated analysis identified the molecular signature associated with AD. Treatment with anti-oxidants, B2, B6, and pantothenate in genetically susceptible individuals in the pre-symptomatic stage might help in better management of the disease.Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid plaques implicated in neuronal death. Genetics, age, and sex are the risk factors attributed to AD. Though omics studies have helped to identify pathways associated with AD, an integrated systems analysis with the available data could help to understand mechanisms, potential biomarkers, and therapeutic targets. Analysis of transcriptomic data sets from the GEO database, and proteomic and metabolomic data sets from literature was performed to identify deregulated pathways and commonality analysis identified overlapping pathways among the data sets. The deregulated pathways included those of neurotransmitter synapses, oxidative stress, inflammation, vitamins, complement, and coagulation pathways. Cell type analysis of GEO data sets showed microglia, endothelial, myeloid, and lymphoid cells are affected. Microglia are associated with inflammation and pruning of synapses with implications for memory and cognition. Analysis of the protein-cofactor network of B The major risk factors of AD include age, sex, and genetic variants2. Rare coding variants in APP(Amyloid precursor protein), PSEN1 (Presenilin 1), and PSEN2 (Presenilin 2) increase the risk for late-onset of AD3. Genetic variants of SLC9C1, CSN1S1, and LOXL4 though recessive, delay the age of onset of AD4. The ε4 allele of apolipoprotein E (ApoE), involved in cholesterol metabolism, which is secreted by astrocytes is strongly associated with the late onset of AD5. ApoE isoforms have a role in controlling brain glucose and lipid metabolism, neuronal signaling, inflammation, and mitochondrial function and regulate Aβ aggregation and clearance7. ApoE4 is associated with gene expression changes in all cell types of the human brain and aberrant deposition of cholesterol in oligodendrocytes leads to reduced myelination8. ApoE-expressing microglia promote neurodegeneration by disrupting the phagocytosis of Aβ aggregates9. Mutations in APP, PSEN1 & 2, ADAM10, and ADAM1J cause the formation of Aβ plaques and are sufficient to cause the whole biochemical and morphological characteristic of AD2. However, studies have also shown that the toxicity is caused due to soluble monomers or oligomers10. AD is associated with changes in brain volume, and atrophy in the hippocampus and entorhinal cortex at the early stages of disease progression11. The disease manifestations include motor, behavioral, cognitive, and memory dysfunction5. Current medications are restricted to the management of symptoms and disease-modifying therapies with antibodies such as aducanumab and lecanumab is shown to reduce the amyloid plaques in AD patients which influence the course of disease13. Hence understanding the factors that contribute to amyloid formation will help to achieve mechanistic insights and also help to identify potential biomarkers and therapeutic targets which will aid in the better management of the disease14. Studies have shown impaired energy metabolism in AD which could act both as biomarkers and therapeutic target in disease15. Previous studies have also shown that brain volume and metabolic changes precede the onset of symptoms in neurodegenerative diseases like AD and Huntington’s disease (HD)17.Alzheimer’s disease (AD) is a progressive neurological disorder characterized by the formation of amyloid plaques, tau tangles, and synaptic and neuronal dysfunction19. Studies have reported the presence of Aβ in the cells and mitochondria. In mitochondria, Aβ binds to the heme group leading to the production of free radicals, thus causing oxidative damage and cell death20. Bioenergetic deficits and oxidative stress are the key contributors to cognitive decline in AD21. The mitochondrial enzymes  that are important for energy metabolism is deficient in AD brains22.Mitochondrial impairment, cellular energy deficiencies, and oxidative damage are found to be important in the pathogenesis of AD23. Glycolysis was found to be upregulated in AD24. Levels of lactate and lactate transporters are altered in AD mouse models resulting in deregulated astrocyte-neuron lactate shuttle25. AD brain was shown to exhibit hypometabolism decades before the manifestation of symptoms suggesting the role of metabolic dysfunction in AD26. Fluorodeoxyglucose-positron emission tomography (FDG-PET) studies have shown a reduction in glucose metabolism at the mild cognitive impairment (MCI) stage of AD progression27. Magnetic resonance imaging studies have shown decreased levels of glutamate in the AD brain which is crucial for the functioning of the central nervous system (CNS). The astrocyte-neuron glutamine-glutamate shuttle was also found to be affected in AD28. A decrease in metabolite levels of N-acetyl aspartate/Creatinine (NAA/Cr) ratio is also observed in AD29. The data from the literature are suggestive of metabolic pathways as potential modifiers of AD. Consistent with metabolic deregulation, changes in the levels of various vitamins like B2, B5, B6, B12 and B9 are reported in AD33. The vitamins act as cofactors in various enzymatic reactions and low levels of these vitamins are associated with many diseases including neurodegenerative diseases34. For many genetic diseases with non-synonymous single nucleotide polymorphisms (SNPs) in the genes encoding enzymes, vitamin/cofactor supplementation is used as the standard of care treatment35. Previous studies have shown that supplementation of B2, B5 and B6 was shown to improve symptoms in AD model system38. The vitamins might potentially act by helping to achieve metabolic homeostasis though this remains to be answered39. Previous studies have shown that metabolic deregulation precedes onset of symptoms in AD40.Consistent with mitochondrial dysfunction, AD is associated with metabolic remodeling41. Transcriptomics profiling of late-onset Alzheimer’s affected brain tissue revealed pathways associated with neural communication, cerebral vasculature, and amyloid-beta clearance are deregulated42. Blood transcriptomic analysis has shown an association of AD at different stages with type 2 diabetes43. Single-cell transcriptomics analysis revealed myelination-related processes were deregulated in multiple cell types like neurons, astrocytes, oligodendrocytes and microglia44. An individual’s metabolome reflects the culmination of the cumulative alterations in genomics, transcriptomic and proteomic profile. In addition, it is also influenced by the environment and lifestyle of an individual45. Metabolomic analysis of different kinds of samples of AD patients also indicates deregulation of the Krebs cycle, mitochondrial function, neurotransmitter and amino acid metabolism, and lipid biosynthesis pathways48. Multi-omic analysis and validation has helped to understand mechanisms, biomarkers and potential therapeutic targets in osteonecrosis, rheumatoid arthritis, glaucoma51.Earlier omics analysis carried out on the samples obtained from AD patients and model systems has shown the associated deregulated pathways with diseases2, B6, and pantothenate pathways. The cell type analysis showed endothelial, microglial, myeloid, and lymphoid cells. Since vitamins act as cofactors, their implications for enzyme activity associated with the deregulation of metabolic pathways have been analysed using a cofactor-protein interaction network using Cytoscape. The results are discussed in light of the implications of deregulated pathways for disease, symptoms and therapeutic targets.Although the multi-omics approach enabled the comprehensive understanding of deregulated pathways associated with the disease. An integrated systems analysis using the available multi-omics data sets can potentially shed light on the interactions of the different molecular processes with further implications for the disease and could also help identify markers, early prognosis, and new therapeutic targets. In the present study an integrative analysis of transcriptomics, proteomics, and