Investigating Celastrol's Anti-DCM Targets and Mechanisms via Network Pharmacology and Experimental Validation.

Methods: Data from TCMSP and GEO databases were utilized to identify targets for Celastrol on DCM. The relationship between the major targets and conventional glycolipid metabolism was obtained with Spearman correlation analysis. Experiments on animals were conducted utilizing healthy control (HC), low-dose Celastrol interventions (CL), and no intervention groups (NC), all of which had 8 SD rats in each group. To study alterations in signaling molecules, RT-PCR was performed.
Results: There were 76 common targets and 5 major targets for Celastrol-DCM. Celastrol have been found to regulate AGE-RAGE, TNF, MAPK, TOLL-like receptors, insulin resistance, and other signaling pathways, and they are closely linked to adipocytokines, fatty acid metabolism, glycolipid biosynthesis, and glycosylphosphati-dylinositol biosynthesis on DCM. These five major targets have been found to regulate these pathways. Experiments on rats indicated that P38 MAPK was considerably elevated in the cardiac tissue from rats in the CL and NC groups compared to the HC group, and the difference was statistically significant (P < 0.01). Significant differences were seen between the CL and NC groups in P38 MAPK levels, with a statistical significance level of less than 0.05.
Conclusion: Celastrol may play a role in reversing energy remodeling, anti-inflammation, and oxidative stress via modulating p38 protein expression in the MAPK pathway, which have been shown in the treatment of DCM.

1. Introduction

DCM involves a range of changes caused by diabetes mellitus (DM) to the myocardial structure and function [1]. The increase of fatty acid-oxidation and decrease of glucose oxidation in ventricular myocytes are the significant characteristics of DCM energy rebuilding. In some research, it has been shown that myocardial energy restoration is conducive to enhancing DCM development [2-4]. As a leading cause of diabetes-related mortality, DCM can have a substantial impact on the well-being of patients [5, 6]. Cardiac remodeling and dysfunction may be alleviated by early intervention with the rebuilding of myocardial energy (changing the ratio of myocardial glucose to fatty acid metabolism) [7-10]. Besides, there are more and more studies confirming that the early intervention with DCM energy restoration plays an essential role in preventing myocardial remodeling and cardiac dysfunction [11, 12]. Celastrol, which can produce various biological effects, has been reportedly applied for the prevention and treatment of tumors, lupus erythematosus, rheumatoid arthritis, etc. [13-15]. Additionally, Celastrol is regarded as having a massive potential to serve as a contemporary medicine for treatment by “CELL” due to its diverse effects [16]. However, there remains a lack of clarity on whether Celastrol can play a protective role in the early energy remodeling of DCM and its underlying mechanism. Besides, network pharmacology is a relatively new subject of study that links the disciplines like pharmacology, bioinformatics, and system biology. It provides an effective solution to revealing the mechanism of effect produced by those compounds used in traditional Chinese medicine. It entails an analysis as to the mechanism of medication-induced effects and the organisms in aggregate by establishing a “disease-target-drug” network. Thus, it is hypothesized that triptolide Celastrol is applicable to the reversal of early energy remodeling in DCM. This paper is purposed to predict the targets and underlying mechanism of Celastrol in reversing the early energy remodeling for DCM. So, network pharmacology concepts and methodologies were applied in this study to explore the key targets of Celastrol against DCM, as well as the pathways involved in the mechanism of action of DCM [17-19]. According to the relevant principles and methods of network pharmacology [17, 20], we not only analyzed the main targets of Celastrol on DCM but also applied the research methods of “Li et al.” to predict and analyze its enriched biological processes and signaling pathways [17]. Finally, during animal experiments, PCR technology was applied to confirm the content of specific molecular genes in individual signaling pathways (Figure 1).

Figure 1

A schematic diagram showing analysis performed in this study. Network pharmacology methods were used to identify effective targets of Celastrol through TCMSP database, Swiss Target database, and Batman-TCM database. Potential effective targets related to DCM were then identified using DisGeNET database, OMIM database, GeneCard database, and GEO database (GSE4745). Further, common targets of Celastrol and DCM were determined using a Venn diagram. STRING tool was used to construct PPI networks for targets of Celastrol and DCM, and visualization was done using Cytoscape 3.7.1. KEGG enrichment analysis was performed to explore signaling pathways of the drug and DCM that are implicated in energy reconstruction. The metabolic pathway level of each sample was quantified based on ssGSEA, and the relationship between major targets and energy metabolism was explored through Spearman correlation analysis. Experiments on animals were conducted to study alterations in signaling molecules.

