Abstract Objective: We aimed to reveal the potential active ingredients, targets and pathways of Shiwei Hezi pill (SHP) in the treatment of nephritis based on systematic network pharmacology. Methods: The online database was used to screen the common targets of SHP and nephritis, and the interaction between targets was analyzed. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Bioinformatics website. Molecular docking was carried out to verify the correlation between core ingredients and key targets. Cytoscape 3.6.1 was applied to perform protein-protein interactions (PPT) network construction and data visualization. Results: A total of 82 active ingredients in SHP were screened, and 140 common targets of SHP and nephritis were obtained. Our results demonstrated that TNF, AKT1 and PTGS2 might be the key targets of SHP in the treatment of nephritis. GO enrichment analysis yielded 2163 GO entries (p < 0.05), including 2,014 entries of the biological process (BP) category, 61 entries of the cell composition (CC) category and 143 entries of the molecular function (MF) category. KEGG pathway enrichment analysis produced 186 signaling pathways (p < 0.05), involving the AGE-RAGE, IL-17and TNF signaling pathways. The results of molecular docking showed that three active ingredients in SHP (quercetin, kaempferol and luteolin) could effectively bind to the TNF, AKT1 and PTGS2 targets. Conclusion: The effective active ingredients in SHP may regulate multiple signaling pathways through multiple targets, thereby exhibiting a therapeutic effect on nephritis. Keywords: network pharmacology, mechanisms, Shiwei Hezi pill, nephritis, molecular docking 1 Introduction Nephritis is an immunity-mediated inflammatory response that has a great impact on human health ([27]Fotioo, 1949; [28]Kinoshita, Hirasawa et al., 1966). It belongs to a chronic kidney disease and has emerged as a major global health concern, accounting for approximately 69 percent of the total global disease burden. The mortality of nephritis is considerable and has increased 16.1 percent from 2006 to 2016. In China, around 119.5 million patients suffered chronic kidney disease, making it the second highest chronic disease after hypertension (254 million), and the number of patients with chronic kidney disease is higher than that with diabetes (113.9 million). Due to the unclear etiology and untypical early clinical symptoms of renal disease secondary to nephritis, coupled with the fact that some patients are skeptical of existing clinical treatments, research on drugs for the treatment of nephritis is urgently needed. With the continuous development of Chinese medical practice, traditional Chinese medicine (TCM) is becoming increasingly recognized for its potential in treating chronic and complex diseases both at home and abroad ([29]Cheung, 2011). In 2010, TCM products exported to Europe and the United States amounted US$ 2 billion and US$ 7.6 billion, respectively, and these figures are still growing ([30]Cheung, 2011). Of course, TCM is also believed to play a vital role in the treatment of kidney-related diseases. For example, Ying Ding et al. have revealed the better curative effect of Tripterygium wilfordii combined with Salvia miltiorrhiza on children with allergic purpura nephritis ([31]Ding, Zhang et al., 2019). Wan Yudang et al. have summarized the clinical characteristics of patients with heat shock nephritis, providing the most effective theoretical basis for TCM in terms of heat shock nephritis treatment ([32]Dang, Yan et al., 2018). Overall, TCM has been studied to be effective in the treatment of kidney-related diseases, especially nephritis. Shiwei Hezi pill (SHP) is a classic Tibetan medicine prescription commonly used to treat nephritis, which consists of ten species of medicinal herbs, including Chebulae Fructus (Hezi), Carthami Flos (Honghua), Radixet Rhizoma Rubiae (Zangqiancao), Mountain alum leaf (Shanfanye), Swertia petiolata (Zhangyacai), Lithospermum erythrorhizon (Zicaorong), Canavaliae Semen (Daodou), Alpinia Katsumadai Hayat (Doukou), Slag breaking cream (Zhaxungao), and Sabina chinensis (Yuanbai) ([33]Table 1). The effectiveness of SHP in treating nephritis has been demonstrated in many studies. For instance, in a study by Ramala, nephritis patients treated with Yishen fossil granules were recruited for the control group, and patients treated with SHP were for