metabolomics of blood, brain, and cerebrospinal fluid samples obtained from patient datasets was performed. In addition, proteomics datasets of the brain and blood, and metabolomic datasets of the brain, blood, and cerebrospinal fluid of mice were analysed. The overall analysis from this approach shows the deregulation of inflammation, complement and coagulation, signalling, and metabolic pathways as well as vitamin B52, GSE3698053, GSE4477054, GSE4835055, and GSE14082956 from Humans were used for analysis. All the data sets were from the brain except for GSE140829 which is from the peripheral blood data set. The data sets were analysed using Enrichr58 as given in methods and genes are binned into pathways. To corroborate the gene expression profile to different cell types that are associated with the disease, we carried out cell type analysis using Enrichr as given in the methods. The kinases and transcription factors were predicted using X2K59 on all the GEO datasets as described in the methods. The significant differential genes with an adjusted p-value of 0.05 were used for all analyses. Pathway and transcription factor analysis was performed for up-regulated and downregulated genes separately and provided in Supplementary file The GEO (Gene Expression Omnibus) data sets GSE5281Represent the pooled data sets from the brain regions: Entorhinal cortex, hippocampus, medial temporal gyrus, posterior cingulate, superior frontal gyrus, and Primary visual cortex. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways include pathways involved in the protein life cycle, neurodegenerative diseases like spinocerebellar ataxia, amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), citric acid cycle, and pathways involved in infection Fig. . PathwayDataset represents the pooled datasets from brain regions frontal cortex, temporal cortex, and hippocampus. Differentially expressed genes are binned into pathways. The top significant pathways involved Synapse based pathways like Synaptic vesicle cycle, Dopaminergic synapse, neurodegenerative diseases such as , proteasome, endocytosis, and signaling pathways Fig. . PathwayDataset represents the pooled datasets from brain regions dorsolateral prefrontal cortex, visual cortex, and cerebellum. Differentially expressed genes are binned into pathways. The top significant pathways involved signaling pathways like retrograde endocannabinoid signalling, AGE-RAGE signaling pathway in diabetic complications, C-type lectin receptor signaling pathway, long-term potentiation, axon guidance, gastric acid secretion, citric acid cycle, pathways involved in infection, and DNA repair Fig. . PathwayRepresent the pooled data sets from the brain regions Entorhinal cortex, hippocampus, superior frontal gyrus, and post-central gyrus. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways involved riboflavin metabolism, neurodegenerative diseases such as Huntington’s disease, Amyotrophic lateral sclerosis, Parkinson’s disease, pathways of neurodegeneration, prion disease, and proteasome, endocytosis, and pathways involved in infection Fig. . PathwayDataset was obtained from peripheral blood. Analysis of the differentially expressed significant genes using Enrichr binned them into pathways. The top significant pathways involved infection-based pathways like salmonella infection, Yersinia infection, Shigellosis, pathogenic Escherichia coli infection, signaling pathways such as NOD-like receptor signaling pathway, carbohydrate metabolism pathways like pentose phosphate pathway, and osteoclast differentiation Fig. . PathwayFurther, we studied if the pathways obtained from different data sets across populations, samples, and study settings exhibit a common trend. The overlapping pathways include retrograde endocannabinoid signaling and salmonella infection are found in 4 datasets and Alzheimer’s disease, TCA cycle, endocytosis, pathways of neurodegeneration and proteasome occurred in 3 datasets while dopaminergic synapse and axon guidance are found in two of the datasets Fig. e.For understanding common transcription factors, we looked at the overlap of transcription factors from 5 datasets. Our study showed that 6 transcription factors are common among the different transcriptomic datasets. These transcription factors include CREB1, MYC, RCOR1, TCF3, UBTF, and ZNF384. Functional annotation of these TFs shows that they belong to signaling pathways, pathways related to infection, and protein synthesis pathways Fig. f.Further, we studied the kinases that are common among the different transcriptomic data sets. Analysis using the top 15 kinases from the 5 transcriptomic data sets conjured 10 kinases to be common among them. These kinases include AKT1, CDC2, CDK1, CDK2, CDK4, CK2Alpha, CSNK2A1, GSK3B, MAPK1, and MAPK14. Functional annotation of these kinases shows that they largely fall into those regulating cell proliferation, inflammation, etc. However, commonality analysis of kinases among the 5 kinases data sets using all the significant kinases conjured 69 kinases , and Vitamin B6 metabolism , as well as unsaturated fatty acids and arginine biosynthesis, are deregulated  as well as arginine metabolism , and succinate dehydrogenase complex iron sulphur subunit B (SDHB) that are involved in the citric acid cycle and oxidative phosphorylation  an L-amino acid oxidase, and monoamine oxidases (MAOA and MAOB) are upregulated while SDHA and SDHB are downregulated  in the dopaminergic synapse, Glutamate decarboxylases  in GABAergic synapse, ABAT and BCAT1 in valine, leucine, and isoleucine degradation pathway are down-regulated. The enzymes Glutamine synthetase (GLUL) in glutamatergic synapse and GABAergic synapse is upregulated while the BCAT2 is upregulated in the valine, leucine, and isoleucine degradation pathway  is an important substrate in CoA biosynthesis. The levels of CoA decide the availability of acetyl CoA for the biosynthesis of acetylcholine76. Our analysis clearly captures the role of B6 and pantothenate in the deregulation of neuronal signalling and function. In addition, proteomic and metabolomic analysis shows deregulation of cysteine and methionine metabolism. Methionine is an important component involved in the biosynthesis of choline32. In the methionine cycle the by-product homocysteine is either converted into methionine in a pathway involving B12 or folate or into cysteine through the trans-sulphuration pathway involving B630. Cysteine is an important substrate used in the biosynthesis of cellular antioxidants like glutathione, taurine, coenzyme A and lanthionine77. Reduced B6 will lead to reduced levels of cysteine. Consistent with these observations, glutathione pathway was found to be deregulated in proteomic analysis. Elevated oxidative stress is associated with AD, which contributes to disease progression80.Our integrated multi-omics analysis showed pathways related to neuronal signalling. The deregulated pathways include dopaminergic, cholinergic synapses, nicotine, and morphine addiction, and choline metabolism in cancer which are all downregulated. We also find that the B2) metabolism. Previous studies have shown that B2 metabolism is affected in AD37. Using a yeast model it was demonstrated that riboflavin improved mitochondrial function and reduced amyloid load in AD81. To further delineate the role of vitamins/cofactors like riboflavin, B6, and pantothenate in metabolic deregulation and AD we generated a vitamin/cofactor-protein interaction network. The interacting proteins were further binned into pathways. The pathways so obtained were then analysed for overlap with deregulated pathways obtained from the multi-omics analysis of AD data sets. Considerable concordance in pathways was observed between the two data sets. Taken together, our results show good concordance between neuronal dysfunction with metabolic pathways. In particular, our results show a potential role for riboflavin (B2), B6, and pantothenate in the disease process.Our analysis also showed impaired riboflavin  of genes in complex III and IV lead to clearance of protein aggregates93. In addition, the inflammatory response also leads to elevated levels of ROS. ROS is