Active compounds of the LCP
A total of 98 components were screened from LCP. Among the active ingredients screened from the TCMSP, we selected 68 ingredients according to OB and DL (see Additional file 1), including Huangqi (astragalus, HQ)20 species, Taoren (Peach kernel TR) 23 species, Zhimu (anemarrhena, ZM)15 species, Ezhu (Curcumae Rhizoma, EZ) 3 species, Sanleng (Rhizoma sparganii, SL) 5 species, Danggui (Chinese angelica, DG) 2species. In addition, we upload 50 (see Additional file 2) compounds of leech obtained from document retrieval.17-21 According to the two conditions, we get 30 active ingredients of leech in the end (see Additional file 3).
Putative targets of LCP
According to the above active ingredients,881 potential targets were to predicted by using TCMSP and Swiss Target Prediction, the targets included 209 from HQ, 59 from TR,85 from ZM, 9 from EZ, 54 from SL,29from DG, 436 from SZ (see Additional file 4), gathering them up and removed the duplicate targets, the final number was 380.
EM related targets
Endometriosis-related genes, obtained from five databases previously described, we get 90 genes from the Drug Bank, 1009 genes from the GeneCards, two genes from the OMIM, 86 genes from the PharmGKB, 15 genes from the TTD, a total of 1115 targets were obtained after sorting out the screening data (Figure 2).
Construction of LCP-compound-target-EM Network
From the Venn chart, we found there were 122 overlapping targets between drugs and disease potential targets (Figure 3). Use Cytoscape3.8.0 to map the LCP-compound-target-EM network (Figure 4), there are 168 nodes (Except overlapping targets, 45 nodes are drug constituents, including 12 from TR, 10 from SZ,7 from ZM, 11 from HQ, 1 from SL, and A1, B1, C1, D1, E1 means the ingredient comes from a variety of drugs, see Table 1) and edge 298 in the network,It can be seen that the effects of traditional Chinese medicines are widely distributed, each ingredient in LCP can take part in the treatment of diseases through a synergistic action.
PPI Network Analysis and Hub Genes Investigation
122 potential targets were uploaded to the STRING database. High confidence will improve the accuracy of the results so we set the minimum required interaction score to “highest confidence” (0.900). There were 110 proteins in the network, which contained 371 protein interaction relationships (Figure 5), and then filtered with application CytoNCA22, a Cytoscape plugin for centrality analysis and evaluation of protein interaction networks (Figure 6a), based on the median values for betweenness, closeness, degree, eigenvector, LAC and network, which were39.31546869, 0.1244298725, 6, 0.0291560585, 2.5333333335, 3.4583333335, identified 28 highly connected node (Figure 6b), then performed second screening, which was 9.1005245735,0.586956522,9,0.1712328495, 4.211111111,5.154761905. We gradually obtained the core network of hub genes, finally identified 10 highly connected nodes significant endometriosis-related targets (Figure 6c), which are MAPK1, FOS, JAK2, EGFR, RELA, SRC, MAPK8, PIK3CA, MAPK14, and ESR1. (Figure 6d)
GO and KEGG enrichment analysis
The Gene Ontology (GO) can describe how genes act in biological systems and produce a dynamic, controlled vocabulary that can be applied to all eukaryotes,23 which concludes three parts, biological progress (BP), cellular compound (CC), and molecular function (MF) to describe the biochemical activity of a gene product. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules, which can provide most of the known metabolic pathways and some of the known regulatory pathways.24 We used R packets to perform GO and KEGG enrichment analysis of 122 intersected genes, the detailed results are listed in additional files (see Additional file 5 and Additional file 6), the top 10 GO enrichment results of each project are shown in figure 7a/b. At the top of the BP group was “response to nutrient levels”, “cellular response to chemical stress”, “reproductive structure development”, “reproductive system development”, “response to oxygen levels” and “response to oxidative stress”, etc. The MF group mainly included “steroid binding”, “protein tyrosine kinase activity”, “nuclear receptor activity” and “ligand-activated transcription factor activity”, etc. And in the CC group, the GO terms were mainly involved “membrane raft”, “membrane microdomain”, “membrane region”, “organelle outer membrane”, “outer membrane mitochondrial outer membrane” and so on, which reflected the abnormality of multiple biological processes involved in endometriosis, and indicates that LCP may play a therapeutic role by improving the above biological pathways.