2. Materials and Methods

2.1. Database and Software

Databases and software used in this study include TCMSP database (http://lsp.nwu.edu.cn/tcmsp.php), Swiss Target database (http://www.swisstargetprediction.ch/), Batman-TCM database (http://bionet.ncpsb.org/batman-tcm), DisGeNET database (https://www.disgenet.org/), OMIM database (https://omim.org/), GeneCard database (https://www.genecards.org/), GEO database (GSE4745) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4745), R 3.6.1 software, Venny2.1 software (http://bioinfogp.cnb.csic.es/tools/venny/index.html), STRING online database 11.0 (https://string-db.org), and NetworkAnalyzer in the Cytoscape software (version 3.7.1).

2.2. Screening of Celastrol Effect Targets

Celastrol was retrieved from the TCMSP database, and DCM targets were retrieved from the Swiss Target database and the Batman-TCM database. Gene names and ID corresponding to all drug targets were recorded for further network pharmacological data analysis.

2.3. DCM Targets

“Diabetic cardiomyopathy” was used as the keyword to retrieve target genes related to DCM from DisGeNET database, OMIM database, and GeneCard database. Retrieved data were sorted out and included in the GSE4745 differential gene data set. Redundant and duplicate targets were deleted.

2.4. Screening of Drug-DCM Interactive Targets

Venn diagram of Celastrol and DCM disease targets was constructed using Venny 2.1 online tool (http://bioinfogp.cnb.csic.es/tools/venny/index.html). Protein names of interactive targets were obtained then obtained from the Venn diagram.

2.5. Construction of “Drug-Target-DCM” Network

Major targets of Celastrol against DCM were explored using STRING online database 11.0 (https://string-db.org), with a confidence score > 0.4 as the separating point standard. A network diagram of “drug-target-disease” interaction and PPI network was then constructed. Topological parameters in the network, such as median degree and maximum degree, were analyzed using NetworkAnalyzer in Cytoscape (version 3.7.1). Major targets were identified based on the degree. The upper limit of the range was the largest degree in the topological data, and the lower limit was twice the median value of the degree. The Cytoscape software was used for visualization of the PPI network.

2.6. GO and KEGG Functional Annotation and Analysis

Functional annotation of major targets was carried out using “ClusterProfiler,” R package, to comprehensively explore related function of these targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore the relevant functional categories. GO function analysis was mainly used to describe the function of major targets, including cell localization, molecular function, and biological process. KEGG enrichment analysis was used to explore enriched signaling pathways related to the common targets of Celastrol and DCM. GO and KEGG enrichment paths with P and q values less than 0.05 were considered as significant categories.

2.7. Correlation Analysis between Target and Metabolic Pathway

The metabolic path level of each sample was quantified using ssGSEA. Spearman correlation analysis was carried out to determine correlation between gene expression quantity and metabolic pathway.

2.8. Validation of Animal Tests

2.8.1. Main Reagents and Instruments

These are as follows: Streptozotocin (STZ), Sigma. Celastrol, Chengdu Destructive Biotechnology Co., and high-sugar and high-fat feed (58.8% nutrient rich base feed, 20% lard, 1% cholesterol, 0.2% bile salt, and 20% sugar), Nanjing Shengmin Research Animal Farm. Blood glucose meter and test strips, Stable type, were manufactured by China Sanuo Biosensing Co.

2.8.2. Experimental Animals and Groups

16 DCM and 8 healthy control SD rats, male, 200-230 g, were kept in rat cages with a temperature range of 18-26°C, a relative humidity range of 40-70%, a noise level of less than 85 dB, and an ammonia content of less than 20 ppm. They were given a breather every 8-12 hours. Sixteen model rats were randomly assigned to the Cel low-dose group (CL) or the control group that received no treatment (NC). High-sugar, high-fat diets were given to 8 rats from each group. Modeling had been completed on day 1, and therefore, treatment could begin on day 2. For the HC group, the regular diet was maintained, whereas the CL group received 50 μg/kg of body weight per day. After 4 weeks of high sugar and fat diet, the rats in the NC group were given a lethal injection. All animal experiments involving rats were performed following the Principles of Laboratory Animal Care (Peoples Republic of China), and the animal protocols were approved by the Ethics Committee of Shanxi Medical University (No. SYDL2019002).

