Screening potential therapeutic targets of sirolimus and rosuvastatin for CABG treatment
To identify the intersecting genes and construct drug-drug and drug-disease networks, we compiled lists of genes related to CABG and the drugs sirolimus and rosuvastatin. We screened the DisGeNET, GeneCards, and OMIM databases for CABG-related disease targets and identified 656 unique targets. Redundancies were excluded using the UniProt database. Subsequently, from the CTD, SuperPred, Swiss Target Prediction, and PharmMapper databases, we assembled 815 and 417 pharmacological targets for sirolimus and rosuvastatin, respectively. Redundant genes were filtered out using the UniProt database. Venn diagram analysis showed that sirolimus and CABG had 115 shared targets, rosuvastatin and CABG had 23 shared targets, and all three had 96 shared targets (Fig. 2A).
To directly present the network topology status of sirolimus and rosuvastatin for CABG, we constructed a shared target network. In the network shown, the blue nodes represent shared targets between sirolimus and CABG, the pink nodes indicate shared targets between rosuvastatin and CABG, and the yellow nodes represent shared targets among all three, such as TNF, AKT1, and MMPs (Fig. 2B).
Exploring the therapeutic potential of sirolimus and rosuvastatin against NIH using GO and KEGG enrichment analysis
To further evaluate the intersecting targets associated with NIH, we performed GO and KEGG enrichment analyses. We conducted analyses for each group of shared targets: sirolimus and CABG, rosuvastatin and CABG, and the combination of sirolimus, rosuvastatin, and CABG. We subsequently selected and visualized the top ten categories from each of the three domains— BP, CC, and MF —as well as the KEGG pathways (Fig. 3–5).
GO and KEGG enrichment analysis for combined sirolimus, rosuvastatin, and CABG
We found that sirolimus, rosuvastatin, and CABG have 96 shared targets that are significantly linked to various biological activities, including the negative regulation of apoptotic process, regulation of inflammatory response, ECM disassembly and organization, cellular oxidative stress, and pathways such as PI3K-Akt, MAPK, TNF, IL-17, fluid shear stress, lipid and atherosclerosis (Fig. 5 and Tables S9–12). These results help explain the changes in biological function and related pathways when sirolimus and rosuvastatin are used either alone or in combination for NIH treatment after CABG.
Predicting CABG-related hub genes for sirolimus and rosuvastatin via PPI network analysis
We examined the hub genes and potential mechanisms related to the use of sirolimus and rosuvastatin in the treatment of NIH of graft and anastomotic sites after CABG. The STRING database was used to construct PPI networks for shared targets of sirolimus, rosuvastatin, and CABG. We used Metascape and Cytoscape software to visualize the network. Additionally, topological parameters were calculated. We used the CytoHubba plug-in of Cytoscape to identify hub and bottleneck genes in the PPI network, using MCC and bottleneck scores (Fig. 6; Table 1 and S13).
Table 1
Top ten significant core targets ranked using CytoHubba
No. | Name | Description | MCC1) Score | BottleNeck Score | DC2) | BC3) | CC4) |
Sirolimus |
1 | TP53*5) | Tumor protein p53 | 9.22E + 13 | 50.00 | 146.00 | 972.93 | 178.00 |
2 | IL6* | Interleukin 6 | 9.22E + 13 | 19.00 | 163.00 | 1,400.41 | 186.50 |
3 | STAT3* | Signal transducer and activator of transcription 3 | 9.22E + 13 | 15.00 | 134.00 | 541.92 | 172.00 |
3 | VCAM1 | Vascular cell adhesion molecule 1 | 9.22E + 13 | 15.00 | 87.00 | 194.11 | 148.33 |
