3.1 Active compounds and target genes of DHXD
From TCMSP and TCMID databases, 397 related compounds were identified in DHXD. Among them, there are 287 components (72.3%) in HL and 110 components (27.7%) in DH. After screening by ADME threshold (OB ≥ 30%, DL ≥ 0.18 and Caco-2 > 0), 30 compounds were obtained ( Table 1 ). 197 genes were obtained in this research, including 177 genes from HL, 70 genes from DH.
Table 1
Herb | Mol ID | Molecule Name | OB (%) | DL | Caco-2 |
Coptis chinensis | MOL001454 | Berberine | 4 | 0.57 | 36.86 |
Coptis chinensis | MOL013352 | Obacunone | 7 | -0.43 | 43.29 |
Coptis chinensis | MOL002894 | Berberrubine | 4 | 0.17 | 35.74 |
Coptis chinensis | MOL002897 | epiberberine | 4 | 0.4 | 43.09 |
Coptis chinensis | MOL002903 | (R)-Canadine | 5 | 0.57 | 55.37 |
Coptis chinensis | MOL002904 | Berlambine | 6 | 0.17 | 36.68 |
Coptis chinensis | MOL002907 | Corchoroside A_qt | 6 | -1.31 | 104.95 |
Coptis chinensis | MOL000622 | Magnograndiolide | 4 | -0.24 | 63.71 |
Coptis chinensis | MOL000762 | Palmidin A | 8 | -1.47 | 35.36 |
Coptis chinensis | MOL000785 | palmatine | 4 | 0.37 | 64.6 |
Coptis chinensis | MOL000098 | quercetin | 7 | -0.77 | 46.43 |
Coptis chinensis | MOL001458 | coptisine | 4 | 0.32 | 30.67 |
Coptis chinensis | MOL002668 | Worenine | 4 | 0.24 | 45.83 |
Coptis chinensis | MOL008647 | Moupinamide | 5 | -0.51 | 86.71 |
rhubarb | MOL000096 | (-)-catechin | 6 | -0.78 | 49.68 |
rhubarb | MOL000358 | beta-sitosterol | 1 | 0.99 | 36.91 |
rhubarb | MOL000471 | aloe-emodin | 5 | -1.07 | 83.38 |
rhubarb | MOL000554 | gallic acid-3-O-(6'-O-galloyl)-glucoside | 14 | -2.76 | 30.25 |
rhubarb | MOL002235 | EUPATIN | 8 | -0.26 | 50.8 |
rhubarb | MOL002251 | Mutatochrome | 1 | 0.84 | 48.64 |
rhubarb | MOL002259 | Physciondiglucoside | 15 | -3.43 | 41.65 |
rhubarb | MOL002260 | Procyanidin B-5,3'-O-gallate | 16 | -2.88 | 31.99 |
rhubarb | MOL002268 | rhein | 6 | -0.99 | 47.07 |
rhubarb | MOL002276 | Sennoside E_qt | 9 | -1.56 | 50.69 |
rhubarb | MOL002280 | Torachrysone-8-O-beta-D-(6'-oxayl)-glucoside | 12 | -1.84 | 43.02 |
rhubarb | MOL002281 | Toralactone | 5 | 0.37 | 46.46 |
rhubarb | MOL002288 | Emodin-1-O-beta-D-glucopyranoside | 10 | -2 | 44.81 |
rhubarb | MOL002293 | Sennoside D_qt | 9 | -1.46 | 61.06 |
rhubarb | MOL002297 | Daucosterol_qt | 1 | 1.07 | 35.89 |
rhubarb | MOL002303 | palmidin A | 8 | -1.47 | 32.45 |
3.2 Disease target acquisition
With the key words of "Diabetes Mellitus, Non-Insulin-Dependent", it integrated the disease-related genes obtained from multi-source databases (including GeneCards, DisGenet, UniProt). At last, it identified 3272 related genes, and UniProt database was used to standardize the selected targets.