the study group. By comparison, Tibetan medicine SHP exhibited high therapeutic efficiency and had a positive effect on alleviating the symptoms of nephritis ([34]Ala, 2017). However, due to the complex formula of SHP, its specific mechanism in the treatment of nephritis remains unclear. TABLE 1. A list of name categories for SHP. Number Herb name Chinese spelling Latin name 1 Hezi Chebulae Fructus 2 Honghua Carthami Flos 3 Zangqiancao Radixet Rhizoma Rubiae 4 Shanfanye Mountain alum leaf 5 Zhangyacai Swertia petiolata 6 Zicaorong Lithospermum erythrorhizon 7 Daodou Canavaliae Semen 8 Doukou Alpinia Katsumadai Hayat 9 Zhaxungao Slag breaking cream 10 Yuanbai Sabina chinensis [35]Open in a new tab Nowadays, network pharmacology has been extensively adopted to elucidate the mechanism of TCM compounds and recipes in the treatment of diseases, and in other words, the mystery of TCM prescriptions in the treatment of complex diseases has been gradually unraveled. For example, Yue SJ et al. have explained the mechanism of Danggui-Honghua in the treatment of blood stasissyndrome by the systems pharmacology approach ([36]Yue, Xin et al., 2017); Liu J et al. have revealed the therapeutic properties of Saffron formula in treating cardiovascular diseases based on systematic pharmacology dissection ([37]Liu, Mu et al., 2016); Pang XC et al. have employed the virtual screening and network pharmacological methods to analyze the potential efficacy of Naodesheng formula in the treatment of Alzheimer’s disease ([38]Pang, Kang et al., 2018); and similar scientific approaches were also performed by Xie W et al. to predict the anti-depressive effect of Panax Notoginseng Saponins ([39]Xie, Meng et al., 2018). However, there are no in-depth studies which explore the specific mechanism of SHP in nephritis treatment by applying network pharmacological method. This study adopted network pharmacology method to identify the potential active ingredients, key targets and pathways of SHP in the treatment of nephritis, and molecular docking was carried out to investigate the interactions between selected key targets and active compounds. As shown in [40]Figure 1, a schematic diagram of network pharmacological strategy was generated to determine the pharmacological mechanism of SHP in treating nephritis. FIGURE 1. [41]FIGURE 1 [42]Open in a new tab Network pharmacology flow chart of SHP in treating nephritis, including database preparation, PPI network construction, GO and KEGG pathway analyses, and molecular docking validation. 2 Methods 2.1 Acquisition of SHP ingredients The chemical composition of each herb in SHP was obtained from free public databases, including TCMSP ([43]http://ibts.hkbu.edu.hk/LSP/tcmsp.php) and TCMID ([44]http://tcm.cmu.edu.tw) databases ([45]Chen, 2011; [46]Ru, Li et al., 2014). SHP ingredients with oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 were then screened according to a number of relevant literature criteria. Ultimately, a data set of the potential active ingredients in SHP was constructed ([47]Chandran, Mehendale et al., 2015; [48]Kibble, Saarinen et al., 2015; [49]Huang, Cheung et al., 2017). 2.2 Acquisition of SHP-related targets The targets of the potential active ingredients in SHP were downloaded from the TCMSP database, and their duplicates were deleted. The UniProt ([50]https://www.uniprot.org/) database was retrieved to annotate the target protein sequence, after that, the corresponding targets of the bioactive ingredients in SHP were obtained. We then established the ingredient-target data set by converting the target sites into gene names with the species limit to “Homo sapiens” by the DAVID database ([51]https://david.ncifcrf.gov) ([52]Dennis, Sherman et al., 2003). 2.3 Acquisition of nephritis-related targets The nephritis-related targets were filtered by searching the PubMed ([53]https://pubmed.ncbi.nlm.nih.gov/) ([54]Amberger, Bocchini et al., 2015), OMIM ([55]http://omim.org/) ([56]Sayers, Agarwala et al., 2019), and GeneCard ([57]https://www.genecards.org/) ([58]Stelzer, Rosen et al., 2016) databases using “Nephritis” as a keyword. Afterwards, deduplication was performed, and a data set of nephritis-related targets was successfully created. 2.4 Acquisition of common targets of SHP and nephritis The SHP and nephritis-related targets were imported into the Venny 2.1.0 online platform ([59]https://bioinfogp.cnb.csic.es/tools/venny/) to construct a Venn diagram of common targets of SHP and nephritis. 