5 | IL1B | Interleukin 1 beta | 9.22E + 13 | 8.00 | 146.00 | 898.27 | 178.00 |
6 | MAPK14 | Mitogen-activated protein kinase 14 | 9.22E + 13 | 6.00 | 93.00 | 214.59 | 151.50 |
6 | ESR1 | Estrogen receptor 1 | 9.22E + 13 | 6.00 | 113.00 | 489.63 | 161.33 |
6 | ALB* | Albumin | 9.22E + 13 | 6.00 | 161.00 | 1,908.35 | 185.50 |
9 | AKT1 | AKT serine/threonine kinase 1 | 9.22E + 13 | 4.00 | 173.00 | 1,687.52 | 191.50 |
9 | MMP2 | Matrix metallopeptidase 2 | 9.22E + 13 | 5.00 | 99.00 | 220.08 | 154.50 |
9 | SRC | SRC proto-oncogene, non-receptor tyrosine kinase | 9.22E + 13 | 5.00 | 134.00 | 766.75 | 172.00 |
Rosuvastatin |
1 | ALB* | Albumin | 9.22E + 13 | 44.00 | 93.00 | 1,177.20 | 105.08 |
2 | AKT1* | AKT serine/threonine kinase 1 | 9.22E + 13 | 11.00 | 89.00 | 878.05 | 103.08 |
3 | TNF* | Tumor necrosis factor | 9.22E + 13 | 8.00 | 97.00 | 1,270.34 | 106.92 |
4 | IL1B* | Interleukin 1 beta | 9.22E + 13 | 6.00 | 79.00 | 557.01 | 97.92 |
5 | SELE | Selectin E | 1.82E + 13 | 5.00 | 36.00 | 94.79 | 76.83 |
6 | CAT | Catalase | 9.22E + 13 | 4.00 | 48.00 | 194.11 | 82.58 |
6 | PTGIS | Prostaglandin I2 synthase | 279 | 5.00 | 12.00 | 400.50 | 61.67 |
8 | MDM2 | MDM2 proto-oncogene | 9.22E + 13 | 2.00 | 33.00 | 50.64 | 74.42 |
8 | ESR1 | Estrogen receptor 1 | 9.22E + 13 | 3.00 | 57.00 | 260.82 | 86.92 |
8 | CASP3* | Caspase 3 | 9.22E + 13 | 3.00 | 65.00 | 379.67 | 90.92 |
8 | PECAM1 | Platelet and endothelial cell adhesion molecule 1 | 9.22E + 13 | 1.00 | 50.00 | 129.10 | 83.42 |
8 | MMP9* | Matrix metallopeptidase 9 | 9.22E + 13 | 5.00 | 77.00 | 474.21 | 96.92 |
Sirolimus + Rosuvastatin |
1 | ALB* | Albumin | 9.22E + 13 | 40.00 | 79.00 | 992.34 | 87.00 |
2 | AKT1* | AKT serine/threonine kinase 1 | 9.22E + 13 | 11.00 | 75.00 | 629.42 | 85.00 |
3 | TNF* | Tumor necrosis factor | 9.22E + 13 | 5.00 | 82.00 | 952.30 | 88.50 |
3 | ESR1 | Estrogen receptor 1 | 9.22E + 13 | 5.00 | 49.00 | 194.33 | 71.83 |
5 | MMP9* | Matrix metallopeptidase 9 | 9.22E + 13 | 3.00 | 66.00 | 404.38 | 80.50 |
5 | IL1B | Interleukin 1 beta | 9.22E + 13 | 7.00 | 67.00 | 418.99 | 81.00 |
7 | IGF1 | Insulin-like growth factor 1 | 9.22E + 13 | 3.00 | 54.00 | 190.52 | 74.50 |
7 | CAT | Catalase | 9.22E + 13 | 3.00 | 38.00 | 110.59 | 66.50 |
7 | CASP3* | Caspase 3 | 9.22E + 13 | 3.00 | 58.00 | 322.68 | 76.50 |
10 | SRC* | SRC proto-oncogene, non-receptor tyrosine kinase | 9.22E + 13 | 2.00 | 57.00 | 227.32 | 76.00 |
10 | PPARA | Peroxisome proliferator activated receptor alpha | 2.99E + 12 | 1.00 | 31.00 | 85.01 | 63.00 |
10 | HSP90AA1* | Heat shock protein 90 alpha family class A member 1 | 9.22E + 13 | 2.00 | 48.00 | 162.96 | 71.50 |
10 | HMOX1 | Heme oxygenase 1 | 3.46E + 12 | 2.00 | 34.00 | 228.22 | 64.50 |
10 | MDM2 | MDM2 proto-oncogene | 9.22E + 13 | 2.00 | 31.00 | 37.13 | 62.83 |
10 | SERPINA1 | Serpin family A member 1 | 1670018 | 2.00 | 22.00 | 70.77 | 58.33 |
10 | MMP2 | Matrix metallopeptidase 2 | 7.18E + 13 | 2.00 | 47.00 | 127.75 | 71.00 |
1) MCC, maximal clique centrality; 2) DC, degree centrality; 3) BC, betweenness centrality; 4) CC, closeness centrality; 5) asterisks (*) indicate genes with overlapping hub-bottlenecks. |
Sirolimus and CABG PPI network analysis
The network complex between sirolimus and CABG comprised 211 nodes and 6,086 edges. We identified the top ten hub genes as beta-actin, JUN, STAT3, vascular endothelial growth factor A, hypoxia-inducible factor 1 subunit alpha, IL6, albumin (ALB), caspase 3 (CASP3), catenin beta 1, and tumor protein 53 (TP53). The top 11 bottleneck genes were TP53, IL6, STAT3, vascular cell adhesion molecule 1, IL1B, MAPK14, estrogen receptor 1 (ESR1), ALB, AKT1, MMP2, and SRC proto-oncogene (SRC) (Fig. 6A).
