Collection of CLMN active ingredients and targets
In this study, 33 active compounds were collected from the TCMSP, PubChem and Swiss Target Prediction databases, targeting 2035 proteins, including 9 active compounds in Pueraria lobata targeting 506 proteins, 3 active compounds in Rhizoma Dioscoreae Nipponicae targeting 94 proteins, 12 active compounds in Ligusticum chuanxiong targeted 443 proteins, and 9 active compounds in propolis targeting 992 proteins.
Screening core targets
3652 targets were retrieved from the GeneCards database, and the data from GSE48060 and GSE66360 chips and platform were downloaded from the GEO database. 898 significant differential genes were obtained. We drew both DEGs volcano and heat maps, as shown in Fig. 2a: GSE chip DEGs volcano map and Fig. 2b: a heat map. In the volcano map, the down-regulated genes in the normal group were represented by green dots, while those up-regulated in the experimental group were represented by red dots.
There were 550 compositional targets from the TCMSP and Swiss Target Prediction databases, 2495 disease targets from the GeneCards database, and 898 DEGs from the GEO database that were imported into RStudio software. Using the "VennDiagram" package, 29 drug-disease interaction targets (ABCB1, ALDH2, RORC, PLAU, TNF, KIT, PTGS2, MMP9, CXCR1, NR3C2, NLRP3, FTO, ICAM1, VCAM1, TLR4, CCR5, MME, ADA, ADRB2, VEGFA, FOS, CDKN1A, JUN, NFKBIA, TIMP2, HMOX1, CD40LG, IL1B, C5AR1) were obtained. The results are shown in Fig. 2c.
Protein interaction network (PPI) analysis
The 29 targets described above were imported into the STRING database and were view in the network diagram "Analysis" of the database. The PPI network consisted of 29 nodes and 34 edges. The average node degree was 2.34 and the average clustering coefficient was 0.521 < 2.83e ~ 14, which indicated that the target genes had potential interactions. The TSV file data of protein interaction were obtained, and then the data was imported into the Cytoscape software to construct the protein interaction network diagram, as shown in Fig. 3a. The "Network Analyzer" tool was used to score the proteins. In the Figure, the top 10 proteins are represented by a red oval, and the rest are represented by a blue oval. TNF, JUN, IL1B, VEGFA, CCR5, MMP9, NFKBIA, proteins with high scores play an important role in the regulation of the network and be the key targets of CLMN for the treatment of MI.
Construction of "active ingredient-core target" network
Using the "merge" tool within the Cytoscape3.7.2 software, the network diagram for 32 drug active components and 29 MI targets was constructed (Fig. 3b). Among them, the ovals of different colors were used to represent each active ingredient from different TCMs, and the triangle represents the disease targets. The network consists of 61 nodes and 123 edges. From the graph, it is apparent that a single active component corresponds to multiple targets, and one target can also correspond to multiple active components. Taken together, CLMN therapy for MI has characteristics of multi-components and multi-targets. Using the Network Analyzer analysis tool, the top components were puerarin, Kaempferol, apigenin, daidzein and top targets were PTGS2, ABCB1, MMP9, JUN and CZCR1, suggesting that all may play an important role in the mechanism of CLMN in the treatment of MI.
GO and KEGG enrichment analysis
Using R language, 29 common targets were selected for GO and KEGG enrichment analysis with significance of p<0.05. We correlated CLMN treatment with neuroinflammation, bacterial molecules, lipopolysaccharide and cells to external stimuli and regulation of the production of immunoreactive cytokines. KEGG pathway enrichment of the first 20 signal pathways indicates that CLMN treatment involves NF-kappa B, TNF, IL-17. Figure 4 represents the drawing of a GO analysis bubble chart of CLMN (Fig. 4a-c), KEGG pathway analysis bar chart of CLMN (Fig. 4d) and NF-kappa B signal pathway map enriched in the first place (Fig. 4e). Based on the above analysis, the data suggests that CLMN results in a multi-modal pharmacological profile for CLMN
Molecular docking analysis
Five important target proteins, TNF, IL1B, PTGS2, VCAM1 and NFKBIA, which are ranked first in the PPI network and enriched in the first NF-kappa B pathway, were docked with their corresponding compounds by Discovery Studio4.5 software, and compared with the positive drugs of the corresponding target proteins. The docking results are shown in Table 1, and the interaction between them is shown in Fig. 5. The results showed that the active components Daidzein-4,7-diglucoside, rutin and puerarin had good binding affinity with target proteins TNF, IL1B, PTGS2, VCAM1 and NFKBIA.
HE staining results
The results of HE staining are shown in Fig. 6. The structure of cardiomyocytes in the sham operation group was normal and orderly, with no pathological changes. However, in the MI model group, the cardiomyocytes were injured and disordered, with a large number of inflammatory cell infiltrates, and large areas of myocardial infarction and myocardial fibrosis. With the low dose of CLMN, more inflammatory cell infiltrates were observed, but with the middle and high dose groups of CLMN, inflammation was alleviated, the area of myocardial infarction was significantly reduced, and cardiomyocyte fibrosis was inhibited.
TNF- α, TRAF-2 and IkB α proteins in Myocardial Infarctions
The results of immunohistochemical staining (Fig. 7) showed that the expression of TNF-α and TRAF-2 proteins were increased by MI, while IkBα protein decreased. The middle and high dose groups of CLMN significantly decreased the expression of TNF-α and TRAF-2 proteins and increased the expression level of IkBα protein.
Table 1 Docking scores of target proteins with their corresponding compounds and positive controls.
Key target
|
Small molecule ligand
|
Docking score
|
Positive for drugs
|
Docking score
|
TNF
|
Daidzein-4,7-diglucoside
|
107.769
|
Chloroquine
|
109.37
|
IL1B
|
rutin
|
109.301
|
Talmapimod
|
105.2
|
PTGS2
|
puerarin
|
135.137
|
Dihomo-gamma-linolenic acid
|
139.496
|
PTGS2
|
beta-sitosterol
|
131.835
|
PTGS2
|
Mandenol
|
125.133
|
PTGS2
|
Phenethyl caffeate
|
118.007
|
PTGS2
|
Rhamnetin
|
117.062
|
PTGS2
|
Quercetin
|
112.161
|
ICAM1
|
Butylphthalide
|
68.9836
|
Olopatadine
|
67.4193
|
ICAM1
|
Ligustilide
|
65.5129
|
ICAM1
|
apigenin
|
58.8867
|
ICAM1
|
Kaempferol
|
50.3748
|
NFKBIA
|
puerarin
|
98.3206
|
Astaxanthin
|
101.948
|
NFKBIA
|
apigenin
|
84.3222
|