2.5 Construction of protein-protein interaction (PPI) network The data set of common targets of SHP and nephritis was imported into the STRING database Version 10.5 ([60]https://cn.string-db.org/) with the species limit to “H. sapiens”. The PPI was obtained and saved as a “tsv” format file. Afterwards, the file was input into Cytoscape 3.6.1 to build a PPI network. 2.6 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses GO functional annotation and KEGG pathway analysis were performed via the Bioinformatics website ([61]http://bioinformatics.com.cn/). By entering a list of target gene names and restricting the species to human, all target gene names were corrected to their official gene symbols. The top 15 and top 20 entries, including biological processes (BP), cellular components (CC) and molecular functions (MF), were selected for analysis with p-value as a screening criterion. The histograms and bubble charts were plotted to further explore the biological significance of common targets of SHP and nephritis. 2.7 Construction of network In order to further investigate the mechanism action of SHP in nephritis treatment, the herb-ingredient, herb-ingredient-target-disease and target-pathway networks were established and visualized using Cytoscape 3.7.1, an open source software for visualizing complex networks ([62]Su, Morris et al., 2014). In these networks, “nodes” represented herbs, compounds, targets, diseases or pathways, and “edges” represented the interactions between them. 2.8 Molecular docking The most important active ingredients and action targets are selected for molecular docking. The files of 3D structures of compounds were downloaded in SDF format from the PubChem database, while the files of the 3D structures of targets were obtained in PDB format from the Protein Data Bank (PDB) database ([63]https://www.rcsb.org/) ([64]Chen, Chen et al., 2019). Using AutoDock, water molecules, hydrogen, and charges were removed, and the PDBQT format was saved. Subsequently, AutoDock Vina was used to performed molecular docking. Optimal docking score combinations were visualized by PyMOL 2.4. 3 Results 3.1 Bioactive ingredients in SHP Combining TCMSP and TCMID databases with literature search, 82 bioactive ingredients ([65]Table 2) of 10 herbs in SHP were obtained. Among them, the active ingredients of 5 herbs (Chebulae Fructus, Carthami Flos, Canavaliae Semen, Alpinia Katsumadai Hayat, and S. petiolata) were obtained from the TCMSP and TCMID databases, while the active ingredients of the remaining 5 herbs were identified through the literature search. Notably, despite madder diester and n-butanol with OB < 30% and DL < 0.18, they still showed a high content and potential anti-nephritis value, making them as active ingredients. Cytoscape software was then used to construct a compositional network diagram, and 75 active ingredients were gained after removing duplicate ingredients of this herb ([66]Figure 2). TABLE 2. The basic information list of chemical constituents in SHP. Number Herbs Mol ID Molecule name MW OB (%) DL 1 Daodou MOL000359 sitosterol 414.79 36.91 0.75 2 Daodou MOL000449 Stigmasterol 412.77 43.83 0.76 3 Honghua MOL001771 poriferast-5-en-3beta-ol 414.79 36.91 0.75 4 Honghua MOL002680 Flavoxanthin 584.96 60.41 0.56 5 Honghua MOL002694 4-[(E)-4-(3,5-dimethoxy-4-oxo-1-cyclohexa-2,5-dienylidene)but-2-enylide ne]-2,6-dimethoxycyclohexa-2,5-dien-1-one 356.4 48.47 0.36 6 Honghua MOL002695 lignan 458.55 43.32 0.65 7 Honghua MOL002698 lupeol-palmitate 665.26 33.98 0.32 8 Honghua MOL002706 Phytoene 545.04 39.56 0.5 9 Honghua MOL002707 phytofluene 543.02 43.18 0.5 10 Honghua MOL002710 Pyrethrin II 372.5 48.36 0.35 11 Honghua MOL002712 6-Hydroxykaempferol 302.25 62.13 0.27 12 Honghua MOL002714 baicalein 270.25 33.52 0.21 13 Honghua MOL002717 qt_carthamone 286.25 51.03 0.2 14 Honghua MOL002719 6-Hydroxynaringenin 288.27 33.23 0.24 15 Honghua MOL002721 quercetagetin 318.25 45.01 0.31 16 Honghua MOL002757 7,8-dimethyl-1H-pyrimido [5,6-g]quinoxaline-2,4-dione 242.26 45.75 0.19 17 Honghua MOL002773 beta-carotene 536.96 37.18 0.58 18 Honghua MOL002776 Baicalin 446.39 40.12 0.75 19 Honghua MOL000358 beta-sitosterol 414.79 36.91 0.75 20 Honghua MOL000422 kaempferol 286.25 41.88 0.24 21 Honghua MOL000006 luteolin 286.25 36.16 0.25 22 Honghua MOL000953 CLR 