Screening of effective active components of XHLP
A total of 568 active components of XHLP were retrieved through TCMSP database and Batman-TCM database, including 12 species of Radix Aconiti, 27 species of Radix Aquilinum, 123 species of Araceae, 127 species of Frankincense, and 276 species of Myrrh. However, Earth Dragon failed to retrieve the active components in the TCMSP database and Batman-TCM database. Therefore, three active components of Earthworm were obtained through literature search and HERB (A high-throughput experiment- and reference-guided database of traditional Chinese medicine,http://herb.ac.cn/) identification by the herbal group. Under the screening conditions of OB≥30% and DL≥0.18, 71 effective active compounds were selected from the active components of XHLP, including 3 species of Radix Aconiti and 8 species of Radix Aconiti, 3 species of Earth Dragon, 7 species of Araceae, 8 species of Frankincense, and 45 species of Myrrh; besides, there is 1 common active component of Radix Aconiti and Radix Aquilinum, and 2 common active components of Araceae and Myrrh (Table 2).
Table 2 XHLP effective active component information
ID
|
Pubchem CId
|
Component
|
OB
(%)
|
DL
|
Herbs
(Latin name)
|
CHW1
|
22212681
|
1-[(5R,8R,9S,10S,12R,13S,14S,17S)-12-hydroxy-10,13-dimethyl-2,3,4,5,6,7,8,9,11,12,14,15,16,17-tetradecahydro-1H-cyclopenta[a]phenanthren-17-yl]ethanone
|
33.47
|
0.42
|
Aconiti Radix
|
CHW2
|
92979
|
delta4,16-Androstadien-3-one
|
37.63
|
0.31
|
CAW1
|
133323
|
Izoteolin
|
39.53
|
0.51
|
Aconitum Kusnezoffii Reichb
|
CAW2
|
441742
|
karakoline
|
51.73
|
0.73
|
CAW3
|
21598997
|
3-deoxyaconitine
|
30.96
|
0.24
|
CAW4
|
21599000
|
3-acetylaconitine
|
37.05
|
0.2
|
CAW5
|
157539
|
crassicauline A
|
34.13
|
0.21
|
CAW6
|
155569
|
yunaconitine
|
33.56
|
0.2
|
CAW7
|
441749
|
napelline
|
34.48
|
0.72
|
DIL1
|
-
|
lumbrifebrine
|
-
|
-
|
Pheretima
|
DIL2
|
-
|
lumbritin
|
-
|
-
|
DIL3
|
-
|
lumbrolysin
|
-
|
-
|
TLX1
|
22524484
|
[(2R)-2-[[[(2R)-2-(benzoylamino)-3-phenylpropanoyl]amino]methyl]-3-phenylpropyl] acetate
|
38.88
|
0.56
|
Arisaematis Rhizoma
|
TLX2
|
5283637
|
24-epicampesterol
|
37.58
|
0.71
|
TLX3
|
12303645
|
sitosterol
|
36.91
|
0.75
|
TLX4
|
5997
|
CLR
|
37.87
|
0.68
|
TLX5
|
5364473
|
8,11,14-Docosatrienoic acid, methyl ester
|
43.23
|
0.3
|
RUX1
|
101257
|
tirucallol
|
42.12
|
0.75
|
Olibanun
|
RUX2
|
15181201
|
O-acetyl-α-boswellic acid
|
42.73
|
0.7
|
RUX3
|
637234
|
3alpha-Hydroxy-olean-12-en-24-oic-acid
|
39.32
|
0.75
|
RUX4
|
168928
|
Boswellic acid
|
39.55
|
0.75
|
RUX5
|
44559813
|
phyllocladene
|
33.4
|
0.27
|
RUX6
|
-
|
3-oxo-tirucallic,acid
|
42.86
|
0.81
|
RUX7
|
15181201
|
acetyl-alpha-boswellic,acid