shown to mediate signalling processes leading to the expression of inflammatory cytokines95. Scavenging of ROS is shown to attenuate inflammation. Consistent with inflammation analysis of transcriptomic data sets from the GEO database shows enrichment of TNF signalling pathway in AD. The results of this analysis suggest elevated levels of cytokines, ROS, oxidative stress, immune metabolism and involvement of immune cells in AD.Mitochondrial dysfunction leads to elevated levels of ROS and oxidative stress96. In CNS astrocytes synthesis and secrete both renin and Angiotensinogen97. The renin-angiotensin system is involved in the functioning of glymphatic system and helps to clear toxic metabolites and Aβ peptides98. Hypertension decreases CSF flow in the glymphatic system thus adversely affecting clearance of toxic metabolites or Aβ peptides resulting in it aggregation, contributing to neurodegeneration99. Further, studies have shown that inhibition of immune metabolism lead, to impaired synthesis and secretion of inflammatory cytokines by microglia100. Taken together our analysis shows activation of immuno-metabolism in AD. The glutamatergic dysfunction is associated with AD101. In brain, the glutamate from the synapse is taken through the excitatory amino acid transporters (EAAT) by astrocytes. Glutamate is further converted to glutamine and it is released by astrocytes which is taken up by the neurons102. AD is shown to have impaired glutamate-glutamine cycle resulting in excitotoxicity and death of neurons103. The GABAergic neurons are considerably reduced in AD compared to controls104. Excess amounts of Aβ downregulate GABA inhibitory interneuron activity that cause loss of inhibitory interneurons104. Further, our analysis shows the deregulation of complementation and coagulation pathway. Interestingly studies have shown the role for complement pathway in the pruning of synapses by microglia and memory loss105. The complement protein was shown to label the synapse to be pruned which is subsequently acted upon by the microglial cells106. The upregulation of complement pathway concomitant with microglial activation due to the inflammatory milieu in AD might prove to be detrimental to the disease process107. The impinging evidence thus accounts for the observed memory loss during disease progression in AD.Our cell type analysis also shows changes in immune cell types like T cells, microglia, astroglia, etc. Renin-angiotensin system regulates the blood pressure and fluid balance and in brain it is involved in memory acquisition and consolidation2, B5 and B6 ROS and oxidative stress in AD. Previous studies using MRI have shown that atrophy of affected regions in the brain of genetically susceptible individuals precedes the onset of symptoms in AD17. Neuronal protection early during the pre-symptomatic stage in genetically susceptible individuals could help to prolong the onset of symptoms in AD. Further, incorporation of N-acetyl cysteine or other anti-oxidants in the diet might also help to mitigate oxidative stress and neuronal cell death in AD108. Dietary intervention with B2, B5 and B6 will help to achieve favourable prognosis in AD. Overall, our results show that a combination of antioxidants, B2, B6, and pantothenate (B5) during the pre-symptomatic stage in genetically susceptible individuals might help to postpone the onset of symptoms and potentially help in better management of AD. Above all this study will help to appreciate the role of deregulated pathways in the biology of AD and as potential therapeutic targets to manage the disease. Summary of the findings from this study are shown in Fig. The study shows an important role for metabolic deregulation of pathways like TCA, Arginine, glutathione and energy metabolism, cell types like microglia, astroglia and neuronal cell types, Vitamin B52, GSE3698053, GSE4477054, GSE4835055, GSE14082956. Single cell transcriptomics examines the gene expression levels of individual cell types in a given population. The transcriptomic data sets used in this study are from those which represent only controls and AD patients. We have also ensured that the data sets have enough subjects for analysis and comparison. Detailed information of age, number, and gender of subjects is provided in Supplementary file Transcriptomics data from previously published literature curated in Gene Expression Omnibus (GEO) database was searched for Alzheimer’s disease and only those GEO datasets were selected where the differential gene expression analysis could be done by the GEO2R tool for gene expression data. We have analysed the gene expression datasets with GSE IDs—GSE5281109 analysis tool, and the samples are divided into two or more experimental or clinical groups. When comparing test samples to controls, the fold change between the groups follows the convention of positive for genes upregulated and negative for genes downregulated. Running the GEO2R analysis with default parameters after the samples have been assigned to the groups yields fold change in the top DEGs between the test and control groups based on their p-value. The DEGs were sorted based on the adjusted p-value for further analysis when the findings were downloaded. For subsequent analysis, differentially expressed genes having an adjusted p-value ≤ 0.05 were chosen. Upregulated and downregulated genes were segregated based on fold change value and pathway and transcription factor analysis was performed separately.The GEO2R tool was used to compare two or more sample groups in a GEO dataset to find differentially expressed genes (DEGs) in various experimental or clinical conditions. The samples to be evaluated are listed in the GEO2Rp-value ≤ 0.05) that were found during GEO2R analysis, gene enrichment analysis was performed using Enrichr maayanlab.cloud/Enrichr/58. Enrichr examines these genes using databases such as the KEGG110, Wiki, and Reactome pathway databases, as well as the DisGeNet database and the Gene Ontology (GO) database for biological processes, to determine which enriched pathways, biological processes, and diseases do they belong. Enrichr uses the combined score to identify pathways and cell types. Multiplying the Z-score of the deviation from the expected rank by the log of p-value from Fisher's exact test, the combined score is calculated58. The results are presented as a bar graph containing pathways, biological processes, and diseases based on the combined score. Cell type analysis was performed for all datasets using the Azimuth database.For the list of differentially expressed genes  Blood Serum61: 22 differentially expressed significant proteins with p-value < 0.05 and fold change(upregulated—1.5 and downregulated—0.67) were obtained from a published dataset with 20 Alzheimer’s patient samples and 20 control samples to find the deregulated pathways. (3). Cerebrospinal Fluid62: 65 differentially expressed significant proteins (63upregulated and 2 downregulated) with p-value < 0.0001 were obtained from a published dataset with 20 Alzheimer’s patient samples and 20 cognitively normal individuals samples to find the deregulated pathways. A detailed information of number of human subjects, gender and age is provided in Supplementary file Proteomics analysis was performed for three tissues blood serum, brain cortex, and cerebrospinal fluid using Enrichr maayanlab.cloud/Enrichr/. The datasets were obtained from existing published literature. To understand the dynamics at the protein level in different tissues (1) Brain Cortex63. 