3.3 Network construction and analysis
Taking the intersection of drug target and diabetes target, 128 overlapping targets were obtained, which were represented by Venn diagram(Fig. 2a). Through the interaction of T2DM, HL and DH targets, it was found that HL alone had 90 related targets for T2DM, suggesting that the main effective components of DHXD in the prevention and treatment of T2DM mostly came from HL. As shown in Fig. 2b, red represented T2DM, yellow represented DH, and blue represented HL. The composition and target information of DH and HL and the related target information of T2DM were made into herb-component-target network diagram, as shown in Fig. 2c, yellow nodes represented HL and DH, green represented active ingredient, purple represents central target, and lines represent their interaction. According to the network analysis, quercetin in HL was considered to be the most effective compound interacting with target genes. Input target genes into the STRING database for PPI network analysis, as shown in Fig. 3a. In this network, there were 126 nodes and 1888 edges in total. When the setting conditions were degree ≥ 17, closeness ≥ 0.459 and betweenness ≥ 0.002, the PPI network with 69 nodes and 1110 edges would be obtained in Fig. 3b. Under the condition of degree ≥ 28, closeness ≥ 0.532 and betweenness ≥ 0.004, the PPI network with 42 nodes and 632 edges remained would be build in Fig. 3c after clustering analysis of these related targets ,in this network, the color and size change of all nodes is based on the pertinency from high to low, the most influential target was VEGFA. The detailed information of 42 genes was shown in Table 2.
Venn diagram and herb-component-target network. a 128 intersection genes; b red, yellow and blue areas represented T2DM, DH and HL respectively; c a complete herb-component-target network
Table 2
Information of 42 targets
Uniprot ID | Gene Symbol | Description |
P05231 | IL6 | Interleukin-6 |
P04637 | TP53 | Cellular tumor antigen p53 |
P15692 | VEGFA | Vascular endothelial growth factor A |
P01375 | TNF | Tumor necrosis factor |
P42574 | CASP3 | Caspase-3 |
P28482 | MAPK1 | Mitogen-activated protein kinase 1 |
P00533 | EGFR | Epidermal growth factor receptor |
P01133 | EGF | Pro-epidermal growth factor |
P35354 | PTGS2 | prostaglandin-endoperoxide synthase 2 |
P14780 | MMP9 | Matrix metalloproteinase-9 |
P03372 | ESR1 | Estrogen receptor |
P01584 | IL1B | Interleukin-1 beta |
P01100 | FOS | Proto-oncogene c-Fos |
P13500 | CCL2 | C-C motif chemokine 2 |
P29474 | NOS3 | Nitric oxide synthase |
P60484 | PTEN | Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN |
P22301 | IL10 | Interleukin-10 |
P37231 | PPARG | Peroxisome proliferator-activated receptor gamma |
P09601 | HMOX1 | Heme oxygenase 1 |
P05121 | SERPINE1 | Plasminogen activator inhibitor 1 |
P05362 | ICAM1 | Intercellular adhesion molecule 1 |
P04626 | ERBB2 | Receptor tyrosine-protein kinase erbB-2 |
Q04206 | RELA | Transcription factor p65 |
P60568 | IL2 | Interleukin-2 |
P10451 | SPP1 | Osteopontin |
P42224 | STAT1 | Signal transducer and activator of transcription 1-alpha/beta |
P05164 | MPO | Myeloperoxidase |
Q16665 | HIF1A | Hypoxia-inducible factor 1-alpha |
Q07817 | BCL2L1 | Bcl-2-like protein 1 |
P35968 | KDR | Vascular endothelial growth factor receptor 2 |
P42771 | CDKN2A | Cyclin-dependent kinase inhibitor 2A |
P02741 | CRP | C-reactive protein |
P08684 | CYP3A4 | Cytochrome P450 family 3 subfamily A member 4 |
Q9UNQ0 | ABCG2 | ATP binding cassette subfamily G member 2 |
P00734 | F2 | Prepro-coagulation factor II |
P00441 | SOD1 | Superoxide dismutase 1 |
P35869 | AHR | Aryl hydrocarbon receptor |
P06401 | PGR | Progesterone receptor |
Q16236 | NFE2L2 | Nuclear factor erythroid 2-related factor 2 |
Q03135 | CAV1 | Caveolin 1 |
P10275 | AR | Androgen receptor |
Q14790 | CASP8 | Caspase 8 |
3.4 Screening and analysis of key genes
Based on 42 genes in Fig. 3c, a target-pathway network was built in Fig. 3d which showed central targets as purple nodes and 6 related pathways as red nodes. There were 11 targets in this network, contained EGFR, MMP9, TP53, MAPK1, TNF, EGF, IL6, CASP3, PTGS2, IL10, VEGFA. In the 6 pathways in the figure, C-type lectin receptor signaling pathway (hsa04625), AGE-RAGE signaling pathway (hsa04933), HIF-1 signaling pathway (hsa04066) were associated with 5 genes respectively, TNF signaling pathway (hsa04668) was associated with 6 genes, IL-17 signaling pathway (hsa04657) was associated with 7 genes, VEGF signaling pathway (hsa04370) was associated with 3 genes.
