Active compound and related targets
There were 187 compounds summarized forming the self-built database of PM through website database and document search. The database mainly includes stilbenes, quinones, flavonoids, phospholipids, phenylpropanoids and others. The compounds of PM are listed in the table S1. After “drug like soft” in FAFDrugs 4, 95 compounds were screened. Then 37 compounds and their predicted targets were obtained sequentially under the condition of probability value >0.25 in STP database. In addition, a total of 1519 diabetes-related targets were acquired through the screening of disease target database. After the intersection of 37 compound-related targets and diabetes-related targets, 63 hypoglycemic targets and 29 related compounds were obtained. The 29 compounds were shown in detail in table 2. The 63 targets were supplied in table S2.
Table 2. Diabetes-related key compounds in PM
No.
|
Compound Name
|
No.
|
Compound Name
|
1
|
Resveratrol
|
16
|
(-)-Epicatechin gallate
|
2
|
Oxyresveratrol
|
17
|
Tricin
|
3
|
Emodin
|
18
|
Apigenin
|
4
|
Aloe-emodin
|
19
|
Kaempferol
|
5
|
Rhein
|
20
|
Quercetin
|
6
|
Citreorosein
|
21
|
Luteolin
|
7
|
Teloschistin
|
22
|
Uridine
|
8
|
Emodin 8-O-β-D-glucopyranoside
|
23
|
Adenosine
|
9
|
Physcion 8-O-glucoside
|
24
|
7-Hydroxy-4-methylcoumarin-5-O-β-D-glucopyranoside
|
10
|
Emodin 1-O-β-D-glucopyranoside
|
25
|
7-Hydroxy-3,4-dimethylcoumarin-5-O-β-D-glucopyranoside
|
11
|
(-)-Catechin
|
26
|
(+)-Catechin 3-O-gallate
|
12
|
(-)-Epicatechin
|
27
|
2,6-Dihydroxybenzoic acid
|
13
|
(-)-Gallocatechin
|
28
|
3,4-Dihydroxybenzoic acid
|
14
|
(-)-Epigallocatechin
|
29
|
Gallic acid
|
15
|
(+)-Catechin gallate
|
|
|
The network of compound-targets
The topological analysis and network diagram of compound-targets were made by using Cytoscape 3.2.1. The degree values of compounds-targets were calculated. The higher the degree is, the closer the relationship between compounds and targets are, and the more important the compounds are in this network. As figure 2(a) shown, the ellipse node represents compound node and the diamond node represents the diabetes disease target. The color of node changed from yellow to red corresponds to degree from small to bigger. The numerical number represents the compound, as shown in table 1. The target labels are indicated by the target symbols. According to the degree of the compounds, the compounds from 2 times of the average degree (17.24) to the maximum degree (43) were screened out, namely resveratrol, apigenin, kaempferol, quercetin and luteolin, respectively, as shown in figure 2(b). What was more, ranking the target degrees, 28 targets from the average degree (3.97) to the maximum degree (15) were listed in table 3.
