Active compounds in ZXF.
After the collection and screening in the TCMSP database, a total of 16 active compounds were obtained in ZXF, including 6 Rhubarb, 3 Red Ginseng, 7 Panax Notoginseng, and 4 Chuanxiong Rhizoma. Three of sixteen active compounds were shared by two or three Chinese herbs, such as beta-sitosterol, ginsenoside rh2, Mandenol. The detailed information was shown in Table 1.
Table 1
Information on the active compounds in Zhongfeng Xingnao Formula
ID
|
Mol. ID
|
Chemical name
|
OB(%)
|
DL
|
Herbs
|
A
|
MOL000358
|
beta-sitosterol
|
36.91
|
0.75
|
HX, SQ, DH
|
B
|
MOL005344
|
ginsenoside rh2
|
36.32
|
0.56
|
HS, SQ
|
C
|
MOL001494
|
Mandenol
|
42
|
0.19
|
CX, SQ
|
CX1
|
MOL002135
|
Myricanone
|
40.6
|
0.51
|
CX
|
CX2
|
MOL002157
|
wallichilide
|
42.31
|
0.71
|
CX
|
CX3
|
MOL000359
|
Sitosterol
|
36.91
|
0.75
|
CX
|
DH1
|
MOL002235
|
EUPATIN
|
50.8
|
0.41
|
DH
|
DH2
|
MOL002268
|
Rhein
|
47.07
|
0.28
|
DH
|
DH3
|
MOL002281
|
Toralactone
|
46.46
|
0.24
|
DH
|
DH4
|
MOL002297
|
Daucosterol_qt
|
35.89
|
0.7
|
DH
|
DH5
|
MOL000471
|
aloe-emodin
|
83.38
|
0.24
|
DH
|
HS1
|
MOL002032
|
DNOP
|
40.59
|
0.4
|
HS
|
SQ1
|
MOL001792
|
DFV
|
32.76
|
0.18
|
SQ
|
SQ2
|
MOL002879
|
Diop
|
43.59
|
0.39
|
SQ
|
SQ3
|
MOL000449
|
Stigmasterol
|
43.83
|
0.76
|
SQ
|
SQ4
|
MOL000098
|
Quercetin
|
46.43
|
0.28
|
SQ
|
Putative targets of ZXF.
A total of 47 potential targets of Rhubarb, 36 potential targets of Red Ginseng, 154 potential targets of Panax Notoginseng, and 23 potential targets of Chuanxiong Rhizoma were obtained from the prediction of the TCMSP database and the conversion of the UniProt database. Among them, 32 potential targets were shared by two or more active compounds. Consequently there were 166 potential targets of ZXF in total for the following study.
Potential targets of ICH.
By searching the above databases, 36 potential targets were from OMIM database, while 1215 potential targets were from GeneCards database, and 24 potential targets were from DrugBank database. After screening, sorting, checking, deduplicating, and unifying the search results, a total of 1258 potential targets related to ICH were collected.
Overlapping targets of ZXF and ICH.
In the Venn diagram, there were 166 ZXF-related targets, 1258 ICH-related targets and 87 overlapping targets in total. It was worth mentioning that the 87 overlapping targets may be the potential targets of ZXF against ICH, which may play a critical role in the treatment of ICH (Fig. 2 and Table 2).
Table 2
Potential targets of Zhongfeng Xingnao Formula against intracerebral hemorrhage
NO.
|
Gene
|
NO.
|
Gene
|
NO.
|
Gene
|
NO.