|
42.73
|
0.7
|
RUX8
|
44583885
|
incensole
|
45.59
|
0.22
|
MOY1
|
13258914
|
quercetin-3-O-β-D-glucuronide
|
30.66
|
0.74
|
Myrrha
|
MOY2
|
5281855
|
ellagic acid
|
43.06
|
0.43
|
MOY3
|
440832
|
pelargonidin
|
37.99
|
0.21
|
MOY4
|
5283663
|
poriferasta-7,22E-dien-3beta-ol
|
42.98
|
0.76
|
MOY5
|
69232409
|
guggulsterol-VI
|
54.72
|
0.43
|
MOY6
|
128211
|
mansumbinoic acid
|
48.1
|
0.32
|
MOY7
|
57401582
|
myrrhanol C
|
39.96
|
0.58
|
MOY8
|
-
|
(8R)-3-oxo-8-hydroxy-polypoda -13E,17E,21-triene
|
44.83
|
0.59
|
MOY9
|
102242792
|
myrrhanones B
|
34.39
|
0.67
|
MOY10
|
-
|
epimansumbinol
|
61.81
|
0.4
|
MOY11
|
51407984
|
diayangambin
|
63.84
|
0.81
|
MOY12
|
667495
|
(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one
|
42.36
|
0.21
|
MOY13
|
-
|
(13E,17E,21E)-8-hydroxypolypodo-13,17,21-trien-3-one
|
44.34
|
0.58
|
MOY14
|
-
|
(13E,17E,21E)-polypodo-13,17,21-triene-3,18-diol
|
39.96
|
0.58
|
MOY15
|
-
|
16-hydroperoxymansumbin-13(17)-en-3β-ol
|
41.05
|
0.49
|
MOY16
|
-
|
mansumbin-13(17)-en- 3,16-dione
|
41.78
|
0.45
|
MOY17
|
-
|
(16S, 20R)-dihydroxydammar-24-en-3-one
|
37.34
|
0.78
|
MOY18
|
5318259
|
15α-hydroxymansumbinone
|
37.51
|
0.44
|
MOY19
|
636531
|
28-acetoxy-15α-hydroxymansumbinone
|
41.85
|
0.67
|
MOY20
|
101281214
|
isofouquierone
|
40.95
|
0.78
|
MOY21
|
11004967
|
[(5aS,8aR,9R)-8-oxo-9-(3,4,5-trimethoxyphenyl)-5,5a,6,9-tetrahydroisobenzofurano[6,5-f][1,3]benzodioxol-8a-yl] acetate
|
44.08
|
0.9
|
MOY22
|
-
|
phellamurin_qt
|
56.6
|
0.39
|
MOY23
|
-
|
(3R,20S)-3,20-dihydroxydammar- 24-ene
|
37.49
|
0.75
|
MOY24
|
-
|
3-methoxyfuranoguaia-9- en-8-one
|
35.15
|
0.18
|
MOY25
|
6439929
|
Guggulsterone
|
42.45
|
0.44
|
MOY26
|
441774
|
petunidin
|
30.05
|
0.31
|
MOY27
|
5280343
|
quercetin
|
46.43
|
0.28
|
MOY28
|
-
|
4,17(20)-(cis)-pregnadiene-3,16-dione
|
51.42
|
0.48
|
MOY29
|
101297585
|
Guggulsterol IV
|
33.59
|
0.74
|
MOY30
|
10496532
|
(7S,8R,9S,10R,13S,14S,17Z)-17-ethylidene-7-hydroxy-10,13-dimethyl-1,2,6,7,8,9,11,12,14,15-decahydrocyclopenta[a]phenanthrene-3,16-dione
|
35.75
|
0.48
|
MOY31
|
-
|
7β,15β- dihydroxypregn-4-ene-3,16-dione
|
43.11
|
0.51
|
MOY32
|
-
|
11α-hydroxypregna-4,17(20)-trans-diene-3,16-dione
|
36.62
|
0.47
|
MOY33
|
102242791
|
myrrhanone A
|
40.25
|
0.63
|
MOY34
|
-
|
3β-acetoxy-16β,20(R)-dihydroxydammar-24-ene
|
38.72
|
0.81
|
MOY35
|
-
|
1α-acetoxy-9,19-cyclolanost-24-en-3β-ol
|
44.4
|
0.78
|
MOY36
|
12019028
|
[(3R,5R,8R,9R,10R,13R,14R,17S)-17-[(2S,5S)-5-(2-hydroxypropan-2-yl)-2-methyloxolan-2-yl]-4,4,8,10,14-pentamethyl-2,3,5,6,7,9,11,12,13,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-yl] acetate
|
33.07
|
0.8
|
MOY37
|
21625900
|
cabraleone
|
36.21
|
0.82
|
MOY38
|
-
|
