Network pharmacology exploration reveals endothelial inflammation as a common mechanism for stroke and coronary artery disease treatment of Danhong injection

Abstract

Although Danhong injection (DHI) is the most widely prescribed Chinese medicine for both stroke and coronary artery disease (CAD), its underlying common molecular mechanisms remain unclear. An integrated network pharmacology and experimental verification approach was used to decipher common pharmacological mechanisms of DHI on stroke and CAD treatment. A compound-target-disease & function-pathway network was constructed and analyzed, indicating that 37 ingredients derived from DH (Salvia miltiorrhiza Bge., Flos Carthami tinctorii and DHI) modulated 68 common targets shared by stroke and CAD. In-depth network analysis results of the top diseases, functions, pathways and upstream regulators implied that a common underlying mechanism linking DHI's role in stroke and CAD treatment was inflammatory response in the process of atherosclerosis. Experimentally, DHI exerted comprehensive anti-inflammatory effects on LPS, ox-LDL or cholesterol crystal-induced NF-κB, c-jun and p38 activation, as well as IL-1β, TNF-α, and IL-10 secretion in vascular endothelial cells. Ten of 14 predicted ingredients were verified to have significant anti-inflammatory activities on LPS-induced endothelial inflammation. DHI exerts pharmacological efficacies on both stroke and CAD through multi-ingredient, multi-target, multi-function and multi-pathway mode. Anti-endothelial inflammation therapy serves as a common underlying mechanism. This study provides a new understanding of DHI in clinical application on cardiovascular and cerebrovascular diseases.

Figure 1

Workflow for DHI co-treatment of both stroke and CAD.

Figure 2

Common targets of DH in the treatment of stroke and CAD. (A) Unique and shared disease targets for stroke and CAD. The network depicted 123 unique targets related to stroke (light blue) and 222 unique targets related to CAD (deep yellow) with 149 targets shared by both (green). (B) Common targets of DH in treating for stroke and CAD. The network described 33 unique stroke targets (pale blue) and 94 unique CAD targets (deep yellow) with 68 common targets shared by both. Venn diagrams showing the number of shared and unique targets by stroke and CAD were also represented.

Figure 3

Analysis of compound-target-disease & function-pathway network of DH. (A) DH ingredient-target network. Fourteen Danshen ingredients (yellow), 17 Honghua ingredients (violet) and six shared ingredients from these two herbs (gray) were presented, which cooperatively modulate the 68 common intracellular targets. Venn diagram showed the unique and shared numbers of ingredients from DH. Ingredients with red border were identified from DHI. (B) Target-disease & function-pathway network. Top 10 diseases, top 10 functions (orange) and top 20 pathways (light blue) correlative with the 68 common (green) targets were shown. (C and D) Function and disease classification by IPA. The order of top 10 diseases and top 10 functions were ranked from left to right by −log(p-value). (E) Six categories with the top 60 expanded pathways list including Cardiovascular signaling, Diseases-specific pathways, Cellular inflammatory response, Cytokine signaling, Intracellular and second message signaling and Cellular growth, proliferation, and development were shown. The order of importance was ranked from top to bottom by −log(p-value). (F and G) The detailed signaling pathway of atherosclerosis signaling and HMGB1 signaling contain certain highlight targets extracted from the 68 common targets were shown.

Figure 4

Upstream regulators of DH action shared by inflammatory response and atherosclerosis. (A) Fifty targets related to inflammatory response extracted from the 68 common targets. (B) The relationship including six key upstream regulators between inflammatory response and atherosclerosis. (C) The PPI network between the six key upstream regulators. (D) The synergetic effects of 27 DH ingredients on the six key upstream regulators. This sub-network was drawn from Fig. 3A, which contains 9 Danshen (yellow), 13 Honghua (violet) and 5 shared ingredients from both herbs (gray).