Table 3. The information of 28 key targets ranking by degree
Target Symbol
|
Uniprot ID
|
Target Symbol
|
Uniprot ID
|
CA2
|
P00918
|
MAOA
|
P21397
|
CA1
|
P00915
|
ABCG2
|
Q9UNQ0
|
CA4
|
P22748
|
ALOX12
|
P18054
|
BACE1
|
P56817
|
ALOX15
|
P16050
|
CA3
|
P07451
|
CD38
|
P28907
|
ESR1
|
P03372
|
ESRRA
|
P11474
|
APP
|
P05067
|
FLT3
|
P36888
|
MMP2
|
P08253
|
GLO1
|
Q04760
|
MAPT
|
P10636
|
GSK3B
|
P49841
|
SYK
|
P43405
|
MMP9
|
P14780
|
ABCC1
|
P33527
|
NOX4
|
Q9NPH5
|
ADORA1
|
P30542
|
PARP1
|
P09874
|
ADORA2A
|
P29274
|
PIM1
|
P11309
|
AKR1B1
|
P15121
|
PTGS2
|
P35354
|
Protein-protein interaction network
The interaction relationship between targets was explored based on the String database. The result was shown in figure 3(a), the target is represented by a circle node. The larger the node is, the higher the degree is and the brighter the color of the node is, the larger the betweenness centrality is. The larger and the brighter color of nodes, the more important the targets are in the hypoglycemia network of PM. The line thickness and the color depth of the nodes represent the size of the edge betweenness value. And the brighter the color of the connection line between the nodes, the thicker the line, indicates the closer the interaction relationship between the targets. At the same time, the module analysis of PPI network was carried out by using Clusterone of Cytoscape 3.2.1. There were four clustering modules with a p-value less than 0.05 among them, indicating that the target proteins in them were more closely related and may jointly perform common biological processes. As figure 3(b) shown, cluster 1 was related to the regulation of intracellular signal transduction, protein kinase B signaling and cell communication. Cluster 2 was linked with protein phosphorylation, phosphate-containing compound metabolic process and regulation of apoptotic process. Cluster 3 was connected with response to oxidative stress, regulation of reactive oxygen species metabolic process. Cluster 4 had a relation to fatty acid derivative metabolic process and oxidation-reduction process. Finally, the hub genes were screened by different algorithms and the results were specially descripted in table 4. And it showed that AKT1, EGFR, ESR1, PTGS2, MMP9, MAPK14, and KDR were the common key targets under different algorithms.
Table 4 The top 10 hub genes ranked with different algorithms
Category
|
Rank Method In CytoHubba
|
MCC
|
MNC
|
Degree
|
EPC
|
Closeness
|
Radiality
|
Gene Symbol
Top 10
|
AKT1
|
AKT1
|
AKT1
|
AKT1
|
AKT1
|
AKT1
|
|
EGFR
|
EGFR
|
EGFR
|
EGFR
|
EGFR
|
EGFR
|
|
PTGS2
|
ESR1
|
ESR1
|
PTGS2
|
PTGS2
|
PTGS2
|
|
MAPK14
|
PTGS2
|
PTGS2
|
ESR1
|
ESR1
|
ESR1
|
|
ESR1
|
MMP9
|
MMP9
|
MAPK14
|
MMP9
|
MMP9
|
|
KDR
|
MAPK14
|
MAPK14
|
MMP9
|
MAPK14
|
MAPK14
|
|
MMP9
|
PIK3CA
|
PIK3CA
|
KDR
|
KDR
|
KDR
|
|
MMP2
|
KDR
|
KDR
|
PIK3CA
|
PIK3CA
|
APP
|
|
MMP3
|
MPO
|
MPO
|
APP
|
APP
|
PIK3CA
|
|
APP
|
PIK3R1
|
PIK3R1
|
IGF1R
|
IGF1R
|
IGF1R
|
Molecular docking results
The top 5 compounds by the degree ranking and the top 10 targets obtained according to the maximal clique centrality (MCC) algorithm were verified by docking systems. As figure 4 showed that all the docking values were all below -73.75 Kal/mol, all of which showed low docking energy occurred between ligands and receptors. The docking energy was detailed listing in table S3. These indicated that the active components of PM could bind to the targets stably and play an effective role in diabetes. Furthermore, based on the average blinding free energy between targets and compounds, the targets ranking from low to high were MMP9, MMP3, PTGS2, AKT1, MAPK14, MMP2, EGFR, KDR, ESR1, APP. And the five compounds with MMP3, MMP9, PTGS2 were all binding with lower docking values. It means that MMP3, MMP9 and PTGS2 may be the important blinding ligands targets related to hypoglycemic effect. And the average docking values between compounds and targets shown that quercetin and luteolin are more lower among the five compounds. It indicates that quercetin and luteolin are more easier to combine with hypoglycemic activity targets.
GO enrichment analysis of key targets
DAVID website was used for GO enrichment analysis of potential targets, and a total of 208 GO terms with P < 0.05 were obtained. Among them, there were 125 entries of biological process (BP), 26 entries of cell composition (CC) and 57 entries of molecular function (MF). Figure 5 listed the top 10 most significantly enriched GO terms respectively. In the biological process category, these targets were mainly concerned with negative regulation of apoptotic process, protein autophosphorylation, transmembrane receptor protein tyrosine kinase signaling pathway, phosphatidylinositol-mediated signaling, response to oxidative stress, monoterpenoid metabolic process, oxidation-reduction process, cellular response to insulin stimulus, inflammatory response. In terms of cell composition, the targets were mainly related to membrane, cytosol, extracellular exosome and endosome. It could be seen from the biological process analysis that the targets mainly involved with enzyme binding, insulin receptor substrate binding, ATP binding, protein serine/threonine kinase activity and so on.