386.73 37.87 0.68 23 Honghua MOL000098 quercetin 302.25 46.43 0.28 24 Honghua MOL000449 Stigmasterol 412.77 43.83 0.76 25 Hezi MOL001002 ellagic acid 302.2 43.06 0.43 26 Hezi MOL002276 Sennoside E_qt 524.5 50.69 0.61 27 Hezi MOL006376 7-Dehydrosigmasterol 414.79 37.42 0.75 28 Hezi MOL006826 chebulic acid 356.26 72 0.32 29 Hezi MOL009135 ellipticine 246.33 30.82 0.28 30 Hezi MOL009136 Peraksine 310.43 82.58 0.78 31 Hezi MOL009137 (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol 324.46 55.88 0.4 32 Hezi MOL009149 Cheilanthifoline 325.39 46.51 0.72 33 Doukou MOL000224 (4E,6E)-1,7-bis(3,4-dihydroxyphenyl)hepta-4,6-dien-3-one 326.37 33.06 0.31 34 Doukou MOL000228 (2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one 270.3 55.23 0.2 35 Doukou MOL000230 Pinocembrin 270.3 57.56 0.2 36 Doukou MOL000235 1,7-diphenyl-3,5-dihydroxy-1-heptene 282.41 49.01 0.18 37 Doukou MOL000238 1,7-diphenyl-5-hydroxy-6-hepten-3-one 280.39 32.65 0.18 38 Doukou MOL000239 Jaranol 314.31 50.83 0.29 39 Doukou MOL000242 7-O-Methyleriodictyol 302.3 56.56 0.27 40 Doukou MOL000243 alpinolide peroxide 282.37 87.67 0.19 41 Doukou MOL000258 dehydrodiisoeugenol 312.39 56.84 0.29 42 Doukou MOL000260 5-[(2R,3R)-7-methoxy-3-methyl-5-[(E)-prop-1-enyl]-2,3-dihydrobenzofuran -2-yl]-1,3-benzodioxole 324.4 65.55 0.4 43 Doukou MOL000006 luteolin 286.25 36.16 0.25 44 Doukou MOL000098 quercetin 302.25 46.43 0.28 45 Zhangyacai MOL003137 Leucanthoside 462.44 32.12 0.78 46 Zhangyacai MOL005530 Hydroxygenkwanin 300.28 36.47 0.27 47 Zhangyacai MOL005573 Genkwanin 284.28 37.13 0.24 48 Zhangyacai MOL005575 Gentiacaulein 288.27 72.82 0.27 49 Zhangyacai MOL007957 Swertiaperennin 288.27 96.85 0.27 50 Zhangyacai MOL007960 8-hydroxy-1,2,6-trimethoxy-xanthone 302.3 77.13 0.3 51 Zhangyacai MOL007962 1,7- dihydroxy-3,5-dimethoxy xanthone 288.27 103.37 0.27 52 Zhangyacai MOL007963 1-hydroxy-2,3,5-trimethoxy-xanthone 302.3 101.06 0.3 53 Zhangyacai MOL007966 1-Hydroxy-2,3,4,7-tetramethoxyxanthone 332.33 88.86 0.37 54 Zhangyacai MOL007967 1-hydroxy-2,3,5,7-tetramethoxyxanthone 332.33 97.52 0.37 55 Zhangyacai MOL007968 norbellidifolin 262.23 58.82 0.22 56 Zhangyacai MOL007970 5,8-Dimethylbellidifolin 302.3 99.75 0.3 57 Zhangyacai MOL007972 8-hydroxypinoresinal 374.42 71.09 0.55 58 Zangqiancao MOL006160 Alizarin 240.22 32.67 0.19 59 Zangqiancao MOL005638 Mollugin 284.33 42.34 0.26 60 Zangqiancao — Rubidate — — — 61 Zangqiancao MOL006139 1,3-dimethoxy-2-carboxyanthraquinone 312.29 102.89 0.33 62 Zangqiancao MOL006153 2′-hydroxymollugin 302.35 40.5 0.29 63 Shanfanye MOL000006 luteolin 286.25 36.16 0.25 64 Shanfanye MOL000028 α-Amyrin 426.8 39.51 0.76 65 Shanfanye MOL000211 Mairin 456.78 55.38 0.78 66 Shanfanye MOL000422 kaempferol 286.25 41.88 0.24 67 Shanfanye MOL000098 quercetin 302.25 46.43 0.28 68 Zhaxungao MOL007115 manool 304.57 45.04 0.2 69 Zhaxungao — HUMIC ACID — — — 70 Zhaxungao MOL002943 1-Butanol 74.14 22.02 0 71 Zicaorong — valerenic acid — — — 72 Zicaorong — Erythrolaccin — — — 73 Zicaorong — deoxyerythrolaccin — — — 74 Zicaorong — Aloesaponarin II — — — 75 Yuanbaigao MOL000422 kaempferol 286.25 41.88 0.24 76 Yuanbaigao MOL013083 Skimmin (8CI) 324.31 38.35 0.32 77 Yuanbaigao MOL000492 (+)-catechin 290.29 54.83 0.24 78 Yuanbaigao MOL002840 Cryptopimaric acid 302.5 39.58 0.28 79 Yuanbaigao MOL002222 sugiol 300.48 36.11 0.28 80 Yuanbaigao MOL001951 Bergaptin 338.43 41.73 0.42 81 Yuanbaigao MOL000392 formononetin 268.28 69.67 0.21 82 Yuanbaigao MOL004564 Kaempferid 300.28 73.41 0.27 [67]Open in a new tab MW: molecule weight; OB(%): oral bioavailability; DL: drug-like properties. FIGURE 2. [68]FIGURE 2 [69]Open in a new tab Herb-ingredient network. Purple represents the herb and green represents the active ingredient (Mol ID) of traditional Chinese medicine. 