PPI networks and their analysis. a a PPI network of 128 targets; b a PPI network of 69 targets; c a PPI network of 42 targets; d the target-pathway network based on 42 key targets; e the bubble chart of KEGG enrichment based on 42 key targets
3.5 Enrichment analysis of GO
Biological processes (BP) were sorted in ascending order of logP value, we found that in the top 10 positive regulation of BP were concentrated in vasoconstriction (GO: 0042310), transcription, DNA-templated (GO: 0045893), transcription from RNA polymerase II promoter (GO: 0045944), superoxide anion generation (GO: 0032930), protein phosphorylation (GO: 0006468), nitric oxide biosynthetic process (GO: 0045429), gene expression (GO: 0010628), ERK1 and ERK2 cascade (GO: 0070374), cell proliferation (GO: 0008284) and angiogenesis (GO: 0045766) as shown in Fig. 4a, among these BP, gene expression (GO: 0010628) was the most important. In the top 10 negative regulation of BP were concentrated in transcription from RNA polymerase Ⅱ promoter (GO: 0010553), smooth muscle cell proliferation (GO: 0048662), neuron apoptotic process (GO: 0043524), lipid storage (GO: 0019915), gene expression (GO: 0010629), extrinsic apoptotic signaling pathway via death domain receptors (GO: 1902042), endothelial cell apoptotic process (GO: 2000352), cell proliferation (GO: 0008285), cell growth (GO: 0030308) and the most significant BP: apoptotic process (GO: 0006915) as shown in Fig. 4b. About cell composition (CC), the most influential was extracellular space (GO: 0005615), as shown in Fig. 4c, others in the top 10 were receptor complex (GO: 00043235), plasma membrane (GO: 0005886), perinuclear region of cytoplasm (GO: 0048471), membrane raft (GO: 0045121), extracellular region (GO: 0005576), extracellular matrix (GO: 0031012), extracellular exosome (GO: 0070062), cytosol (GO: 0005829) and caveola (GO: 0005901). In terms of molecular function (MF), enzyme binding (GO: 0019899) was most significant, others in the top 10 were transcription factor binding (GO: 0008134), steroid hormone receptor activity (GO: 0003707), steroid binding (GO: 0005496), RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding (GO: 0004879), protein homodimerization activity (GO: 0042803), protein binding (GO: 0005515), identical protein binding (GO: 0042802), heme binding (GO: 0020037) and drug binding (GO: 0008144) (Fig. 4d).