KEGG enrichment analysis
The results of KEGG pathway enrichment analysis showed that 63 hypoglycemic targets of PM were significantly enriched in 67 signaling pathways. Through the diabetes database and literature investigation, 38 pathways (Table S4) were prominently related to the occurrence and development of diabetes. And the main pathways of PM treating diabetes were integrated in figure 6. As shown in figure 7, a bubble diagram was drawn by listing the top 25 signal pathways according to p value. Furthermore, the top 10 signaling pathways and their specific related genes information were shown in table 5. Combined with p value and false discovery rate (FDR), regulation of lipolysis in adipocytes was the most significant hypoglycemic signaling pathway for PM. Particularly PI3K-Akt signaling pathway contained the most key targets.
Experiment results
The inhibitory effect of part of screened compounds on alpha-glucosidase activity was detected to evaluate the hypoglycemic activity. Acarbose (20ug/ml) was selected as the positive control, and the concentration of the compounds to be tested were 10uM to evaluate the inhibition rate on alpha-glucosidase activity. The result was shown in figure 8. The alpha-glucosidase inhibition rate of resveratrol, apigenin, luteolin, quercetin, kaempferol, (-)-epicatechin gallate, (-)-catechin, (-)-epicatechin, gallic acid, emodin, rhein and aloe-emodin were 52.6%, 31.5%, 32.4%, 38.7%, 86.2%, 62.9%, 82.7%, 28.6%, 27.5%, 66%, 16.7%, 10.4% and 12.4%. They all showed great inhibitory activity. Especially for the top 5 compounds , the inhibition rate of resveratrol, apigenin, luteolin, quercetin and kaempferol were all no less than 30%. The experimental results showed that the screened compounds have good hypoglycemic activity, which could be potential active ingredients.
Table 5 The top 10 signaling pathways with related genes
Term
|
Pathway
|
Genes
|
hsa04923
|
Regulation of lipolysis in adipocytes
|
PIK3CG, AKT1, PTGS2, PIK3CB, PTGS1, PIK3CA, ADORA1, INSR, PIK3R1
|
hsa04917
|
Prolactin signaling pathway
|
PIK3CG, AKT1, PIK3CB, MAPK14, GSK3B, ESR1, PIK3CA, STAT1, PIK3R1
|
hsa04668
|
TNF signaling pathway
|
PIK3CG, AKT1, PTGS2, PIK3CB, MAPK14, MMP9, PIK3CA, MMP14, MMP3, PIK3R1
|
hsa04370
|
VEGF signaling pathway
|
PIK3CG, AKT1, PTGS2, PIK3CB, MAPK14, PIK3CA, PIK3R1, KDR
|
hsa04068
|
FoxO signaling pathway
|
PIK3CG, AKT1, EGFR, IGF1R, PIK3CB, MAPK14, PIK3CA, INSR, PIK3R1, CDK2
|
hsa04915
|
Estrogen signaling pathway
|
PIK3CG, AKT1, EGFR, PIK3CB, MMP9, ESR1, PIK3CA, MMP2, PIK3R1
|
hsa00591
|
Linoleic acid metabolism
|
CYP3A4, ALOX15, CYP2C19, CYP2C9, PLA2G1B, CYP1A2
|
hsa04015
|
Rap1 signaling pathway
|
PIK3CG, AKT1, EGFR, IGF1R, ADORA2A, PIK3CB, MAPK14, PIK3CA, INSR, PIK3R1, KDR
|
hsa00590
|
Arachidonic acid metabolism
|
ALOX15, CYP2C19, PTGS2, CYP2C9, PTGS1, PLA2G1B, ALOX12
|
hsa04380
|
Osteoclast differentiation
|
PIK3CG, AKT1, PIK3CB, MAPK14, LCK, PIK3CA, STAT1, PIK3R1, SYK
|