3.2 Acquisition of common targets of SHP and nephritis A total of 861 SHP-related targets were obtained based on the TCMSP database. And after the deduplication of the targets corresponding to 75 ingredients of SHP, we acquired 359 SHP-related targets. According to the Pubmed, OMIM and GeneCards databases, a total of 1925 targets related to nephritis were obtained. Ultimately, inputting 359 SHP targets and 1925 nephritis targets into Venny 2.1 software, a total of 140 targets common to both SHP and nephritis were found, and a Venn diagram was established ([70]Table 3; [71]Figure 3). TABLE 3. A list of basic information about the common targets of SHP and nephritis. Number Target name Gene symbol 1 Retinoic acid receptor RXR-alpha RXRA 2 Prostaglandin G/H synthase 1 PTGS1 3 Prostaglandin G/H synthase 2 PTGS2 4 Urokinase-type plasminogen activator PLAU 5 Estrogen receptor ESR1 6 Nitric oxide synthase, inducible NOS2 7 RAC-alpha serine/threonine-protein kinase AKT1 8 Vascular endothelial growth factor A VEGFA 9 72 kDa type IV collagenase MMP2 10 Caveolin-1 CAV1 11 Transforming growth factor beta-1 TGFB1 12 E-selectin SELE 13 Interleukin-6 IL6 14 Nitric oxide synthase, endothelial NOS3 15 Plasminogen activator inhibitor 1 SERPINE1 16 Collagen alpha-1(I) chain COL1A1 17 Cyclin-dependent kinase inhibitor 1 CDKN1A 18 Matrix metalloproteinase-9 MMP9 19 Interleukin-10 IL10 20 Tumor necrosis factor TNF 21 Caspase-3 CASP3 22 Peroxisome proliferator-activated receptor gamma PPARG 23 Intercellular adhesion molecule 1 ICAM1 24 Induced myeloid leukemia cell differentiation protein Mcl-1 MCL1 25 Interferon gamma IFNG 26 Glutathione S-transferase P GSTP1 27 CD40 ligand CD40LG 28 Hepatocyte growth factor receptor MET 29 Apoptosis regulator Bcl-2 BCL2 30 Apoptosis regulator BAX BAX 31 Interleukin-1 beta IL1B 32 C-C motif chemokine 2 CCL2 33 Vascular cell adhesion protein 1 VCAM1 34 Interleukin-8 CXCL8 35 Myeloperoxidase MPO 36 Nuclear factor erythroid 2-related factor 2 NFE2L2 37 C-reactive protein CRP 38 C-X-C motif chemokine 10 CXCL10 39 Osteopontin SPP1 40 Glutathione S-transferase Mu 1 GSTM1 41 Leukocyte elastase SERPINB1 42 Matrix metalloproteinase 1 MMP1 43 Matrix metalloproteinase 7 MMP7 44 Matrix metalloproteinase 12 MMP12 45 Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoform PIK3CG 46 Carbonic anhydrase II CA2 47 Tyrosine-protein kinase receptor UFO AXL 48 Vascular endothelial growth factor receptor 2 KDR 49 Tyrosine-protein kinase SRC SRC 50 Beta-glucuronidase GUSB 51 Glutathione S-transferase A1 GSTA1 52 Focal adhesion kinase 1 PTK2 53 Androgen receptor AR 54 Trypsin-1 PRSS1 55 Dipeptidyl peptidase IV DPP4 56 Caspase-9 CASP9 57 Mitogen-activated protein kinase 1 MAPK1 58 Cellular tumor antigen p53 TP53 59 NF-kappa-B inhibitor alpha NFKBIA 60 Xanthine dehydrogenase/oxidase XDH 61 DNA topoisomerase 1 TOP1 62 E3 ubiquitin-protein ligase Mdm2 MDM2 63 Proliferating cell nuclear antigen PCNA 64 Heme oxygenase 1 HMOX1 65 Baculoviral IAP repeat-containing protein 5 BIRC5 66 Interleukin-2 IL2 67 G2/mitotic-specific cyclin-B1 CCNB1 68 Interleukin-4 IL4 69 Insulin receptor INSR 70 Serotonin transporter SLC6A4 71 Cytochrome P450 2C19 CYP2C19 72 Butyrylcholinesterase BCHE 73 Cytochrome P450 17A1 CYP17A1 74 Nuclear receptor ROR-gamma RORC 75 Fatty acid-binding protein, liver FABP1 76 Phospholipase A2 group 1B PLA2G1B 77 CD81 antigen CD81 78 UDP-glucuronosyltransferase 2B7 UGT2B7 79 Mitogen-activated protein kinase 8 MAPK8 80 Signal transducer and activator of transcription 1-alpha/beta STAT1 81 Cytochrome P450 3A4 CYP3A4 82 Cytochrome P450 1A2 CYP1A2 83 Cytochrome P450 1A1 CYP1A1 84 Matrix metalloproteinase 3 MMP3 85 Epidermal growth factor receptor EGF 86 ETS domain-containing protein Elk-1 ELK1 87 Ornithine decarboxylase ODC1 88 Caspase-8 CASP8 89 Superoxide dismutase [Cu-Zn] SOD1 90 Protein kinase C alpha type PRKCA 91 Hypoxia-inducible factor 1-alpha HIF1A 92 Myc proto-oncogene protein MYC 93 Tissue factor F3 94 Protein kinase C beta type PRKCB 95 Heat shock protein beta-1 HSPB1 96 Tissue-type plasminogen activator PLAT 97 Thrombomodulin THBD 98 Interleukin-1 alpha IL1A 99 Neutrophil cytosol factor 1 NCF1 100 Poly [ADP-ribose] polymerase 1 PARP1 101 C-X-C motif chemokine 11 CXCL11 102 C-X-C motif chemokine 2 CXCL2 103 Inhibitor of nuclear factor kappa-B kinase subunit alpha CHUK 104 Cathepsin D CTSD 105 Interferon regulatory factor 1 IRF1 106 Receptor tyrosine-protein kinase erbB-3 ERBB3 107 Serum paraoxonase/arylesterase 1 PON1 108 Muscarinic acetylcholine receptor M3 CHRM3 109 Protein kinase C delta PRKCD 110 Heparin cofactor 2 SERPIND1 111 Transcription factor Jun JUN 112 Cyclin-dependent kinase 1 CDK1 113 Polyunsaturated fatty acid 5-lipoxygenase ALOX5 114 Tyrosine-protein phosphatase non-receptor type 11 PTPN11 115 Catalase CAT 116 Mu-type opioid receptor OPRM1 117 Solute carrier family 22 member 12 SLC22A12 118 Casein kinase II subunit alpha CSNK2A1 119 Neutrophil elastase ELANE 120 Tyrosine-protein kinase Lck LCK 121 Tyrosine-protein kinase SYK SYK 122 Transthyretin TTR 123 