17 significantly deregulated proteins from blood serum are used for pathway analysis64. Detailed information of number of mice, gender, age and strain of all mice datasets are provided in Supplementary file The mice data selected had only proteomics and metabolomics. We also find that some of the data sets had very small number of metabolites which did not provide any significant pathways or overlap completely with pathways obtained from the present data set used in the study. The mice model is used to compare with human AD data sets to demonstrate that irrespective of different study settings, taxa and techniques used there is a considerable overlap of deregulated pathways among them. In addition, mice model is well appreciated and the overlapping pathways could be considered the core deregulated pathways that might be involved in disease progression. Mice proteomics analysis was performed for brain and blood tissues, datasets were obtained from the literature. The details of mice used for blood proteomics is triple transgenic mice 3xTg-AD mice expressing the human mutations APPSwe, PS1 M146V, and Tau P301L and strain B6129SF2/J. Details of mice used for brain proteomics is 3xTg AD mice and control strain used C57/BL6. Differentially expressed mice proteins are converted into human orthologs using DIOPT flyrnai.org/diopt and used for pathways analysis. Overall 148 significant differential proteins from the cortex and 93 hippocampal proteins are used for pathway analysisAD metabolomics studies give us insight into the pathophysiology of disease as the metabolites are collective termini of transcriptomics and proteomic profiles. We analysed three published datasets of AD patients obtained from the literature. Metaboanalyst 5.0 metaboanalyst.ca/ a web-based tool was used to analyse the pathways associated with the metabolites. Pathways with FDR  ≤ 0.25 are considered significant and used for further analysis.66: significant metabolites with p-value ≤ 0.05 were obtained from a dataset containing 21 AD patients and 19 control samples (2). Blood Serum67: 16 decreased and 7 increased differential metabolite s with adj. p-value ≤ 0.05 were obtained from a published dataset with 23 AD and 21 control samples (3). Cerebrospinal fluid68: 54 significant differential metabolites were obtained from a published dataset with 10 control samples and 10 AD patient samples. A detailed information of number of human subjects, gender and age is provided in Supplementary file Metabolomic analysis was performed on three tissues. The datasets were obtained from published literature. (1). Brain Cortex69. 15 significantly deregulated metabolites from blood plasma of APP/PS1 are used for pathway analysis112. 20 significantly deregulated metabolites from cerebrospinal fluid are used for pathway analysis71. Detailed information of number of mice, gender, age and strain of all mice datasets are provided in Supplementary file Mice metabolomics analysis was performed for the brain, blood and cerebrospinal fluid. The datasets were obtained from literature. Mice strain used for studies was C57BL/6J with APP/PS1 coexpressing the Swedish mutation(K595N/M596L) and the detaE9 with PS1 exon deletion in blood and brain and K670N, M671L mutations in CSF mice not expressing transgene was used as wildtype controls in blood, brain and CSF. A total of 73 significantly deregulated metabolites from brain are used for pathway analysis andp-value ≤ 0.05 are selected and further top 20 pathways and cell types with the highest combined score are selected for commonality study. Commonality study and venn diagram generation for proteomics and metabolomics datasets were carried out by venny online tool bioinfogp.cnb.csic.es/tools/venny/.Common significant pathways, cell types, transcription factors and kinases from 5 datasets are identified using multiple list comparator tool molbiotools.com/listcompare.php. Pathways and cell types with 2 (riboflavin), B5 (pantothenate), and B6 (pyridoxine) and its interacting proteins were generated using pre-published data in cytoscape 3.8.2114. A network was generated for each transcriptomic dataset. A comparative analysis was carried out for common genes between the cofactor-protein network and those involved in the enriched pathways from transcriptomic dataset analysis. Further, Cytoscape 3.8.2 was used to generate the integrated Transcriptomic-Cofactor-protein interaction network using the network merge tool for visualization. The generated network highlights key overlapping genes that are represented in both the cofactor-protein network and enriched pathway network from transcriptomics as shown.A cofactor-protein interaction network for vitamin BSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.Supplementary Information 14.


Subsequently, a validation experiment was conducted, and the expressions of GFAP, C3, TNF-α, and IL-6 were detected via immunofluorescence staining, confirming the inflammatory response at 30 min and 3 days post injury. Immunohistochemical staining for caspase-3 and TUNEL was also performed to assess apoptosis at the same time points. These results indicate that THF can effectively decrease neural cell apoptosis through the caspase-3 pathway and restrain excessive abnormal activation of astrocytes and the release of TNF-α and IL-6, which might be accompanied by the recovery of motor function. Thus, THF may serve as a promising therapeutic strategy for AIS through multiple targets, components, and pathways.Tanhuo formula (THF), a traditional Chinese medicinal formula, has been demonstrated to be effective in the clinical treatment of acute ischemic stroke (AIS). However, its active ingredients, potential targets, and molecular mechanisms remain unknown. Based on the validation of active ingredient concentrations, our study attempted to elucidate the possible mechanisms of THF based on network pharmacological analysis and experimental validation. Components of THF were screened using network pharmacological analysis, and a compound–target network and protein–protein interaction (PPI) network were constructed. In total, 42 bioactive compounds and 159 THF targets related to AIS were identified. The PPI network identified AKT1, TNF, IL6, IL1B, and CASP3 as key targets. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis demonstrated that the inflammation and apoptotic pathways were enriched by multiple targets. The main components of THF were identified  Acute ischemic stroke (AIS), characterized by high morbidity, disability, and mortality, is one of the leading causes of human death and disability worldwide; it, thereby, imposes an immense financial burden on society and severe psychological stress on individuals . AccordiIncreasing evidence has demonstrated that intervening inflammatory reactions are important targets in the clinical treatment of AIS. Astrocytes are important regulators of the neuroinflammatory response and have also been found to be effective targets for therapeutic intervention in ischemic stroke . Owing tCoptis chinensis Franch. , dried roots and rhizomes of Rheum officinale Baill., , dried fruits of Forsythia suspensa (Thunb.) Vahl , dried leaves of Lophatherum gracile Brongn. , and Rhizoma Arisaematis Cum Bile . The herbal components of THF, such as aloe emodin in DH, phillyrin in LQ, berberine, and palmatine in HL, have been shown to inhibit inflammatory responses and neuronal apoptosis in the brain , a traditional Chinese medicinal formula, has been generally acknowledged as an effective prescription for AIS caused by phlegm-heat syndrome in Xuanwu Hospital. Although our previous study confirmed that THF reduced infarct volume and attenuated neuronal damage in cerebral ischemic rats, the mechanism of antiapoptosis and astrocyte regulation has not yet been elucidated . THF comhe brain . We deteverified . Herein,LC-20AT HPLC , Welch Ultimate XB-C18 column , methanol and acetonitrile , ultrapure water , ethanol, and other reagents were used as analytical reagents. LQ , DNX . HL (batch number 1402001), DZY (batch number 1406001), and DH (batch number 1402001) were purchased from Anguo Changda Chinese herbal medicine Pieces Co. Ltd. . The identity of all the herbs was confirmed by a botanical expert, Professor Hui Li , and the specimens were archived in the institute. Berberine hydrochloride  and phillyrin , obtained from the National Institutes for Food and Drug Control , were used as reference standards.The composition of THF was HL (9 g), DH (5 g), LQ (10 g), DZY (9 g), and DNX (9 g). LQ (166.7 g) and DH (83.3 g), ratio: 2:1, were extracted three times by reflux extraction with 65% ethanol (eight times the volume) for 2 h at a time and filtered and concentrated into a filtrate with a relative density of 1.10–1.20 (50–60°C). HL (150 g), DNX , and DZY (150 g), ratio: 1:1:1, were extracted