(20S)-3β-acetoxy-12β,16β,25-tetrahydroxydammar-23-ene
|
34.89
|
0.82
|
MOY39
|
-
|
(20S)-3β,12β,16β,25-pentahydroxydammar-23-ene
|
37.94
|
0.75
|
MOY40
|
-
|
(20R)-3β-acetoxy-16β-dihydroxydammar-24-ene
|
40.36
|
0.82
|
MOY41
|
-
|
3β- hydroxydammar-24-ene
|
40.27
|
0.82
|
MOY42
|
10870920
|
[(5S,6R,8R,9Z)-8-methoxy-3,6,10-trimethyl-4-oxo-6,7,8,11-tetrahydro-5H-cyclodeca[b]furan-5-yl] acetate
|
34.76
|
0.25
|
MOY43
|
-
|
2-methoxyfuranoguaia-9-ene-8-one
|
66.18
|
0.18
|
A1
|
441737
|
hypaconitine
|
31.39
|
0.26
|
Aconiti Radix/Aconitum Kusnezoffii Reichb
|
B1
|
222284
|
beta-sitosterol
|
36.91
|
0.75
|
Arisaematis Rhizoma/Myrrha
|
B2
|
5280794
|
Stigmasterol
|
43.83
|
0.76
|
Arisaematis Rhizoma/Myrrha
|
Construction and analysis of XHLP-active component-target network
The 71 active components in XHLP were entered into the TCMSP database and Batman-TCM database to find their corresponding target proteins. The 206 target proteins obtained were imported into the DrugBank database and the UniProt database for comparison and correction, and 170 standard gene names were output. Next, the XHLP composition and effective active components and the corresponding relationship between the active components and the target was obtained. The XHLP-active component-target network was constructed using Cytoscape 3.7.2 software. The network has 248 nodes, including 6 drug nodes, 71 active compound nodes, and 170 target nodes, with a total of 506 edges (Figure 2). Each edge in the network represents the active component contained in the drug and the interaction between the active component and the target gene. The "Networkanalyze" function in the Cytoscape 3.7.2 software is employed to perform topology analysis and calculation on the network. Among them, Degree value and BetweennessCentrality value (BC) are the key parameters to measure the nodes in the network. The active components in this network have an average relationship with 6 targets, and each target has an interconnection relationship with an average of 2.5 active components. Therefore, the active components in XHLP may be related to multiple targets while one target may be related to multiple active components. It can be revealed by analyzing the Degree value and BC value of the active component node and target gene node in the network that the top 5 active molecules are MOY27-quercetin, B2-Stigmasterol, B1-beta-sitosterol, CAW1-Izoteolin, and MOY2-ellagic acid, which can be connected to 136, 53, 53, 18, and 17 target proteins, respectively; the top six targets are PGR, NCOA2, NR3C2, PTGS2, PTGS1, and RXRA, which can interact with 25, 24, 18, 18, 12, and 12 active components, respectively (Table 3).