Cystic fibrosis transmembrane conductance regulator CFTR 124 ATP-dependent translocase ABCB1 ABCB1 125 Maltase-glucoamylase MGAM 126 Amyloid-beta precursor protein APP 127 ADP-ribosyl cyclase/cyclic ADP-ribose hydrolase 1 CD38 128 Macrophage migration inhibitory factor MIF 129 Vasopressin V2 receptor AVPR2 130 Thrombin F2 131 Insulin-like growth factor-binding protein 2 IGFBP2 132 C-X-C chemokine receptor type 1 CXCR1 133 Immunoglobulin heavy constant gamma 1 IGHG1 134 Leukotriene A-4 hydrolase LTA4H 135 Aldo-keto reductase family 1 member B1 AKR1B1 136 Protein c-Fos FOS 137 Cyclin-dependent kinase inhibitor 2A CDKN2A 138 Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN PTEN 139 Collagen alpha-1 COL3A1 140 Growth arrest-specific protein 6 GAS6 [72]Open in a new tab FIGURE 3. FIGURE 3 [73]Open in a new tab Venn diagram of the common targets of SHP and nephritis. 3.3 Construction and analysis of PPI network The PPI network, containing 140 nodes and 2,660 edges, was constructed using the STRING database and visualized by the Cytoscape software ([74]Figure 4). In this network, the node represented the active compound of SHP and the compound-related target, and the edge represented interactions among active compounds and target proteins. Additionally, the node size is proportional to the target degree value. The higher the degree value, the larger the node and the more important it is. Afterwards, targets were sorted according to degree values, and the first 10 key targets with the highest degree value were listed in [75]Table 4. The first 10 targets were used to identify the active ingredients of herbs they corresponded to, and TNF, AKT1 and PTGS2 with a largest number of corresponding active ingredients were regarded as key targets in the study. Meanwhile, the top three components corresponding to abundant core targets were quercetin, kaempferol and luteolin, which were the main active components. FIGURE 4. [76]FIGURE 4 [77]Open in a new tab PPI network. Nodes are colored from light to dark. The darker the color is, the larger the degree value of the target is, and the more important the target is in the network. TABLE 4. Ten important protein targets with top degree of PPI network. Number Protein target name Degree 1 TNF 108 2 IL6 107 3 AKT1 100 4 VEGFA 96 5 TP53 96 6 IL1B 92 7 JUN 88 8 MMP9 86 9 PTGS2 82 10 CASP3 82 [78]Open in a new tab 3.4 Construction of herb-compound-target-nephritis network As illustrated in [79]Figure 5, an herb-compound-target-nephritis network was constructed to provide a clearing visualization of the relationship among herbs, ingredients, targets and nephritis. In this network, green denotes herbs, orange denotes ingredients, blue denotes targets, and red denotes diseases. FIGURE 5. [80]FIGURE 5 [81]Open in a new tab Herb-ingredient-target-nephritis network. Green represents herbs, orange represents ingredients, blue represents targets, and red represents diseases. 3.5 GO and KEGG analyses GO functional enrichment analysis of target genes in the PPI network was performed using the Bioinformatics website, and a total of 2,163 entries (2,014 of the BP category, 61 of the CC category, and 143 of the MF category) were provided. The top 10 significant enrichment results of each category are shown in [82]Figure 6. The BP entries were mainly related to cell responses to chemical/oxidative stress and lipopolysaccharide. The CC entries focused on membrane rafts, membrane microdomains and membrane regions. The MF entries covered cytokine receptor binding, cytokine activity, and signaling receptor activator activity. Subsequently, the KEGG pathway enrichment analysis was conducted on 140 common targets, and 186 regulated pathways ([83]Figure 7), such as the AGE-RAGE signaling pathway in diabetic complications, the IL-17 signaling pathway and the TNF signaling pathway, were identified. In addition, a target pathway network was established based on the first 10 paths in [84]Figure 7, as shown in [85]Figure 8. FIGURE 6. [86]FIGURE 6 [87]Open in a new tab GO analysis of common targets. The y-axis shows three categories associated with targets, such as “Biological Process” (BP) categories, “Cellular component” (CC) categories, and “Molecular function” (MF) categories; the x-axis shows the enrichment scores of these terms. The length of the column reflects p-value [-log10 (p-value)]. FIGURE 7. [88]FIGURE 7 [89]Open in a new tab