with water three times (2 h each time): the first time with 10 times the volume of water and the second and third times with eight times the volume of water. The extracts were filtered and concentrated into a filtrate with a relative density of 1.20–1.30 (60–70°C). The two filtrates were mixed, dried under reduced pressure, and finally crushed into a powder.Preparation of standard solutions: berberine hydrochloride and phillyrin were prepared in methanol to yield 0.15 and 0.20 mg/ml control solution, respectively, and then filtered through a 0.45-μm membrane filter before HPLC analysis. To prepare a sample solution for berberine, epiberberine, coptisine, and palmatine: THF dried powder (0.5 g) was added to 50 ml of methanol-hydrochloric acid (100: 1) and then sonicated for 30 min. Methanol was added to restore the solution weight to the initial. After filtration, 2 ml of the filtrate was placed in a 10-ml volumetric flask and methanol was added till it reached the mark. The sample solution was obtained by filtration through a 0.45-μm membrane. To prepare a sample solution for phillyrin, 1.0 g of THF dried powder was weighed and 15 ml 70% methanol was added, follow by sonication for 30 min, 70% methanol was used to replace the lost weight, and ultimately the sample solution of phillyrin was obtained by filtration through a 0.45-µm membrane.The concentrations of THF compounds were analyzed by HPLC. HPLC analysis was performed using a Welch Ultimate XB-C18 column .HPLC conditions for berberine, epiberberine, coptisine, and palmatine were as follows: octadecylsilane-bonded silica gel was used as the filler for the HPLC column. Acetonitrile (0.05 mol/L): potassium dihydrogen phosphate (50:50)  were used as the mobile phase. Chromatographic analysis was performed at 30°C with a flow rate of 1 ml/min, detection wavelength of 345 nm, and an injection volume of 10 µl.To perform HPLC for phillyrin, the mobile phase consisted of water (A) and acetonitrile (B), and the gradient elution program was (time/B%): 0–30 min, 23–24%; 30–31 min, 24–95%; 31–39 min, 95%; 39–40 min, 95–23%; 10–15 min, 50–80%; and 40–50 min, 23%. Chromatographic analysis was performed at 30°C with a flow rate of 1 ml/min, detection wavelength of 277 nm, and an injection volume of 10 µl.etc. was used to screen the active ingredients of THF. The TCMSP is a recognized Chinese herbal medicine platform that provides detailed pharmacokinetic data of herbal ingredients, such as oral bioavailability (OB), drug-likeness (DL), aqueous solubility, and the blood–brain barrier (BBB) etc.. Due to etc..https://old.tcmsp-e.com/tcmsp.php), ETCM (http://www.tcmip.cn/ETCM/index.php/Home/Index/), TCMID (http://www.megabionet.org/tcmid/), and BATMAN (http://bionet.ncpsb.org/batman-tcm/index.php) databases and literature search. Subsequently, the TCMSP platform was used to identify target genes. In addition, the targets were predicted using the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) for the quality marker (Q-maker) of each herb in THF. Subsequently, different ID types of the targets were transformed into UniProt IDs using the UniProt database (https://www.uniprot.org/).THF compounds were clarified from the TCMSP (https://www.genecards.org) and OMIM (https://omim.org). “Cerebral infarction,” “stroke,” and “cerebral ischemia” were used as the keywords for target searching. The relevance score was set as greater than or equal to 5, duplicate targets were excluded, and cerebral infarction–related targets were screened out. Intersection of THF-AIS–related target genes was performed to verify common target genes.AIS-related targets were predicted using GeneCards (https://string-db.org/) for PPI network mapping by setting species as “Homo sapiens” and moderate confidence. Subsequently, the PPI network and component–target network were created and visualized using Cytoscape 3.8.0 software, and the degree centrality, betweenness centrality, and closeness centrality were calculated. The size and color of a node represent the degree value, which further reflects its importance and the number of connections with other nodes.Common targets of THF and AIS were uploaded into the STRING database (The ClusterProfiler software package and R software were used to perform Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. GO function enrichment analysis involves biological processes (BP), molecular function (MF), and cellular component (CC) biology.https://www.rcsb.org/). The compounds were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Prior to docking, all receptor proteins were treated with PyMol 2.5, including the removal of water molecules, salt ions, and small molecules. Molecular docking was then accomplished using AutoDock Vina 1.1.2 software, and docking was performed using the default docking parameters. The docking results were visualized using PyMol 2.5.The target receptors were retrieved by downloading them from the PDB database . All male 8-week-old Sprague–Dawley rats were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (License Number: scxk[Beijing]2016-0006) and raised in the experimental animal center of Capital Medical University in a specific pathogen-free SPF environment . Twelve male rats were used to avoid the protective effects of estrogen. The rats were anesthetized using 5% isoflurane , and the right common carotid artery (CCA), internal carotid artery (ICA), and external carotid artery (ECA) were gently dissected and exposed. After permanent ligation of the CCA and ECA in the proximal portion, the distal end of the ICA was clamped using hemostatic forceps, and a small incision was made at the bifurcation of the CCA. The preprocessed nylon suture was then inserted from the CCA into the ICA for 18–22 mm until resistance was probed in the middle cerebral artery. Finally, a nylon suture  was fixed to prevent spontaneous withdrawal, and the incision was sutured and disinfected. The sham group was treated using the same procedure but without MCAO.The THF dried powder  was provided by the China Academy of Chinese Medical Sciences. The daily consumption of THF is 4.9 g crude drug/kg in rats, which is equivalent to 0.7 g crude drug/kg in patients. We set low dose of THF (THF-L) and high dose of THF (THF-H) groups in rats, approximately one and two times the dosage of clinical patients, respectively: 0.92 g dried powder/kg; 1.84 g dried powder/kg.The animals were randomly assigned to four groups: sham group, model group (MCAO rats), THF-L group (0.92 g dried powder/kg), and THF-H group (1.84 g dried powder/kg). For the THF-L group, 9.2 g of dry powder was weighed and dissolved in 100 ml of deionized water, meaning 10 ml of the solution contained 0.92 g of the drug, and the volume administered to rats was 10 ml/kg/d. For the THF-H group, 18.4 g of dry powder was weighed and dissolved in 100 ml of deionized water, meaning 10 ml of the solution contained 1.84 g of the drug, and the volume administered to rats was 10 ml/kg/d. The rats in the administration group were intragastrically administered 6, 24, 48, and 72 h after cerebral ischemia surgery. The rats in the sham and model groups were administered the same volume of saline (10 ml/kg/day) at the same time points.via rapid left ventricular perfusion, and fixed with 4% paraformaldehyde in PBS solution for 60 min. This process was carried out at 3 days and 30 min post injury (dpi) for immunofluorescence studies. The striatal level of the brain was then dissected and fixed with 4% paraformaldehyde in PBS solution for 1 week. After that, the tissues were dehydrated with gradient ethanol, made transparent with xylene, embedded in paraffin, and cut into 0.5-μm sections using a biological tissue microtome. After dewaxing and hydration, the slides were immersed in 5% BSA for 1 h to block nonspecific antibodies. All paraffin sections were then incubated with primary antibodies overnight at 4°C, namely, GFAP  and C3. These sections were then incubated with Alexa Fluor 594 and Alexa Fluor 488  for 2 h.For immunofluorescence , the ratFor immunohistochemistry , the sliTUNEL staining  was used to detect apoptosis in tissues . First, P < 0.05 was considered to be statistically