Rosuvastatin and CABG PPI network analysis
The network complex between rosuvastatin and CABG included 119 nodes and 1,695 edges. We identified the top ten hub genes as TNF, ALB, MMP9, AKT1, insulin-like growth factor 1 (IGF1), epidermal growth factor receptor (EGFR), CASP3, SRC, IL1B, and HRas proto-oncogene (HRAS). The top 12 bottleneck genes were ALB, AKT1, TNF, IL1b, selectin E, catalase (CAT), prostaglandin I2 synthase, MDM2 proto-oncogene (MDM2), ESR1, CASP3, platelet and endothelial cell adhesion molecule 1, and MMP9 (Fig. 6B).
Combined sirolimus, rosuvastatin, and CABG PPI network analysis
The network complex involving sirolimus, rosuvastatin, and CABG comprised 96 nodes and 1,218 edges. The top ten hub genes included TNF, ALB, MMP9, AKT1, SRC, CASP3, EGFR, HRAS, heat shock protein 90 alpha family class A member 1 (HSP90AA1), and MAPK8. The top 16 bottleneck genes were ALB, AKT1, TNF, ESR1, MMP9, IL1B, IGF1, CAT, CASP3, SRC, peroxisome proliferator activated receptor alpha, HSP90AA1, heme oxygenase 1, MDM2, serpin family A member 1, and MMP2 (Fig. 6C). Taken together, these data help predict the potential mechanisms by identifying key genes associated with sirolimus and rosuvastatin during NI formation and those influenced when both drugs are combined.
Evaluating the efficacy of sirolimus and rosuvastatin in preventing NIH in a rabbit model
To investigate the therapeutic effects of the combined use of sirolimus and rosuvastatin, we developed a localized perivascular drug delivery device that contained both drugs. This device was designed based on the results of a network pharmacology analysis. The device was strategically designed to release sirolimus initially, followed by a sustained release of rosuvastatin (Fig. S1).
Figure 7A illustrates the in vivo validation experiment design, outlining the injury and application protocol. We subjected rabbit abdominal aortas to balloon catheter-induced injury, and then applied either a control device (no drugs) or the localized perivascular drug delivery device containing SIR + RSV. No toxicity or complications were observed in any of the groups (Fig. S2).
Histological changes were observed in the H&E-stained aortic sections at 1, 2, and 4 weeks (Fig. 7B and C). In the control group, there was a significant increase in both intimal and medial thickness, as well as NI formation, over time. The intima-to-media ratio was significantly lower in the SIR + RSV group at 1 week compared to that in the control group (3.24 ± 2.08 and 43.60 ± 36.14, respectively). Additionally, after 4 weeks, the SIR + RSV group showed significant reductions in intimal thickness and NI formation compared to those of the control group (Control: 947.64 ± 429.14 mm and 31.73 ± 7.16%; SIR + RSV: 406.74 ± 196.18 mm and 13.15 ± 11.18%, respectively).