Enrichment analysis of KEGG pathway in SHP anti-nephritis. The area of the bubble represents the number of enriched genes in the pathway, and the color of the bubble represents the size of the p-value. FIGURE 8. [90]FIGURE 8 [91]Open in a new tab Target-pathway network. Green represents the pathway. The purple circle is the target. Hsa04933: AGE-RAGE signaling pathway in diabetic complications; hsa05418: Fluid shear stress and atherosclerosis; hsa05417: Lipid and atherosclerosis; hsa05161: Hepatitis B; hsa04657: IL-17 signaling pathway; hsa04668: TNF signaling pathway; hsa05205: Proteoglycans in cancer; hsa04625: C-type lectin receptor signaling pathway; hsa05167: Kaposi sarcoma-associated herpesvirus infection; and hsa05142: Chagas disease. 3.6 Molecular docking of ingredients and targets The binding interactions between main active ingredients (quercetin, kaempferol and luteolin) and key targets (TNF, AKT1 and PTGS2) was verified by molecular docking. The molecular docking binding performance is presented in [92]Table 5. The interaction patterns of these three ingredients with key targets are shown in [93]Figure 9. It is generally believed that binding energy less than −4.25 kcal/mol, −5.0 kcal/mol or −7.0 kcal/mol indicates certain, good or strong binding activity between ligand and receptor, respectively. Therefore, our results indicated a stable complex consisting of quercetin, kaempferol and luteolin, and these three ingredients all showed a strong binding activities with TNF, AKT1 and PTGS2 (binding energy < −6.9 kcal/mol). TABLE 5. The binding energy values of quercetin, kaempferol and luteolin with TNF, AKT1 and PTGS2. Molecule ID Compound Target protein PDB identifier Estimated ΔG (kcal/mol) MOL000098 Quercetin TNF 7jra −6.9 AKT1 3os5 −7.5 PTGS2 5f19 −7.1 MOL000422 Kaempferol TNF 7jra −7.3 AKT1 3os5 −7.6 PTGS2 5f19 −8.4 MOL000006 Luteolin TNF 7jra −7.4 AKT1 3os5 −6.8 PTGS2 5f19 −9.6 [94]Open in a new tab FIGURE 9. [95]FIGURE 9 [96]Open in a new tab Panel (A–I) shows the molecular docking diagram of quercetin, kaempferol and luteolin with TNF, AKT1 and PTGS2. 4 Discussion Nephritis is characterized by various pathological forms and clinically presents with albuminuria, hematuria, hypertension and edema. If left untreated, it can lead to renal shrinkage and decreased function. Therefore, it is urgent to develop safer and more effective anti-nephritis drugs ([97]Chadban and Atkins, 2005). In this study, the active components of SHP and its anti-nephritis molecular mechanism were explored by network pharmacology. In this study, quercetin, kaempferol and luteolin had higher target frequency, suggesting that they played a central role in the treatment of nephritis. Quercetin, with anti-inflammatory, antioxidant and neuroprotective propertie, is a natural flavonoid found in a wide range of fruits, herbs and vegetabless ([98]Shen, Lin et al., 2021). A study has suggested that quercetin can help ameliorate lupus nephritis (LN)-associated renal fibrosis and inflammation ([99]Chen, Chiang et al., 2022). Kaempferol is a dietary flavonoid existed in various plants ([100]Wong, Chin et al., 2019) and has been explored to have protective effects on the kidneys of rats with radiation nephritis ([101]Mostafa, Edmond et al., 2022). Luteolin is another kind of flavonoids commonly found in medicinal plants, and exhibits a strong anti-inflammatory activity both in vitro and in vivo ([102]Aziz, Kim et al., 2018). Kin et al. had probed that luteolin may be able to mitigate kidney inflammation and interstitial fibrosis ([103]Kim, Kim et al., 2016). Collectively, quercetin, kaempferol and luteolin in SHP may all play a role in the treatment of nephritis. By mapping SHP and nephritis-related targets, 140 shared genes between SHP and nephritis were detected. In order to further understand the interaction among these gene-encoded proteins, a PPI network was constructed. The results showed that TNF, AKT1 and PTGS2 were the main targets of SHP in treating nephritis, which was consistent with previous reports. The TNF gene encodes a multifunctional proinflammatory cytokine belonging to the tumor necrosis factor superfamily ([104]Wu, Wen et al., 2020). Studies ([105]Bantis, Heering et al., 2006) have revealed that the G-308A polymorphism of the TNF-α gene is associated with the expression of the −308A allele and the increase of TNF-α production, making TNF a