Table 3 Degree and BC values of the top 8 effective active components and targets of XHLP
Type
|
Description
|
Degree
|
BC
|
Link
|
Rank
|
Component
|
quercetin
|
136
|
0.71368345
|
136
|
1
|
Stigmasterol
|
52
|
0.08655831
|
52
|
2
|
beta-sitosterol
|
52
|
0.07340369
|
52
|
3
|
Izoteolin
|
18
|
0.07627614
|
18
|
4
|
ellagic acid
|
17
|
0.03680757
|
17
|
5
|
[(5aS,8aR,9R)-8-oxo-9-(3,4,5-trimethoxyphenyl)-5,5a,6,9-tetrahydroisobenzofurano[6,5-f][1,3]benzodioxol-8a-yl] acetate
|
16
|
0.03229396
|
16
|
6
|
pelargonidin
|
12
|
0.01540433
|
12
|
7
|
3-methoxyfuranoguaia-9- en-8-one
|
9
|
0.00568861
|
9
|
8
|
Gene
|
PGR
|
25
|
0.02750561
|
25
|
1
|
NCOA2
|
24
|
0.07571674
|
24
|
2
|
PTGS2
|
18
|
0.04966347
|
18
|
3
|
NR3C2
|
18
|
0.00563037
|
18
|
4
|
RXRA
|
12
|
0.03377059
|
12
|
5
|
PTGS1
|
12
|
0.03359142
|
12
|
6
|
CHRM3
|
10
|
0.00861658
|
10
|
7
|
CHRM1
|
10
|
0.00440313
|
10
|
8
|
Acquisition and analysis of differentially expressed genes in KOA cartilage tissue
Our research is based on 20 cases of cartilage tissue samples from KOA patients (8 male cases, 12 female cases, aged 66.20 ± 7.16, and K-L IV patients accounted for 20 cases) and 18 cases of normal cartilage tissue samples (aged 36.61 ± 13.08, 13 male cases and 5 female cases) from the sequencing data set GSE114007. The general clinical characteristics of the patients are presented in Table 1. The filter conditions are: Variance filter=15.0 and Low abundance=4.0; the standardization method is Log2-counts per millio. Box diagram and PCA diagram of the data filtering and before and after standardization are illustrated in Figure 3.A.B. P Value<0.01 and expression changes greater than or equal to twice (|log2 FC|≥1.0) were the criteria for screening differential genes. In the GSE114007 data set, 1672 differentially expressed genes were screened, including 913 up-regulated genes and 759 down-regulated genes. A heat map was drawn for the DEGs selected in GSE114007 and the top 50 DEGs with the most significant differences in the adjust P Value selection (Figure 3.C.D). Among them, red and green represent up-regulation and down-regulation of gene expression. Then, -log10 conversion was performed on the adjust P Value of the gene after the differential analysis process. According to log2 FC, -log10 (adjust P Value) is divided into up-regulated genome, down-regulated genome, and no statistically different genome). The results were imported into GraphPad Prism 7.0 to draw a volcano map (Figure 3.E).
XHLP effective active component-KOA intersection target analysis and mutual PPI network construction
The 170 drug active component targets obtained above and the 1672 differentially expressed genes obtained from the differential analysis of the sequencing data set GSE114007 were introduced into the online Venn diagram production website InteractiVenn. Then, a total of 33 potential targets for XHLP treatment of KOA cartilage changes were obtained by matching mapping. The Venn diagram of the effective active component-KOA intersection target is exhibited in Figure 4 A. Besides, 33 potential target genes were imported into STRING online analysis website (https://string-db.org/); hide unconnected targets were set, and "medium confidence> 0.400" in the lowest interaction score was determined; the result data of protein-protein interaction were exported (Figure 4.B.C). Next, the PPI network of potential target genes was obtained by Cytoscape 3.7.2 software, and the cytoHubba plug-in was adopted to screen Hub genes[40]. Two different algorithms, Degree and Betweenness, were employed to the top 6 potential core target genes: VEGFA, CCND1, MYC, JUN, MMP9, and MMP2 (Figure 4.D.E.F). Meanwhile, the BisoGenet plug-in in the Cytoscape 3.7.2 software was applied to construct the PPI network of the drug active component target and the KOA differential gene PPI network (Figure 5.A.B). The two PPI networks were merged and mapped to an intersection network (Figure 5.C). The network topology of the intersection network was analyzed and calculated using CytoNCA to obtain parameter values such as Degree, Betweenness, BetweennessCentrality (BC), ClosenessCentrality (CC), NeighborhoodConnectivity (NC), and LAC. The first screening threshold was Degree>66 (that is, twice the median value of Degree). The result revealed that there are 1,170 nodes and 52,739 edges in the secondary network graph (Figure 5.D). The second screening thresholds were Degree>109, Betweenness>460.887, BC>0.00043, CC>0.4421, NC>146.685, and LAC>16.999. The results indicated that there are 144 nodes and 1723 edges in the three-level network graph, which also included genes such as VEGFA, CCND1, MYC, JUN, MMP9, and MMP2 (Figure 5.E).