Based on these observations, TEM images at 1 and 2 weeks showed a reduction in the neointimal layer formation in the SIR + RSV group when compared to that in the control group (Fig. 7D and E; Fig. S3). At 1 week, the VSMCs in the control group displayed deformations, whereas the SIR + RSV group did not exhibit any such alterations when compared to the normal group. At 2 weeks, VSMCs had migrated within the neointimal layer in the control group. However, the VSMCs underwent cell death in the SIR + RSV group. These findings suggest that the combination of sirolimus and rosuvastatin effectively prevented NIH by inhibiting VSMC proliferation and migration.
Inhibition of pro-inflammatory factor production by sirolimus and rosuvastatin in the acute stage of NIH progression
Our network pharmacology analysis identified potential interactions between sirolimus, rosuvastatin, and the regulation of inflammatory responses associated with CABG-related disease targets. While sirolimus appeared to influence the acute stage of NIH progression via IL-6/STAT3 signaling, rosuvastatin was found to be associated with the chronic stage, primarily via TNF-α-dependent MMP9 signaling.
To validate these findings, we assessed the changes in the inflammatory response in balloon-injured rabbit models treated with a device containing sirolimus and rosuvastatin. At 2 weeks, the control group showed a significant increase in the expression of pro-inflammatory cytokines, such as IL-6, TNF-α, and IL-1β (Fig. 8). In contrast, in comparison to the control group, the SIR + RSV group demonstrated significant reductions in the expression of IL-6, TNF-α, and IL-1β at 2 weeks (4.37-, 4.95-, and 2.67-fold decreases, respectively). Notably, sirolimus and rosuvastatin effectively reduced pro-inflammatory cytokine expression at 2 weeks (acute stage), suggesting that these drugs prevent NIH by negatively regulating cytokine production.
Attenuation of STAT3 activation by sirolimus and rosuvastatin in the chronic stage of NIH progression
Following the observed impacts of sirolimus and rosuvastatin that affected pro-inflammatory cytokine production, we next investigated the role of IL-6 and STAT3, which were hub genes associated with sirolimus and CABG as identified in our PPI analysis. Previous studies have confirmed that the IL-6/STAT3 signaling pathway exhibits abnormal overactivation in chronic inflammatory conditions [41]. STAT3 activity is of paramount importance in a multitude of biological processes, including cell proliferation, apoptosis, differentiation, and VSMC phenotype switching [42, 43]. Accordingly, we investigated the alterations in protein levels of STAT3 and p-STAT3 (Fig. 9). Western blot analysis demonstrated a time-dependent increase in the expression of STAT3 and p-STAT3. In contrast, the expression levels were significantly decreased in the SIR + RSV group when compared to those in the control group at 4 weeks, with reductions of 2.53-fold and 2.04-fold, respectively. Taken together with our IL-6 expression results, we suggest that sirolimus and rosuvastatin effectively inhibit cell proliferation and survival during the chronic stage by attenuating STAT3 activation via IL-6 inhibition in the acute stage.
Modulation of MMP expression by sirolimus and rosuvastatin via the Akt1/mTOR/NF-kB signaling pathway during the chronic stage of NIH progression
To further elucidate the pro-inflammatory cytokine-related mechanisms, we investigated the roles of TNF, AKT1, and MMP9— key genes from our PPI analysis— in association with sirolimus, rosuvastatin, and CABG. TNF-α, a key modulator of VSMC phenotypic changes during NIH progression, activates the AKT/mTOR pathway and augments MMP9 expression via NF-κB activation in VSMCs [44, 45]. Therefore, we investigated the impact of combining sirolimus and rosuvastatin on factors associated with the AKT/mTOR/NF-κB signaling pathway (Fig. 10). Compared to the control group, the expression levels of Akt1, mTOR, NF-κB, and MMP9 in the SIR + RSV group significantly reduced at 4 weeks, showing 2.15-fold, 2.76-fold, 1.85-fold, and 1.79-fold reductions, respectively. Moreover, p-mTOR expression showed a decreasing trend in the SIR + RSV group (3.83-fold decrease; p = 0.0594). These findings indicate that the combination of sirolimus and rosuvastatin can effectively inhibit VSMC proliferation and prevent phenotypic alterations in the chronic stage of NIH, primarily by modulating MMP expression via the TNF-α-dependent Akt/mTOR/NF-κB signaling pathway.