significant.Statistical analyses were performed using SPSS 26.0 . All data are expressed as the mean ± standard deviation. Homogeneity of variance was tested; one-way analysis of variance was used to compare multiple groups; and subsequently, the LSD test was performed for pairwise comparison. HL is a monarch medicine, and LQ has the highest dosage in THF. A qualitative study of HL and LQ is important for controlling the quality of the entire prescription. Therefore, berberine, epiberberine, coptisine, palmatine of HL, and phillyrin of LQ were selected as quality control indices according to the standards of the Chinese Pharmacopoeia 2015 edition. HPLC was used to determine the concentrations of the key compounds. The results are shown in Traditional Chinese medicine is a multitarget, multicomponent therapy model. By screening the TCMSP, ETCM, TCMID, and BATMAN databases, a total of 64 active ingredients were retrieved, of which 42 had regulatory effects on cerebral infarction. DH, HL, LQ, DNX, and DZY contained eight, 10, 16, five, and one chemical compounds/compound, respectively. Beta-sitosterol is a common component of DH and DNX, whereas quercetin is a common component of HL and LQ .A total of 518 targets were obtained by eliminating duplicate targets . These dTo identify the predominant common targets of THF and AIS, a network analysis of PPI was performed using the STRING database . A totalTo establish the correlation between THF and potential targets, the “Compound–Target Network” was conducted using Cytoscape 3.8.0 . This nep <0.05). The top 10 significant terms in the BP, CC, and MF categories are shown in To determine the biological characteristics of potential targets of THF on AIS, GO and KEGG pathway enrichment analyses on 159 common targets were performed using the ClusterProfiler software package of the R platform. A total of 2681 GO functional processes were obtained and annotated in terms of BP, MF, and CC (Berberine (the most abundant component in HL) and phillyrin were used for molecular docking. The key targets IL-6 and TNF-α were also selected for analysis. It is generally accepted that the lower the Vina score, the more stable the binding of the compound is to the crucial target. The results indicated that berberine and phillyrin had good binding activity for IL-6 and TNF-α .As network pharmacology analysis suggested that THF may protect against neuronal damage by inhibiting the apoptotic pathway, TUNEL staining was used to estimate the number of apoptotic cells at the infarction locus. A marked increase in apoptotic cells was observed in the cortex of MCAO rats, whereas THF-L and THF-H remarkably reduced apoptosis . ImmunocIn the network pharmacology analysis, IL-6 and TNF were the hub genes of the PPI network, and the TNF signaling pathway was the key pathway in the KEGG enrichment, suggesting that the inflammatory response may play a pivotal role in treating AIS by THF. To evaluate the inflammatory response after cerebral infarction, immunofluorescence was performed to determine the activation of A1 astrocytes that induce an evident inflammatory response in many CNS diseases. First, double immunofluorescence staining of GFAP and C3 was performed to label astrocytes, in which positive cells labeled with C3/GFAP represented A1-type pro-inflammatory astrocytes, whereas positive cells labeled with GFAP represented all types of astrocytes. In this study, a large number of C3-/GFAP-labeled positive cells were detected at the edge of the infarct foci in MCAO rats, but not in the ipsilateral brains of rats in the sham group. Furthermore, the statistical results showed that the cell density of GFAP and C3/GFAP positive cells around infarct lesions in the THF-L and THF-H groups was lower than that in MCAO rats . SimultaAIS is a common disease in elderly people, with a stepwise sequence of pathophysiological processes that are strongly correlated to inflammatory responses and apoptosis during development and progression . THF is in vivo experiments showed that phillyrin inhibits microglia-mediated inflammation by promoting the polarization of “M2” microglia. In addition, phillyrin has been reported to alleviate apoptosis by inhibiting apoptotic pathways such as caspase-3 (Network pharmacological analysis was first used to predict and determine the active ingredients and potential targets of THF in AIS. First, 42 potential compounds associated with the therapeutic effects of cerebral infarction were identified. By constructing a PPI network for the common targets of THF and AIS, AKT1, TNF, IL6, IL1B, and CASP3 were predicted as pivotal targets. Finally, KEGG pathway enrichment analysis showed that inflammation- and apoptosis-related pathways were significantly enriched, and we focused on the TNF pathway. IL-6 is a pleiotropic cytokine primarily produced by activated microglia and astrocytes. Upregulation of IL-6 may act on microvascular endothelial cells and increase the expression of adhesion molecules and chemokines, thus mediating the inflammatory cascade and aggravating cerebral ischemic injury . CASP3 iaspase-3 . Altogetaspase-3 . Therefoaspase-3 . Our finNumerous studies have demonstrated that abnormally activated A1 astrocytes exhibit a detrimental effect on the transmission of inflammatory mediators and glutamic acids, initiating immune cascade reactions and leading to increased tissue damage, such as the destruction of the BBB, brain edema, and neuronal degeneration and death . TNF-α iFinally, although our study demonstrates that THF has an impact on the inflammatory response and apoptosis by targeting IL-6, TNF-α, and caspase-3 in MCAO rats, the upstream pathway has not been elucidated. In addition, a previous study showed that THF can improve clinical outcomes by altering the gut microbiota . HoweverTo the best of our knowledge, ischemic stroke is a complex pathological process mediated by multiple targets and multiple pathways, and the complicated pathophysiological mechanism of THF in treating AIS was explored based on network pharmacology analysis and experimental validation. According to network pharmacology analysis, 42 bioactive compounds and 159 targets of THF related to AIS were obtained. Simultaneously, AKT1, TNF, IL6, IL1B, and CASP3 were identified as hub targets. Inflammation and apoptosis were significantly enriched in multiple signaling pathways and multiple targets. Therefore, THF is likely to be involved in modulating AIS by primarily regulating the TNF signaling pathway and targeting certain genes such as CASP3, IL-6, and TNF-α. Based on experimental validation, this study provides new insights into the mechanisms by which THF can effectively decrease neural cell apoptosis through the caspase-3 pathway and restrain the excessive activation of A1 astrocytes. Therefore, THF may serve as a prospective treatment strategy for AIS through multiple targets, components, and pathways.


Background: Diabetic kidney disease (DKD) is the most common cause of end-stage renal disease. The effective treatment of DKD would rely on the incorporation of a multi-disciplinary. Studies have shown that Tripterygium wilfordii Hook.F. and Trichosanthes kirilowii Maxim have remarkable curative effects in treating DKD, but their combination mechanism has not been fully elucidated.Methods: We explored the mechanism of Tripterygium wilfordii Hook.F.