2.8.3. Quantitative Real-Time Fluorescence PCR Assay (RT-PCR) for P38 MAPK Content

A total of roughly 100 mg of fresh tissue was taken, frozen, and kept at -80°C. Total RNA was isolated from the obtained cardiac tissue using Trizol reagent (#15596-026, Ambion, California, USA) and kept at -80°C in the refrigerator as a backup. First-strand cDNA was synthesized from total RNA using 4× gDNA wiper mix (#R223-01, VAZYME, Nanjing, China). 5× HiScript II Select qRT SuperMix II (#R233-01, VAZYME, Nanjing, China) was used to detect amplification of specific PCR products according to the manufacturer's instructions. It was done by electrophoresis with GelRed and DNA markers in GelRed, which resulted in separation of PCR products from each other. As a control, GAPDH was employed. The manufacturer's instructions were followed for doing real-time quantitative PCR using SYBR Green Master Mix (#R223-01, VAZYME, Nanjing, China). Data were normalized based on β-actin mRNA levels. The final specific primer sequences (SANGON, Shanghai, China) are listed in Table 1 below.

Table 1

Primer sequence list of RT-PCR.

Gene	Primer	Sequence (5′-3′)	PCR products
GAPDH	Forward	ACAGCAACAGGGTGGTGGAC	253 bp
	Reverse	TTTGAGGGTGCAGCGAACTT
p38 MAPK	Forward	ATGTCGCAGGAGAGGCCCACGTTCT	188 bp

The primers were synthesized by Shanghai Sangon Biological Engineering Technology Company Limited.

2.9. Statistical Analysis

R was used to do network pharmacology data analysis (version 3.6). All statistical tests were two-sided, and P < 0.05 was deemed statistically significant in all cases. We conducted several basic tests and utilized the SPSS 26.0 statistical analysis software to analyze the data, with continuous variables reported in terms of mean ± standard deviation, while comparisons between two groups that satisfied the normality test were assessed by samples t. For numerous comparisons between groups that passed the homogeneity test for variances, one-way ANOVA was used. LSD-t was used for two comparisons between groups that had identical variances. When the variance normality and homogeneity tests were not fulfilled, nonparametric tests were used to compare data from different groups. The Kruskal-Wallis test may be used to compare two groups, and differences are regarded statistically significant if P < 0.05.

3. Results

3.1. Drug and DCM Target Prediction

A total of 161 targets associated with Celastrol were retrieved from the TCSMP database, the Swiss Target database, and the Batman database (Supplementary data 1). Differential genes of DCM were evaluated using the GSE4745 data set in the GEO database. The differential screening conditions were logFC > 1 and P < 0.05. A total of 59 differential genes were identified, including 35 upregulated genes and 24 downregulated genes (Supplementary Table 1). Targets related to DCM were explored using the DisGeNET database, OMIM database, and GeneCard database (relevance score > 5), and a total of 2075 targets related to DCM were retrieved (Supplementary data 2). Furthermore, the intersection of all targets was determined using a Venn diagram, and a total of 76 drug-DCM interactive targets were obtained (Figure 2, Supplementary Table 2). Pharmacological targets and function-related PPI networks of Celastrol on DCM were constructed.

Figure 2

Venn diagram of common targets of Celastrol and DCM. There were 161 targets of Celastrol that can prevent and treat disease, 2075 targets for the prevention and treatment of DCM, and 76 common targets for Celastrol and diabetic cardiomyopathy (DCM).

3.2. Construction of PPI Network and Screening of Key Target Genes

A total of 76 Celastrol-DCM common targets were uploaded into the STRING database for PPI network analysis. A median degree of 24.5, the maximum degree of 57, and a screening range of core gene connectivity of 49-57 were obtained. The analysis showed a total of five major targets of Celastrol on the DCM network, namely, IL6, VEGFA, TNF, CASP3, and MAPK8 (Figure 3, Supplementary Table 3).

Figure 3

Relationship between the five key targets. Celastrol key targets against DCM were IL6, VEGFA, TNF, CASP3, and MAPK8.