The KEGG pathway enrichment shows the top 30 contains, (Figure 8 a/b.) such as PI3K-Akt signaling pathway, EGFR tyrosine kinase inhibitor resistance, Th17 cell differentiation, relaxin signaling pathway, HIF-1 signaling pathway, Ovarian steroidogenesis, PD-L1 expression, and PD-1 checkpoint pathway in cancer and so on. The results of the first 30 of the GO and KEGG enrichment analysis were made into bubble charts and histograms, and produced a pathway map that the gene most enriched.
Verification of Molecular Docking
We screened 14 key components according to the LCP-compound-target-EM network(degree>5) as small-molecule drug ligands, which were docked with the 10 key targets identified in the previous PPI network (Table 2), then we obtained 140 sets of results(Table 3). The binding energy<0, indicating that the ligand molecules can bind spontaneously to the receptor proteins, however, there was no clear definition of the criteria for free energy screening after searching the literature,This paper take the binding energy<-5.0 kal˙mol-1 as a standard to judge the binding property is good, and the smaller the binding energy, the better running of docking. We found that the main way of molecules docking were hydrogen bonding and π-π stacking, The minimum binding energy is -10.5 kal˙mol-1 (Figure 9), and the binding energy<-5.0kal˙mol-1accounts for 96.43% of the total, and the binding energy>-5.0 kal˙mol-1was abandoned. It can fully explain that the pivotal components of LCP have strong binding force with their key targets.
Table 1 Description of nodes in the LCP-compound-target-EM Network (Figure 3)
Node Name
|
Source
|
Compound
|
A1
|
Zhimu,Danggui,Sanleng
|
Stigmasterol
|
B1
|
Taoren,Danggui,Sanleng
|
Beta-sitosterol
|
C1
|
Huangqi, Sanleng
|
Formononetin
|
D1
|
Huangqi, Zhimu
|
Kaempferol
|
E1
|
Huangqi,Taoren,Sanleng,Ezhu
|
Hederagenin
|
HQ1
|
Huangqi (astragalus,HQ)
|
Bifendate
|
HQ2
|
Huangqi (astragalus,HQ)
|
Jaranol
|
HQ3
|
Huangqi (astragalus,HQ)
|
Calycosin
|
HQ4
|
Huangqi (astragalus,HQ)
|
(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[ (2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol
|
HQ5
|
Huangqi (astragalus,HQ)
|
FA
|
HQ6
|
Huangqi (astragalus,HQ)
|
Quercetin
|
HQ7
|
Huangqi (astragalus,HQ)
|
Isorhamnetin
|
HQ8
|
Huangqi (astragalus,HQ)
|
(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol
|
HQ9
|
Huangqi (astragalus,HQ)
|
Mairin
|
HQ10
|
Huangqi (astragalus, HQ)
|
7-O-methylisomucronulatol
|
HQ11
|
Huangqi (astragalus,HQ)
|
3,9-di-O-methylnissolin
|
SL1
|
Sanleng (leech, SZ)
|
trans-gondoic acid
|
SZ1
|
Sanleng (leech, SZ)
|
gamma-aminobutyric acid
|
SZ2
|
Sanleng (leech, SZ)
|
L-tryptophan
|
SZ3
|
Sanleng (leech, SZ)
|
phenylalanine
|
SZ4
|
Sanleng (leech, SZ)
|
hexadecyl ethers of glycerol
|
SZ5
|
Sanleng (leech, SZ)
|
methyl 14-methylpentadecanoate
|
SZ6
|