Enrichment analysis of intersection target by GO/KEGG
The Bioconductor package and clusterProfiler package in R language were used to perform GO and KEGG pathway enrichment analysis on 33 XHLP active components-KOA intersection targets. The GO analysis of 33 potential target genes demonstrated that the biological process (BP) mainly focuses on response to oxygen levels, mechanical stimulus, vitamin, drug, and regulation of smooth muscle cell proliferation; cellular component (CC) is mainly concentrated in collagen-containing extracellular matrix, endoplasmic reticulum lumen, and fibrillar collagen trimer; molecular function (MF) is mainly manifested in activating transcription factor binding, growth factor binding, and core promoter sequence-specific DNA binding (Table 4, Figure 6 A.B.C). The enrichment analysis indicated that the KEGG pathway mainly focuses on AGE-RAGE signaling pathway in diabetic complications, Relaxin signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, Fluid shear stress and atherosclerosis signaling pathways. The pathview package was adopted to display the signal pathway diagram related to cartilage (Figure 6.D.E.F.G).
Table 4 Annotation of intersection target gene GO/KEGG enrichment analysis
Category
|
Term
|
Description
|
Count
|
P.adj.value
|
Genes
|
BP
|
GO:0070482
|
response to oxygen levels
|
11
|
0.00000005
|
PTGS2,PLAU,VEGFA,CDKN1A,MMP2,PPARG,MYC,ICAM1,COL1A1,SLC2A4,HK2
|
GO:0009612
|
response to mechanical stimulus
|
8
|
0.00000150
|
PTGS2,JUN,RELA,PPARG,FOS,COL1A1,COL3A1,IRF1
|
GO:0033273
|
response to vitamin
|
6
|
0.00000436
|
PTGS2,RELA,PPARG,CCND1,COL1A1,SPP1
|
GO:0048660
|
regulation of smooth muscle cell proliferation
|
7
|
0.00000436
|
PTGS2,JUN,CDKN1A,MMP2,MMP9,PPARG,IGFBP3
|
GO:0042493
|
response to drug
|
9
|
0.00000436
|
PTGS2,RELA,CDKN1A,PPARG,CCND1,FOS,MYC,ICAM1,COL1A1
|
CC
|
GO:0062023
|
collagen-containing extracellular matrix
|
6
|
0.00580234
|
MMP2,MMP9,ICAM1,COL1A1,COL3A1,PCOLCE
|
GO:0005788
|
endoplasmic reticulum lumen
|
5
|
0.00580234
|
PTGS2,COL1A1,COL3A1,SPP1,IGFBP3
|
GO:0005583
|
fibrillar collagen trimer
|
2
|
0.00580234
|
COL1A1,COL3A1
|
GO:0098643
|
banded collagen fibril
|
2
|
0.00580234
|
COL1A1,COL3A1
|
GO:0005667
|
transcription regulator complex
|
5
|
0.01219532
|
JUN,RELA,PPARG,CCND1,FOS
|
MF
|
GO:0033613
|
activating transcription factor binding
|
5
|
0.00005068
|
JUN,RELA,PPARG,FOS,MYC
|
GO:0019838
|
growth factor binding
|
5
|
0.00035415
|
COL1A1,COL3A1,INSR,IGFBP3,ERBB3
|
GO:0001046
|
core promoter sequence-specific DNA binding
|
3
|
0.00456064
|
RELA,FOS,MYC
|
GO:0005178
|
integrin binding
|
4
|
0.00456064
|
IGF2,ICAM1,COL3A1,SPP1
|
GO:0004955
|