3.3. GO Functional Enrichment Analysis

GO enrichment analysis of major targets was carried out using the R software, and data were presented as a histogram, bubble diagram, and chordal graph (Figure 4). Color-code in the histogram indicates the significance of the P value of the corresponding gene. Deep red color represents highly enriched genes (Figure 4(a)). The abscissa of the bubble diagram represents the percentage of genes. A larger circle represents a higher enrichment number, and deep red color implies that the expression level of the gene is significantly high (Figure 4(b)). GO enrichment analysis showed that five major targets are mainly involved in biological processes such as response to reactive oxygen species (ROS), peptidyl-serine phosphorylation, peptidyl-serine modification, lipopolysaccharide, molecules of bacterial origin, oxidative stress, and regulation of cell-cell adhesion, DNA-binding transcription factors, peptidyl-serine phosphorylation, and leukocyte migration (Supplementary Table 4).

Figure 4

GO enrichment analysis of biological processes associated with the five major targets of Celastrol on DCM. The top 30 biological processes (BP) with P < 0.05 were identified using R packages. (a) Histograms, (b) bubble diagram, and (c) chordal graph showing top 30 enriched BP categories. The x-axis represents enriched gene count or ratio, whereas the y-axis represents the different biological processes. Color intensity represents adjusted P value.

3.4. KEGG Enrichment Analysis

The five major targets were identified through KEGG pathway analysis using the R software ClusterProfiler package. Previous studies report that pathways related to energy reconstruction are associated with DCM including AGE-RAGE signaling pathway, TNF signaling pathway, MAPK signaling pathway, TOLL-like receptor, insulin resistance, and NOD-receptor signaling pathway (Figure 5, Supplementary Table 5).

Figure 5

KEGG enrichment analysis for five key targets implicated in the role of Celastrol in DCM. Top 30 signaling pathway with P < 0.05 were identified using R packages. (a) Histograms, (b) bubble diagram, and (c) chordal graph showing top 30 enriched signaling pathways. The x-axis represents enriched gene count or ratio, and y-axis represents the different biological processes. Color intensity represents adjusted P value.

3.5. Visualization of Drug and DCM Targets

The network visualization “Celastrol-target-DCM” interaction diagram (Figure 6) was generated using the Cytoscape software. The network showed that Celastrol regulates DCM by modulating five major targets, namely, VEGFA, MAPK8, IL6, CASP3, and TNF. This finding implies that Celastrol is implicated in abrogation of the pathogenesis of DCM.

Figure 6

Network visualization of detailed interactions of Celastrol-target-GO-KEGG-DCM. Orange regions on both sides show the top 30 biological processes and the top 30 signal pathways in implicated in the mechanism of Celastrol in DCM. The upper green region represents Celastrol, the middle red region represents the 5 key targets, and the lower region represents DCM.

3.6. Analysis of the Relationship between Targets and Metabolic Pathways

Spearman correlation analysis showed that the five major targets had different degrees of correlation with disease energy metabolism. Specific manifestations observed were as follows: VEGFA was correlated with adipocytokines, lipid metabolism, fatty acid metabolism, glycolipid biosynthesis, glycosylphosphatidylinositol biosynthesis, and insulin signaling pathway whereas TNF was correlated with adipocytokines, lipid metabolism, and insulin signaling pathway. In addition, MAPK8 was correlated with fatty acid metabolism and glycolipid biosynthesis; IL6 was correlated with peroxidase activity whereas CASP3 was correlated with lipid metabolism, fatty acid metabolism, and glycolipid biosynthesis (Figure 7).

Figure 7

Correlation analysis between target gene expression levels and metabolic pathways. Correlation is color-coded; a value greater than 0 indicates a positive correlation whereas a value less than 0 indicates a negative correlation; ∗P < 0.05 and ∗∗P < 0.01.

3.7. Experimental Validation

RT-PCR analysis revealed that the ratio of P38 MAPK/GAPDHA was significantly increased in the DCM group compared with the HC group (1.71 ± 0.08 vs. 0.61 ± 0.43, P < 0.01), whereas the ratio of P38 MAPK/GAPDHA was significantly decreased in the CL group compared with the DCM group after Celastrol administration (0.67 ± 0.49 vs. 1.71 ± 0.08, P < 0.01) (Figure 8). This indicates that low-dose Celastrol may also have a modest effect on the expression of the P38 MAPK gene in the myocardial tissue of DCM to some extent, suggesting a novel target for intervention in the energetic remodeling of DCM and progression of the disease.

Figure 8

RT-PCR-based myocardial P38 MAPK protein expression for comparison. HC is the healthy control group, CL is the low-dose Celastrol intervention group, and NC is the nonintervention group. ∗∗P < 0.01 compared with HC group; ##P < 0.01 compared with NC group.

