3.1. Screening of Active Components and Their Targets of HWD
Retrieved from TCMSP database, there were 173 related components of the whole formula in total by the screen criteria of OB and DL. The active components of HL, ZS, FL, JH, SJ, GC, BX, were 14, 22, 15, 9, 5, 92, 13 respectively. 3 active components of ZR were retrieved from Stich database(ZR is not included in the TCMSP database, only through literature or searching other databases to find the compound of ZR). 134 active components remained after removing no target genes and duplication. Subsequently, we explored the potential targets of the 134 potential pharmacologically active components by excavating TCMSP databases, Stitch database and Swisstargetpredicition database, which yielded to 219 targets after removing duplication(shown in Table S1).
3.2. Excavation of the Core Targets of HWD in Treating MetS
We collected 1728 targets(shown in Table S2) associated with MetS from the GeneCards database and DisGeNET database. The target genes of HWD were compared with the related genes of MetS and 77 overlapping common targets(shown in Table S3) were screened out. To explore the interaction between 77 overlapping common targets, we built a PPI network(Figure 1). The network had 76 nodes(One target had no interaction relationship with other targets, so the target was removed), which interacted with 817 edges, the average node degree is 21.5. As the degree increase, the nodes color deepens and size increase, the combine_score increase, the edges color deepens and thicker increase, which suggest stronger interactions. These results demonstrate that these targets were defined as the candidate targets for HWD in treating MetS.
3.3. Construction of Components-Targets Core Network for HWD in Treating MetS
In order to holistically and systemically obtain comprehensive understanding of the component -target for HWD in treating MetS, a network map was constructed by using Cytoscape. First, we found the corresponding components of 77 core targets. The basic information of the components of HWD is shown in Table 1. Then we used Cytoscape to construct a network map, including 301 edges and 115 nodes (Figure 2). It showed that HWD plays a therapeutic role in MetS mainly through multi-component corresponding to multi-target.
Table 1. active components of HWD.
MOL ID
|
Component name
|
OB%
|
DL
|
Number of targets
|
Herb
|
MOL000098
|
quercetin
|
46.43
|
0.28
|
84
|
GC HL
|
MOL000289
|
pachymic acid
|
33.63
|
0.81
|
1
|
FL
|
MOL000354
|
isorhamnetin
|
49.6
|
0.31
|
5
|
GC
|
MOL000358
|
beta-sitosterol
|
36.91
|
0.75
|
3
|
BX JH
|
MOL000392
|
formononetin
|
69.67
|
0.21
|
5
|
GC
|
MOL000417
|
Calycosin
|
47.75
|
0.24
|
1
|
GC
|
MOL000422
|
kaempferol
|
41.88
|
0.24
|
8
|
GC
|
MOL000449
|
stigmasterol
|
43.83
|
0.76
|
4
|
BX SJ
|
MOL000497
|
licochalcone a
|
40.79
|
0.29
|
2
|
GC
|
MOL001454
|
berberine
|
36.86
|
0.78
|
1
|
HL
|
MOL001803
|
sinensetin
|
50.56
|
0.45
|
1
|
ZS
|
MOL001942
|
isoimperatorin
|
45.46
|
0.23
|
4
|
JH
|
MOL002670
|
Cavidine
|
35.64
|
0.81
|
1
|
BX
|
MOL002714
|
baicalein
|
33.52
|
0.21
|
10
|
BX
|
MOL002776
|
baicalin
|
40.12
|
0.75
|
1
|
BX
|
MOL004328
|
naringenin
|
22.05
|
0.74
|
24
|
GC
|
MOL004804
|
18beta-glycyrrhetinic acid
|
59.29
|
0.21
|
1
|
GC CP ZS JH
|
MOL005812
|
naringin
|
22.05
|
0.74
|
1
|
GC
|
MOL005828
|
nobiletin
|
61.67
|
0.52
|
1
|
ZS CP
|
|
arginine
|
|
|
3
|
ZR
|
|
glutamic acid
|
|
|
3
|
ZR
|
|
ornithine
|
|
|
3
|
ZR
|
3.4. Enrichment Analysis of the Core Targets of HWD in Treating MetS
In order to further understand the mechanism of “multitarget and multipathway” of HWD in treating MetS, “R program” was used to perform enrichment analysis of GO-PB and KEGG on core targets and to excavate the biological processes and signaling pathways regulated by HWD in treatingMetS. These 77 core targets were involved in several biological process, mainly including response to nutrient levels, gland development, response to steroid hormone, cellular response to oxidative stress, reproductive structure development et al(Figure 4). Moreover, according to the p_values of enriched pathways and their correlation with MetS(shown in Table S4), we were most interested in the following five representative signal pathways including AGE-RAGE, MAPK, AMPK, JAK-STAT signaling pathways (Table 2).
Table 2 Representative enriched KEGG pathway of the core targets of HWD in treating Mets.
Pathway
|
Gene count
|
P value
|
Pathway ID
|
Associated genes
|
AGE-RAGE signaling pathway
|
9
|
1.93E-07
|
hsa04933
|
|
AMPK signaling pathway
|
8
|
9.39E-06
|
hsa04152
|
|
JAK-STAT signaling pathway
|
8
|
8.22E-05
|
hsa04630
|
|
MAPK signaling pathway
|
12
|
4.91E-08
|
hsa04010
|
|
3.5 Predicted binding of components of HWD to target proteins in MetS
To further validate candidate compounds of HWD targets in MetS, we tested the precision of docking between beta-sitost, naringenin, berberine, baicalein and the following potential target proteins (Figure4-5): IL6(PDB:6NCO) and AKT1(PDB:5KCV). We chose to study these target proteins because they were high-degree nodes in the network with many functional connections. Meanwhile, there were enriched in related-pathways, suggesting they play a critical role in the response to compounds in MetS. Docking analysis successfully predicted docking between beta-sitost, naringenin, berberine, baicalein and the binding pocket of two tested target proteins. Overall, these results provide further evidence that these two proteins act as beta-sitost, naringenin, berberine and baicalein targets in MetS.