-Trichosanthes kirilowii Maxim decoction  in the treatment of DKD by network pharmacology and molecular docking. The main active components and action targets of LTD were collected from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database. The speculative targets of DKD were obtained from GeneCards, DisGeNET, and Online Mendelian Inheritance in Man (OMIM) databases. Then, an herb-component-target network was constructed based on the above analyses. The biological function of targets was subsequently investigated, and a protein-protein interaction (PPI) network was constructed to identify hub targets of DKD. The gene ontology (GO) function enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed by RStudio. Finally, molecular docking was performed by AutoDock Vina and PyMOL software to explore the interaction between compounds and targets. Furthermore, the DKD model of human renal tubular cells (HK-2) induced by high glucose (HG) was selected, and the predicted results were verified by western blot analysis and immunofluorescence.Results: A total of 31 active components of LTD were screened out, and 196 targets were identified based on the TCMSP database. A total of 3,481 DKD related targets were obtained based on GeneCards, DisGeNET, and OMIM databases. GO function enrichment analysis included 2,143, 50, and 167 GO terms for biological processes (BPs), cellular composition (CCs), and molecular functions (MFs), respectively. The top 10 enrichment items of BP annotations included response to lipopolysaccharide, response to molecule of bacterial origin, response to extracellular stimulus, etc. CC was mainly enriched in membrane raft, membrane microdomain, plasma membrane raft, etc. The MF of LTD analysis on DKD was predominately involved in nuclear receptor activity, ligand-activated transcription factor activity, RNA polymerase II-specific DNA-binding transcription factor binding, etc. The involvement signaling pathway of LTD in the treatment of DKD included AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, insulin resistance, TNF signaling pathway, etc. Molecular docking results showed that kaempferol, triptolide, nobiletin, and schottenol had a strong binding ability to PTGS2 and RELA. Furthermore, the in vitro experiments confirmed that LTD effectively decreased the expression of PTGS2, NF-κB, JNK, and AKT in the HG-induced DKD model.Conclusion: The findings of this study revealed that the therapeutic efficacy of LTD on DKD might be achieved by decreasing the expression of PTGS2, NF-κB, JNK, and AKT, which might improve insulin resistance, inflammation, and oxidative stress. These findings can provide ideas and supply potential therapeutic targets for DKD. Diabetic kidney disease (DKD), which accounts for about 40% of diabetic patients, is the main cause of chronic kidney disease (CKD) worldwide. The natural history of DKD includes glomerular hyperfiltration, progressive proteinuria, decreased glomerular filtration rate, and eventually end-stage renal disease (ESRD) . DKD is Tripterygium wilfordii Hook.F. and Trichosanthes kirilowii Maxim have remarkable curative effects in treating DKD. Experiments in vitro and in vivo have shown that Tripterygium wilfordii Hook.F. has powerful immunosuppressive, anti-inflammatory, and anti-proteinuria effects  has shown remarkable advantages in treating DKD. Studies have shown that  effects . Trichos effects . The comNetwork pharmacology is a new method that combines computer science with medicine, constructs, and visualizes the interaction network of multi-gene, multi-target, and multi-pathway to evaluate the molecular mechanism of herbs . BecauseTripterygium wilfordii Hook.F.-Trichosanthes kirilowii Maxim decoction  for the treatment of DKD by network pharmacology and molecular docking. The main active components and targets of LTD in the treatment of DKD were screened based on network pharmacology and molecular docking, and the pharmacodynamic material basis of LTD in the treatment of DKD was preliminarily verified, to provide ideas for further mechanism research and clinical promotion.In this paper, we initially explore the mechanism of action of https://tcmsp-e.com/) was used to extract the active components of LTD the compounds were screened according to the two ADME attribute values of oral bioavailability (OB)≥30% and herb-likeness property (DL)≥0.18. Consulting the literature and searching the TCMSP database, there are few active compounds of Trichosanthes kirilowii Maxim. Then the active compounds of Trichosanthes kirilowii Maxim were transformed into Canonical SMILES format by PubChem database (https://pubchem.ncbi.nlm.nih.gov/). SMILES was imported into SwissTargetPrediction (http://www.swisstargetprediction.ch/) platform, and the attribute was set as Homo Sapiens to predict the potential targets of active compounds and supplement the known targets of unpredicted active compounds according to literature reports (https://www.uniprot.org/) to corrected the gene names of targets. The target proteins were introduced into Cytoscape3.9.0 software to construct an herb-component-target network and to be analyzed.Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) ( reports  and usedhttps://www.genecards.org/), DisGeNET databases (https://www.disgenet.org), and OMIM databases (http://www.omim.org), “diabetic kidney disease” and “diabetic nephropathy” was used as the search term to collect disease targets related to DKD. Finally, duplicates from the three databases were removed to obtain the final DKD related targets.In the GeneCards databases (https://cn.string-db.org/) to build a Protein-Protein Interaction (PPI) network. The biological species was set as “Homo sapiens”, the minimum interaction threshold was set as “highest confidence” (>0.9), and the isolated proteins were hidden. Then, the PPI network was obtained by Cytoscape3.9.0 software, and the core targets were screened by CytoNCA. The core targets were obtained with Degree, Betweenness, and Closeness greater than or equal to the median as screening conditions.The intersecting targets and Venn diagram of LTD and DKD targets were obtained through the online Venn map platform. Intersection targets were extracted and submitted to STRING version 11.5 database , molecular functions (MFs), and cellular compositions (CCs) were included in the GO function enrichment analysis. Furthermore, the condition was limited to The components of LTD, LTD-DKD-KEGG pathway common targets and pathway were imported into Cytoscape3.9.0 software to obtain the network diagram of herb-compound-target-pathway-disease network. Then, the targets and components in the top 4 of the Degree value were selected for molecular docking, and the 3D structure was downloaded from PubChem and converted to the component structure through PDB format. The receptor file of the core protein would be imported into PyMOL software to remove small-molecule ligands and water molecules, and imported into AutoDockTools software to add hydrogen, then output as a PDBQT file. The molecular docking was conducted by importing into AutoDock Vina software to obtain the minimum binding free energy of the target protein and the ligand. The optimal binding pattern of each protein was plotted by using Discovery Studio 2019 software.Human kidney proximal tubular epithelial cells (HK-2) were maintained with DMEM/F12 medium containing 10% FBS in the conditions: 95% air and 5% CO2 at 37°C in an incubator, and passaged or preserved every 2–3 days. Cells were observed under microscopy and digested with 0.25% trypsin solution for passaging when they reached 80%–90% confluence. HK-2 cells were separated into three groups: normal group (DMEM/F12 medium supplemented with 5.5 mmol/L glucose), high glucose (HG) group (DMEM/F12 medium supplemented with 30 mmol/L glucose), and high glucose (HG)+LTD group .After inventions, the whole cell lysate of HK-2 was obtained using RIPA buffer  and cOmplete™ Protease Inhibitor Cocktail tablets . The protein concentration was quantified by the Quick Start™ Bradford Protein Assay Kit . Then the samples were separated electrophoresed through 10% SDS-polyacrylamide gels, and then transferred to the PVDF membrane . Following blocking in 5% non-fat milk for 1 h at room temperature, and incubated overnight at 4°C with primary antibodies. The primary antibodies included PTGS2 Monoclonal antibody , NF-κB p65 Polyclonal antibody , Phospho-NF-κB p65 , AKT Monoclonal antibody , Phospho-AKT Monoclonal antibody , Recombinant Anti-JNK1+JNK2+JNK3, Phospho-JNK , GAPDH . Then, the membranes were incubated with secondary antibodies  for 1 h at room temperature.HK-2 cells were immunofluorescently stained for phospho-p65 using anti-phospho-NF-κB p65 antibody in a humid environment at 4°C overnight. After being immunostained, the cells were treated for 1 h at 37°C with a secondary antibody that was TRITC (red fluorescence) or FITC (green fluorescence)-labeled. DAPI solution (1.0 g/ml) was then used to stain the cell nuclei for 10 min. A