Sanleng (leech, SZ)
|
L-tyrosine
|
SZ7
|
Sanleng (leech, SZ)
|
methyl (Z)-11-hexadecenoate
|
SZ8
|
Sanleng (leech, SZ)
|
hirudinoidine a
|
SZ9
|
Sanleng (leech, SZ)
|
palmitic acid
|
SZ10
|
Sanleng (leech, SZ)
|
glycerol
|
TR1
|
Taoren (peachkernel, TR)
|
Gibberellin A44
|
TR2
|
Taoren (peach kernel, TR)
|
3-O-p-coumaroylquinic acid
|
TR3
|
Taoren (peach kernel, TR)
|
2,3-didehydro GA70
|
TR4
|
Taoren (peach kernel, TR)
|
2,3-didehydro GA77
|
TR5
|
Taoren (peach kernel, TR)
|
GA120
|
TR6
|
Taoren (peach kernel, TR)
|
GA63
|
TR7
|
Taoren (peach kernel, TR)
|
4a-formyl-7alpha-hydroxy-1-methyl-8-methylidene-4aalpha,4bbeta-gibbane-1alpha,10beta-dicarboxylic acid
|
TR8
|
Taoren (peach kernel, TR)
|
Sitosterol alpha1
|
TR9
|
Taoren (peach kernel, TR)
|
GA60
|
TR10
|
Taoren (peach kernel, TR)
|
GA121-isolactone
|
TR11
|
Taoren (peach kernel, TR)
|
GA77
|
TR12
|
Taoren (peach kernel, TR)
|
campesterol
|
ZM1
|
Zhimu (anemarrhena, ZM)
|
Timosaponin B III_qt
|
ZM2
|
Zhimu (anemarrhena, ZM)
|
Anemarsaponin F_qt
|
ZM3
|
Zhimu (anemarrhena, ZM)
|
Hippeastrine
|
ZM4
|
Zhimu (anemarrhena, ZM)
|
Anemarsaponin C_qt
|
ZM5
|
Zhimu (anemarrhena, ZM)
|
coumaroyltyramine
|
ZM6
|
Zhimu (anemarrhena, ZM)
|
Anhydroicaritin
|
ZM7
|
Zhimu (anemarrhena, ZM)
|
diosgenin
|
Table 2 The PDB ID corresponding to each core target in the PPI network
Target
|
PDB ID
|
JAK2
|
3UGC
|
MAPK1
|
6SLG
|
FOS
|
1AO2
|
EGFR
|
2MOB
|
RELA
|
3QXY
|
SRC
|
2H8H
|
MAPK8
|
2NO3
|
PIK3CA
|
6PYS
|
MAPK14
|
2LGC
|
ESR1
|
2BJ4
|
Table 3 Docking scores of the active ingredients of LCP with their potential targets
Compound
|
3UGC
|
6SLG
|
1AO2
|
2MOB
|
3QXY
|
2H8H
|
2NO3
|
6PYS
|
2LGC
|
2BJ4
|
Quercetin
|
-7.8
|
-7.9
|
-8.4
|
-6.5
|
-9.2
|
-9.2
|
-9
|
-8.6
|
-8.6
|
-8.1
|
Hirudinoidine a
|
-7.2
|
-7
|
-7.6
|
-6
|
-7.4
|
-7.3
|
-7.6
|
-7.5
|
-7.5
|
-6.2
|
Methyl (Z)-11-hexadecenoate
|
-6.4
|
-
|
-5.5
|
-
|
-6.5
|
-6.1
|
-5.6
|
-5.9
|
-7.1
|
-6.2
|
Methyl 14-methylpentadecanoate
|
-6.3
|
-
|
-5.9
|
-
|
-6.5
|
-5.6
|
-5.5
|
-6.1
|
-7.1
|
-5.9
|
Kaempferol
|
-9
|
-7.9
|
-8.1
|
-6.3
|
-9.4
|
-9.1
|
-8.9
|
-8.3
|
-8.5
|
-8.3
|
Isorhamnetin
|
-8.8
|
-7.9
|
-9.8
|
-6.3
|
-9.2
|
-9.4
|
-8.7
|
-8.5
|
-8.4
|
-8.2
|
Beta-sitosterol
|
-8.9
|
-9.2
|
-8.1
|
-7
|
-8.2
|
-9.5
|
-9
|
-8.4
|
-9
|
-7.8
|
Stigmasterol
|
-9.5
|
-9.8
|
-8.2
|
-6.8
|
-9.1
|
-9.1
|
-9.3
|
-9
|
-8.3
|
-7.2
|
7-O-methylisomucronulatol
|
-7.5
|
-7
|
-7.4
|
-6.6
|
-9
|
-8.4
|
-7.5
|
-7.1
|
-8.2
|
-7
|
Anhydroicaritin
|
-8.4
|
-8.2
|
-8.7
|
-6.3
|
-9.3
|
-9.4
|
-8.8
|
-9.5
|
-7.8
|
-8.3
|
Formononetin
|
-8.8
|
-7.8
|
-7.9
|
-6.5
|
-9.2
|
-9.2
|
-8.7
|
-9
|
-9
|
-8.6
|
Calycosin
|
-8.3
|
-7.9
|
-7.7
|
-6.4
|
-9.3
|
-9.1
|
-8.7
|
-9.3
|
-8.9
|
-8.2
|
Diosgenin
|
-9.6
|
-9.4
|
-9.2
|
-7.2
|
-9.1
|
-10.4
|
-9.2
|
-9.8
|
-10.5
|
-8.1
|
phenylalanine
|
-6.4
|
-5.5
|
-6.2
|
-
|
-6.3
|
-6.4
|
-5.9
|
-6.6
|
-6.5
|
-6.2
|