|
Gene
|
1
|
MMP2
|
23
|
CHRM3
|
45
|
RUNX2
|
67
|
IL2
|
2
|
PLAU
|
24
|
IGFBP3
|
46
|
PGR
|
68
|
ERBB3
|
3
|
NOS2
|
25
|
PTGS1
|
47
|
CHRM2
|
69
|
ERBB2
|
4
|
CCNB1
|
26
|
F3
|
48
|
ODC1
|
70
|
FOS
|
5
|
BIRC5
|
27
|
JUN
|
49
|
CXCL10
|
71
|
IFNG
|
6
|
PON1
|
28
|
ESR2
|
50
|
SERPINE1
|
72
|
ICAM1
|
7
|
CHEK1
|
29
|
CD40LG
|
51
|
VCAM1
|
73
|
BCL2L1
|
8
|
F7
|
30
|
RASSF1
|
52
|
TP53
|
74
|
CHEK2
|
9
|
CASP8
|
31
|
CYP3A4
|
53
|
CASP9
|
75
|
RELA
|
10
|
CHRM1
|
32
|
CYP1B1
|
54
|
CDKN1A
|
76
|
HIF1A
|
11
|
PPARG
|
33
|
PRSS1
|
55
|
RASA1
|
77
|
MAP2
|
12
|
CRP
|
34
|
MMP9
|
56
|
AKT1
|
78
|
NOS3
|
13
|
GSTP1
|
35
|
TOP1
|
57
|
IL1B
|
79
|
CASP3
|
14
|
CCL2
|
36
|
STAT1
|
58
|
IL6
|
80
|
ADRA1B
|
15
|
EGF
|
37
|
IL1A
|
59
|
MPO
|
81
|
NFE2L2
|
16
|
CXCL8
|
38
|
ADRA1A
|
60
|
SELE
|
82
|
ADCYAP1
|
17
|
KDR
|
39
|
HMOX1
|
61
|
MMP3
|
83
|
PTGS2
|
18
|
PCNA
|
40
|
CASP1
|
62
|
SPP1
|
84
|
MAPK14
|
19
|
THBD
|
41
|
NR3C1
|
63
|
CHRM4
|
85
|
PLAT
|
20
|
IL10
|
42
|
MAPK1
|
64
|
IGF2
|
86
|
MYC
|
21
|
CCND1
|
43
|
ESR1
|
65
|
VEGFA
|
87
|
RB1
|
22
|
TNF
|
44
|
EGFR
|
66
|
GSK3B
|
|
|
GO and KEGG Enrichment Analysis.
In the GO enrichment analysis, 87 potential targets were significantly enriched in 1782 BP terms, 41 CC terms, 112 MF terms (P.adjust༜0.05). The terms were sorted in descending order from top to bottom by the gene ratio. As was shown in Fig. 3, receptor ligand activity (P.adjust = 1.02E− 07), signaling receptor activator activity (P.adjust = 1.02E− 07), and cytokine receptor binding (P.adjust = 2.65E− 09) were three most highly enriched GOMF terms. Among the 41 GOCC terms, membrane raft (P.adjust = 1.34E− 07), membrane microdomain (P.adjust = 1.34E− 07), and membrane region (P.adjust = 1.40E− 07) were three most closely related to cell component of ICH. In terms of biological processes, potential targets concentrated more in response to lipopolysaccharide(P.adjust = 5.03E− 23), response to molecule of bacterial origin (P.adjust = 7.15E− 23) and cellular response to drug (P.adjust = 4.80E− 19).
A total of 139 signaling pathways were obtained from the KEGG enrichment analysis (P.adjust༜0.05). Among them, Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, PI3K-Akt signaling pathway, Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection pathway were the most relevant pathway to ZXF against ICH (Fig. 3 and Table 3).