The bubble chart about whole 128 genes. In these bubble charts, the larger the bubble, the more genes were enriched in the pathway; the redder the color, the smaller the P value, the more significant the result is; Rich factor referred to the ratio of the number of genes belonging to the pathway in the target gene set to the number of genes belonging to the pathway in the background gene set, and the higher the value, the higher the enrichment degree. a Top 10 of positive regulation of GO-BP; b Top 10 of negative regulation of GO-BP; c Top 10 of GO-CC; d Top 10 of GO-MF; e Top 10 of of KEGG-Pathway
3.6 Enrichment analysis of KEGG pathway
By enrichment analysis of KEGG pathway in 128 genes, top 10 pathways were found to be significantly correlated with T2DM, PI3K-Akt signaling pathway (hsa04151) had the most genes and HIF-1 signaling pathway (hsa04066) was the most significant pathway, others were VEGF signaling pathway (hsa04370), Toll-like receptor signaling pathway (hsa04620), TNF signaling pathway (hsa04668), Sphingolipid signaling pathway (hsa04071), p53 signaling pathway (hsa04115), FoxO signaling pathway (hsa04068), Estrogen signaling pathway (hsa04915) and Calcium signaling pathway (hsa04020) (Fig. 4e). After clustering analysis, there were most relevant 6 pathways in 42 genes, especially IL-17 signaling pathway (hsa04657), others were VEGF signaling pathway (hsa04370), TNF signaling pathway (hsa04668), HIF-1 signaling pathway (hsa04066), C-type lectin receptor signaling pathway (hsa04625), AGE-RAGE signaling pathway in diabetic complications (hsa04933) (Fig. 3e).
After labeling the common genes with KEGG-MAPPER, insulin resistance signaling pathway (hsa04931) and HIF-1 signaling pathway (hsa04066) were the most correlated pathways with DHXD, according to KEGG-MAPPER, their most related targets were shown in Fig. 5 and Fig. 6 as red nodes.
HIF-1 signaling pathway influenced by DHXD. The red nodes represented the hub genes.
Insulin resistance signaling pathway influenced by DHXD. The red nodes represented the hub genes.
3.7 Component-target molecule docking of DHXD
INSR and GLUT4, two target genes, showed strong association with other targets, pathways and active components, in this study, we combined them with 5 putative components to test their binding ability as shown in Table 3. According to the binding energy (Δ gbind) of molecular docking results, their binding activities were good (Δ gbind < − 5kj · mol − 1). The docking results of INSR with 5 active components were shown in Fig. 7, Fig. 7a-e represented their action mode, and the highly relevant target-pathway network was presented in Fig. 7f, Among them, HIF-1 signaling pathway (hsa04066) were the most correlated pathway (Fig. 7g) and quercetin (MOL000098) had the strongest binding force with INSR (Fig. 7h). Figure 8a-e meant the action mode of GLUT4 with 5 active ingredients, INSR,VEGFA,PPARG and other targets had been proved to be related again(Fig. 8f), HIF-1 signaling pathway (hsa04066) was also the most significant related pathway(Fig. 8g), berberine (MOL001454) had a strong interaction with GLUT4 (Fig. 8h).
Table 3
Compounds- targets docking score of DHXD
Mol ID | Compounds | GLUT4 | INSR | Herb |
MOL000098 | Quercetin | -8.9 | -7.6 | Coptis chinensis |
MOL000358 | Beta-sitosterol | -6.7 | -7.4 | rhubarb |
MOL000471 | Aloe-emodin | -8.5 | -7.1 | rhubarb |
MOL002904 | Berlambine | -9.3 | -7.3 | Coptis chinensis |
MOL001454 | Berberine | -10.6 | -7.5 | Coptis chinensis |
Molecular docking results of 5 active compounds with INSR. a action mode of quercetin (MOL000098) with target INSR; b action mode of berberine (MOL001454) with target INSR; c action mode of berlambine (MOL002904) with target INSR; d action mode of beta-sitosterol (MOL000358) with target INSR; e action mode of aloe-emodin (MOL000471) with target INSR; f the relevant target-pathway network; g the bubble chart of KEGG enrichment based on target INSR; h a bar chart of comparison on binding force
Molecular docking results of 5 active compounds with GLUT4. a action mode of berberine (MOL001454) with target GLUT4; b action mode of berlambine (MOL002904) with target GLUT4; c action mode of quercetin (MOL000098) with target GLUT4; d action mode of aloe-emodin (MOL000471) with target GLUT4R; e action mode of beta-sitosterol (MOL000358) with target GLUT4; f the relevant target-pathway network; g the bubble chart of KEGG enrichment based on target GLUT4; h a bar chart of comparison on binding force