prostaglandin receptor activity
|
2
|
0.00456064
|
PPARG,PTGER3
|
KEGG
|
hsa05219
|
Bladder cancer
|
7
|
0.00000002
|
VEGFA,CDKN1A,MMP2,MMP9,CCND1,MYC,RASSF1
|
hsa05205
|
Proteoglycans in cancer
|
10
|
0.00000015
|
PLAU,VEGFA,CDKN1A,MMP2,MMP9,IGF2,CCND1,MYC,COL1A1,ERBB3
|
hsa04933
|
AGE-RAGE signaling pathway in diabetic complications
|
8
|
0.00000015
|
JUN,RELA,VEGFA,MMP2,CCND1,ICAM1,COL1A1,COL3A1
|
hsa04926
|
Relaxin signaling pathway
|
8
|
0.00000087
|
JUN,RELA,VEGFA,MMP2,MMP9,FOS,COL1A1,COL3A1
|
hsa05167
|
Kaposi sarcoma-associated herpesvirus infection
|
9
|
0.00000095
|
PTGS2,JUN,RELA,VEGFA,CDKN1A,CCND1,FOS,MYC,ICAM1
|
hsa04668
|
TNF signaling pathway
|
7
|
0.00000479
|
PTGS2,JUN,RELA,MMP9,FOS,ICAM1,IRF1
|
hsa04151
|
PI3K-Akt signaling pathway
|
10
|
0.00001120
|
RELA,VEGFA,CDKN1A,IGF2,CCND1,MYC,COL1A1,INSR,SPP1,ERBB3
|
hsa05418
|
Fluid shear stress and atherosclerosis
|
7
|
0.00001428
|
JUN,RELA,VEGFA,MMP2,MMP9,FOS,ICAM1
|
Molecular docking verification
The top 5 active molecules in the 2.2 results (quercetin, Stigmasterol, beta-sitosterol, Izoteolin, and ellagic acid) and the top 6 potential core target genes in the 2.4 results (VEGFA, CCND1, MYC, JUN, MMP9, and MMP2) were selected for molecular docking verification. The Chem3D software was employed to draw the corresponding 3D structure according to the structural formulas of the 5 effective active components, and output in mol*2 format. The 3D structures of 6 core proteins were downloaded from the PDB database and output in pdb format. The AutoDockTools 1.5.6 software was used to convert active components and core proteins into pdbqt format to find active pockets, that is, the ligand is combined with one or more amino acid residues to form H bonds, H-π bonds, H-π bonds, or π-π bonds and other active sites. Vina script, LeDock, and Discovery Studio 2016 software were operated to calculate the binding energy of ligand and receptor. The results suggested that quercetin can form a stable docking model with CCND1, JUN, MMP9, and MMP2, respectively; beta-sitosterol can dock with JUN and MMP2 protein ligands; izoteolin and ellagic acid can form stable docking with JUN, MMP9, and MMP2 protein ligands (Table 5). When the receptor-ligand binding energy calculated by Vina and LeDock was ≤−5.0 kcal·mol-1 and DS can find the docking site, the results of the active component receptor output by the LeDock software were imported into Pymol, and the 3D molecular docking display was performed with the protein ligand (Figure 7). Simultaneously, the Discovery Studio 2016 software was adopted to display the results of molecular docking that meet the requirements in two dimensions (Figure 8).