risk factor for membranous glomerulonephritis ([106]Müller, Hoppe et al., 2019). AKT encodes one of three members of the human AKT serine-threonine protein kinase family, and it is commonly referred to as the protein kinases B encoding one of three key component of many signaling pathways. It has been demonstrated that subcellular C5b-9 complex can induce the proliferation of glomerular mesangial cells in rat Thy-1 glomerulonephritis by activating TRAF6-mediated PI3K-dependent AKT1 ([107]Qiu, Zhang et al., 2012). PTGS2 (also known as COX-2), a prostaglandin endoperoxidase, exerts a key effect in prostaglandin biosynthesis. It has been reported that there is a certain relationship between COX-2 inhibitors and acute interstitial nephritis ([108]Albrecht, Giebel et al., 2017). Juan Jin et al. ([109]Jin, Lin et al., 2018) have revealed that the over-expression of COX-2 can lead to renal autophagy and injury. Thus, the regulation of TNF, AKT1 and PTGS2 may contribute to the treatment of nephritis. After identifying the main targets (TNF, AKT1 and PTGS2) of SHP in nephritis treatment, we further conducted a KEGG analysis to reveal the signal pathways of these main targets. The results showed that the AGE-RAGE, IL-17 and TNF signal pathways were the important pathways of SHP acting on nephritis. Studies have shown that the accumulation of AGE and RAGE in the kidneys and other tissues of diabetic patients is related to the development of diabetic nephropathy and vascular diseases ([110]Tanji, Markowitz et al., 2000). Additionally, a study on the pathogenesis of LN has pointed out that AGE-RAGE can regulate high nitrotyrosination in LN, thereby reducing the oxidative stress in LN ([111]Ene, Georgescu et al., 2021). IL-17 has also been recognized as an independent risk factor for LN prognosis and an effective indicator for the clinical diagnosis, treatment and prognosis of LN ([112]Paquissi and Abensur, 2021). Similarly, multiple evidences have suggested that recently discovered T cells (Th17 cells) that produce interleukin 17 (IL-17) are involved in the renal inflammatory cascade associated with glomerulonephritis ([113]Ramani and Biswas, 2016). The TNF signaling pathway is also related to nephritis. For instance, Xiaoping Qing et al. have elucidated that the TNF signaling pathway plays a key role in irreversible LN kidney damage ([114]Qing, Chinenov et al., 2018). Moreover, it has been found that TNF-α production in T lymphocytes alleviates NTN-induced kidney injury and fibrosis by inhibiting renal T helper 17 lymphocyte response and neutrophil infiltration ([115]Wen, Rudemiller et al., 2020). In conclusion, we speculateed that SHP may ameliorate nephritis by regulating the AGE-RAGE, IL-17 and TNF signaling pathways. To further verify the relationship between active ingredients (quercetin, kaempferol and luteolin) and key targets (TNF, AKT1 and PTGS2), we carried out molecular docking. The results showed that the binding energies of quercetin and luteolin with TNF, AKT1 and PTGS2 were lower than −5.0 kJ/mol, indicating the potential for forming an effective and stable complex between the ligand and receptor. In short, the core active compounds in SHP appeared to regulate SHP-related pathways by acting on the important genes linked to nephritis, thus offering therapeutic value for nephritis treatment. However, our research has some limitations. Bioactive ingredients of SHP were screened only from existing public databases and literatures, rather than using mass spectrometry and other methods. Additionally, there is a lack of animal experiments and clinical trials to verify our findings. To further improve our research, more animal experiments and clinical trials will be conducted in the future. 5 Conclusion In this study, we combined network pharmacology and molecular docking to explore the mechanism by which SHP exerts its anti-nephritis effects. We found that quercetin, kaempferol and luteolin are likely the main active compounds of SHP responsible for its therapeutic effects against nephritis. Moreover, SHP can target the expression of TNF, AKT1 and PTGS2 via the AGE-RAGE, IL-17 and TNF signaling pathways. Overall, although more researches are needed to clarify the exact mechanism, this study provides a valuable insight into the application of SHP for nephritis treatment and the potential for future anti-nephritis drug development. Acknowledgments