Table 3
Information of signaling pathway obtained from KEGG enrichment analysis
ID
|
Description
|
p.adjust
|
Count
|
hsa05418
|
Fluid shear stress and atherosclerosis
|
1.25E-21
|
24
|
hsa04933
|
AGE-RAGE signaling pathway in diabetic complications
|
2.04E-23
|
23
|
hsa04151
|
PI3K-Akt signaling pathway
|
4.9E-12
|
23
|
hsa05167
|
Kaposi sarcoma-associated herpesvirus infection
|
4.41E-16
|
22
|
hsa05163
|
Human cytomegalovirus infection
|
9.97E-14
|
21
|
hsa05161
|
Hepatitis B
|
2.52E-15
|
20
|
hsa05205
|
Proteoglycans in cancer
|
1.91E-13
|
20
|
hsa04010
|
MAPK signaling pathway
|
6.47E-11
|
20
|
hsa04668
|
TNF signaling pathway
|
5.65E-17
|
19
|
hsa04657
|
IL-17 signaling pathway
|
5.65E-17
|
18
|
hsa05160
|
Hepatitis C
|
2.66E-13
|
18
|
hsa05164
|
Influenza A
|
9.06E-13
|
18
|
hsa05215
|
Prostate cancer
|
1.73E-15
|
17
|
hsa05142
|
Chagas disease
|
3.29E-15
|
17
|
hsa04218
|
Cellular senescence
|
2.32E-12
|
17
|
hsa05169
|
Epstein-Barr virus infection
|
9.58E-11
|
17
|
hsa05166
|
Human T-cell leukemia virus 1 infection
|
3.39E-10
|
17
|
hsa05165
|
Human papillomavirus infection
|
1.21E-07
|
17
|
hsa05162
|
Measles
|
4.9E-12
|
16
|
hsa05224
|
Breast cancer
|
1.08E-11
|
16
|
Drug-compound-target-pathway network
In the drug-compound-target-pathway network, 4 red triangle nodes represented the drugs, while 16 green octagon nodes were on behalf of the compounds, 87 orange V-shape nodes were in the name of the potential targets, and the signaling pathways were represented by 20 purple diamond nodes. Altogether, there were 127 nodes in the network and 518 edges in the network, indicating that ZXF treated ICH with multiple compounds, multiple targets and multiple pathways.
As shown by topology analysis, the five compounds with the highest degree were Quercetin, beta-sitosterol, Mandenol, Myricanone, and aloe-emodin, suggesting that these five compounds may be the main compounds for ZXF against ICH. Also, the top five potential targets sorted by degree value were AKT1, RELA, MAPK1, TP53, TNF, indicating that these five targets may be the core targets in treating ICH by ZXF. In addition, the top five signaling pathways with the highest degree value are Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, PI3K-Akt signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Human cytomegalovirus infection, indicating that ZXF treated ICH mainly through these five signaling pathways.
PPI network
As was shown in Fig. 4(A), there were a total of 87 nodes and 1514 edges in the PPI network for potential targets. The average node degree was 34.8. Sorted by the degree value, the top 10 potential targets, considered as core genes, were AKT1 (degree=68), TP53 (degree=68), and VEGFA (degree=68). =67), IL6 (degree=67), CASP3 (degree=66), JUN (degree=64), EGF (degree=64), PTGS2 (degree=63), EGFR (degree=61). Besides, it can be seen from Fig. 4(B) that 10 nodes and 45 edges were included in the PPI network of the core genes with the average node degree was 9.
Molecular docking
As was shown in Table 4, the core targets (AKT1, MAPK1, RELA, TNF, TP53) and active compounds (beta-sitosterol, myricanone, aloe-emodin, quercetin, mandenol) obtained from drug-compound-target-pathway network were chosen to further verify compound-target interaction by molecular docking. In general, the lower the binding affinity was, the more effective the molecular docking was. And the binding affinity less than 5 kcal/mol would be regarded as effective molecular docking. According to the criteria, aloe-emodin, beta-sitosterol and quercetin would be three active compounds in ZXF. Additionally, MAPK1 and TNF may be therapeutic targets for ZXF against ICH. Furthermore, the hydrogen-bonding interaction between receptor (core target) and ligand (active compound) was shown in Fig. 6.
Table 4
Molecular docking of core targets and active compounds
Target
|
PDB ID
|
Affinity (kcal·mol− 1)
|
Aloe_emodin
|
Beta_sitosterol
|
Mandenol
|
Myricanone
|
Quercetin
|
AKT1
|
1UNQ
|
-6.6
|
-7.4
|
-4.3
|
-4.2
|
-6.4
|
MAPK1
|
4IZ5
|
-8.1
|
-9.5
|
-5.0
|
-8.6
|
-8.6
|
RELA
|
1NFI
|
-8.3
|
-7.6
|
-4.8
|
-7.1
|
-8.4
|
TNF
|
5YOY
|
-8.2
|
-9.0
|
-5.5
|
-9.0
|
-8.4
|
TP53
|
3Q05
|
-9.0
|
-7.8
|
-4.9
|
-8.2
|
-9.1
|