Figure 5

The effects of DHI on nuclear translocation of c-Jun, p38, NF-κB p65 induced by different stimulants. EA.hy926 cells were pre-incubated with DHI for 1 h before adding 10 μg/mL LPS or 100 μg/mL ox-LDL, and cultured for 30 min. (A) Representative photo-images and (B) summary bar graph of DHI at different dilution ratio (1/3200, 1/1600 and 1/800) on 10 μg/mL LPS induced p-c-Jun, p-p38 or p-NF-κB p65 nuclear translocation. (C) Representative photo-images and (D) summary bar graph of different dilution ratio of DHI on 100 μg/mL ox-LDL induced p-c-Jun, p-p38, or p-NF-κB p65 nuclear translocation. (E) CHC (10–500 μg/mL) on p-c-Jun, p-p38 or p-NF-κB p65 nuclear translocation. Nucleus were stained by Hoechst (blue) and the transcriptional factors were stained by immunolabeled antibodies for p-c-Jun (green), p-p38 (yellow), or p-NF-κB p65 (red). Cells were imaged with the HCA reader using a 20× objective lens with each column reflecting images collected from the respective fluorescent channels using the same optical field. Data are presented as mean ± SD (n = 3). **P < 0.01 versus control; ^#P < 0.05 versus LPS or ox-LDL group; ^##P < 0.01 versus LPS or ox-LDL group.

Figure 6

The effects of DHI on phosphorylation of NF-κB p65, p38, and JNK induced by different stimulants. Different concentrations of DHI were pre-incubated with EA.hy926 cells for 1 h. (A,D) DHI on 10 μg/mL LPS-induced phosphorylation of NF-κB, p38 and JNK. (B,E) DHI on 100 μg/mL ox-LDL-induced phosphorylation of NF-κB, p38 and JUK. (C,F) DHI on 100 μg/mL CHC-induced phosphorylation of NF-κB, p38 and JNK. Data are presented as mean ± SD (n = 3). *P < 0.05 versus control; **P < 0.01 versus control; ^#P < 0.05 versus LPS or ox-LDL group; ^##P < 0.01 versus LPS or ox-LDL group. Each Blot was cropped at the position of the blotted protein and high-contrast was not used.

Figure 7

The effects of DHI on expression of IL-1β, IL-10 and TNF-α induced by different stimulants. EA.hy926 cells were pre-treated with different concentrations of DHI (1/800, 1/1600 and 1/3200 dilutions) for 1 h, and then stimulated with LPS, ox-LDL or CHC for 18 h respectively. Cell supernatants were collected to detect cytokines IL-1β, IL-10 and TNF-α levels. (A–I) IL-1β, IL-10 and TNF-α levels were promoted by 10 μg/mL LPS, 100 μg/mL ox-LDL and 100 μg/mL CHC. (A–C) DHI effects on LPS, ox-LDL or CHC-stimulated IL-1β level. (D–F) DHI effects on LPS, ox-LDL or CHC- stimulated IL-10 level. (G–I) DHI effect on LPS, ox-LDL or CHC-stimulated TNF-α level. Data are presented as mean ± SD (n = 3). *P < 0.05 versus control; **P < 0.01 versus control; ^#P < 0.05 versus LPS, ox-LDL or CHC group; ^##P < 0.01 versus LPS, ox-LDL or CHC group.

Figure 8

Anti-inflammatory screening of individual DH ingredients. (A) Sub-network of 14 ingredients predicted targeting NF-κB p65by network analysis. (B) EA.hy926 cells were cultured with each of the 14 DH ingredients (1 μM) for 1 h before adding 10 μg/mL LPS for 30 min. NF-κB p65 nuclear translocation assay was performed and quantification of the data was shown in bar graph. (C) Representative fluorescence microscopic images of apigenin effect on LPS-induced NF-κB p65 nuclear translocation. Nucleus were stained by Hoechst (blue) and the NF-κB p65 were stained by immunolabeled antibodies (green). Cells were imaged with the HCA reader using a 20× objective lens and then amplified to see the details, each column reflecting images collected from the respective fluorescent channels by the same optical field. Data are presented as mean ± SD (n = 3). **P < 0.01 versus control; ^#P < 0.05 versus LPS group; ^##P < 0.01 versus LPS group.