fluorescence microscope  was used to capture images of immunofluorescence staining.Tripterygium wilfordii Hook.F. and 2 from Trichosanthes kirilowii Maxim. There were 22 active components without corresponding targets in the database, and 31 active components were finally obtained, as shown in Trichosanthes kirilowii Maxim were predicted based on the SwissTargetPrediction platform, and the targets and repetitions with a probability of 0 were deleted, and a total of 61 potential targets were obtained. After the repetitive items were deleted, a total of 196 potential targets for the active components in LTD were finally obtained.The active components of LTD were screened through the TCMSP database. With OB ≥ 30% and DL ≥ 0.18 as the screening conditions, a total of 53 active components were obtained, including 51 from Trichosanthes kirilowii Maxim, the yellow nodes represented 29 active components of Tripterygium wilfordii Hook.F., and the blue nodes represented 196 targets related to the components of LTD. The nodes with a larger area and darker color have a larger degree value. According to herb-component-target network analysis, the active components in the top 6 of the Degree values were kaempferol, spinaterol, schottenol, β-sitosterol, nobiletin, and triptolide, respectively. The top 6 targets of Degree value were prostaglandin-endoperoxide synthase 2(PTGS2), sodium voltage-gated channel alpha subunit 5(SCN5A), nuclear receptor coactivator 2(NCOA2), progesterone receptor (PGR), DNA topoisomerase II alpha (TOP2A), and potassium voltage-gated channel subfamily H member 2(KCNH2), respectively. It was speculated that they might be the important components and possible active targets of LTD.The obtained active components and potential targets were imported into Cytoscape3.9.0 software, and the obtained herb-component-target network was shown in The GeneCards database obtained 3,422 diabetic nephropathy targets and 13,244 diabetic kidney disease targets. The targets with a relevance score greater than the median were set as the potential targets of DKD, and the final targets were obtained through multiple median screening. After DisGeNET, OMIM, and the database were combined to supplement the related targets, and the duplicate values were deleted after the combination, a total of 3,481 DKD related targets were obtained.Taking the intersection of the LTD and DKD related targets, a total of 126 potential action targets of LTD in the treatment of DKD were obtained, as shown in In the GO function enrichment analysis of 126 intersection targets using RStudio, 2,143 BPs, 50 CCs, and 167 MFs were obtained as the target genes. The first 10 entries of the enrichment pathway were selected to visualize the data, as shown in The components of LTD, LTD-DKD-KEGG pathway common targets and KEGG pathway were imported into Cytoscape3.9.0 software for intersection, and the herb-component-target-pathway network was obtained, as shown in −1, which indicated that LTD had a strong binding activity with the predicted action targets. The docking binding energy was shown in Molecular docking was conducted on the key targets and active ingredients in the herb-component-target-pathway network. The protein structures of the core targets PTGS2(5F19), RELA (3QXY), AKT1(3CQU), and MAPK8(2XRW) were downloaded from the PDB database according to the set selection conditions. It is generally believed that the closeness of association between protein and ligand can be represented by the binding energy of docking binding. Generally, lower binding energies will lead to more stable binding conformations and more likely interaction . Moleculin vitro, we exposed cultured HK-2 cells to glucose (30 mmol/L) and showed an upregulation of PTGS2, NF-κB, JNK, and AKT expression , c-Jun N-terminal kinases (JNK), and AKT expression pression , whereaspression .In our research, we found that LTD may act on key targets such as PTGS2, NF-κB, JNK, and AKT through a variety of chemical components , and further exert its multi-component, multi-target, and multi-network renal protection by regulating the AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, insulin resistance, and TNF signaling pathway.The pathogenesis, pathological process, and related mechanisms of DKD are complex and diverse. Modern medicine mainly treats DKD by controlling blood glucose and preventing or delaying the progressive deterioration of renal function. At present, the therapeutic schemes mainly include angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, and sodium-glucose cotransporter 2 inhibitors. However, the residual risk of DKD progression and cardiovascular death remains high. In recent years, TCM has demonstrated significant advantages in the treatment of DKD, but the specific mechanism remains unclear. Therefore, it is of great significance to explore the mechanism of TCM in the treatment of DKD.Tripterygium wilfordii Hook.F. and its extracts can effectively reduce proteinuria and protect renal function. Its pharmacological mechanism is related to anti-inflammation, anti-oxidation, anti-fibrosis, and anti-glomerular sclerosis. The mechanism is achieved by balancing Th1/Th2 cells, regulating macrophage infiltration, and regulating p38 mitogen-activated protein kinases (MAPK), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), transforming growth factor-β(TGF-β), Wnt/β catenin signaling, AKT serine/threonine kinase (AKT), and neurogenic locus notch homolog protein 1(Notch1)  . Ma et a(Notch1) . The resteinuria . Modern immunity .Tripterygium wilfordii Hook.F. and its extracts are often used in the treatment of a variety of chronic kidney diseases , as well as renal blood flow and hemodynamics . RAAS isThrough GO function enrichment analysis, it was found that LTD exerted effects on the treatment of DKD mainly by participating in such biological processes as response to lipopolysaccharide, response to molecule of bacterial origin, response to extracellular stimulus, membrane raft, membrane microdomain, plasma membrane raft, nuclear receptor activity, ligand-activated transcription factor activity, RNA polymerase II-specific DNA-binding transcription factor binding, etc., demonstrating the multifunctional nature of LTD component. The results of the KEGG pathway enrichment analysis showed that it was mainly involved in AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, insulin resistance, TNF signaling pathway, etc. The AGE-RAGE signaling pathway is closely related to diabetes mellitus and its complications. With the occurrence of insulin resistance, excessive glucose in the blood will cause non-enzymatic glycosylation of proteins, lipids and DNA in the body, resulting in excessive advanced glycosylation end products (AGEs), which are widely distributed in various tissues . AGEs maIn addition, the core components in the top 4 of the Degree value in the herb-component-target-pathway network diagram of LTD were molecularly docked with the core targets in this study. The results showed that the docking effect of the model is generally good and the structure is stable. The PTGS2 and RELA have relatively low binding energies for docking with most components. These results suggested that LTD might treat DKD by targeting PTGS2 and RELA.In summary, the mechanism of LTD in the treatment of DKD was analyzed and discussed in this paper by network pharmacology and verified by molecular docking analysis, western blotting, and immunofluorescence based on the preliminarily screened action targets. The results indicated that the therapeutic efficacy of LTD on DKD might be achieved by decreasing the expression of PTGS2, NF-κB, JNK, and AKT, which might improve insulin resistance, inflammation, and oxidative stress. Moreover, from the perspective of holism in TCM, the multi-component and multi-target synergistic therapeutic effects of TCM are revealed, which is of certain guiding significance for further confirming the key active components of LTD in the treatment of DKD and the development of new herbs in the later stage. We will focus on the study related to the treatment of DKD with LTD to clarify the efficacy and specific mechanism of LTD in intervening DKD.