4. Discussion

Diabetes mellitus has now become a severe public health concern worldwide [20], with the total cases of diabetics estimated to reach 629 million by 2045 [21]. DCM, as one of the primary complications and causes of mortality, was initially postulated in 1972 [22] and has since become a focus of clinical and fundamental research, attracting the attention of clinicians and researchers [23]. Furthermore, it has been confirmed by both clinical and experimental studies that various pathophysiological changes can result from myocardial energy remodeling, reduced sugar and lactate metabolism, and increased lipid metabolism, such as oxidative stress, apoptosis, endothelial dysfunction, increased inflammatory response, blood hypercoagulation, and myocardial fibrosis, as well as the damage caused to mitochondria, cells, and microcirculation, interstitial fibrosis, and the structural and functional changes in the myocardium. It is manifested as the catastrophic repercussions like microvascular and macrovascular cardiac issues, cardiac arrhythmias, cardiac insufficiency, and even heart failure, all of which are the major causes of mortality and have a significant impact on the quality of life for diabetics [6, 11, 24]. At present, there are still no definitive diagnostic markers or preventative measures for DCM [25]. As one of the main components of Tripterygium wilfordii, Celastrol has been recently reported in some studies to prevent and treat diabetic nephropathy, insulin resistance, and obesity [26-31]. However, there is still no clarity provided on whether Celastrol has a protective effect on early energy remodeling in DCM. In addition, network pharmacology, as an emerging drug research discipline that involves cell biology, pharmacology, and systems biology, was first proposed by Chinese scholars in 2007 and then became a research hotspot in 2017. The World Federation of Chinese Medicine Societies approved and published the “Guidelines for Network Pharmacology Evaluation Methods.”

On the basis of what has been discussed above, a “drug-target-disease” coexpression network was established by applying network pharmacology and taking bioinformatics approaches, so as to predict the key targets of Celastrol for the treatment of DCM, with five significant targets explored. According to the additional enrichment study of DCM through GO and KEGG, the primary signaling pathways involved in its regulation include AGE-RAGE, TNF, MAPK, TOLL-like receptor, insulin resistance, and the NOD-receptor signaling pathway. Therefore, Celastrol is expected to produce a protective impact on DCM through a variety of signaling mechanisms. Both glucolipid metabolism and cardiomyocyte contractile performance can be affected by insulin resistance [32], as demonstrated by the interaction with MAPK and PI3-K signaling mechanisms. The metabolic actions of insulin are largely dependent on the role of PI3-K in regulating glucose, lipid, and protein metabolism [33, 34]. As a “transmitter” in the signaling network of eukaryotic organisms, MAPK is involved in a variety of signaling pathways, including those for cell proliferation, stress, inflammation, differentiation, functional synchronization, transformation, and death [35]. As one of the most significant components of inflammatory and oxidative stress signaling pathways, MAPK phosphorylation is presumed to play a role in cell proliferation, survival, necrosis, and other pathological events [6]. Mitogen-activated protein kinase p38 (P38 MAPK), which is a critical molecule in the MAPK signaling pathway [36], plays a critical role in energy remodeling in T2DM, IR, and survival status of pancreatic β-cells. Triggered by elevated glucose, free fatty acids, and inflammatory substances, it may contribute to the mitogenic and growth-promoting activities of insulin. DCM microangiopathy, myocardial interstitial fibrosis, and myocardial hypertrophy all rely on it for development.

It has been shown in some studies that even the minimum dosage of Celastrol may significantly reduce the damage caused by diabetes-induced nephropathy. In order to exert its effects, Celastrol is involved to modify the MAPK/NF-B signaling pathway [37]. Under the regulation of DCM, the MAPK signaling pathway plays a critical role [38, 39]. At the same time, the MAPK/NF-κB signaling pathway is also crucial to the regulation of energy metabolism in DCM. Caused by abnormal glucose uptake, cardiac dysfunction is associated with the upregulation of p38 MAPK and the increase of ROS. The upregulation of ROS in diabetes can lead to the activation of p38 MAPK, which reduces the activity of insulin receptors, thus affecting the synthesis and expression of glucose transporters in cardiomyocytes through phosphatidylinositol 3. This results in myocardial mitochondrial dysfunction and the reduced efficiency of myocardial substrate production [38]. In the model rats (CL and NC groups), P38 MAPK expression was found much higher than in the HC group, indicating that P38 MAPK can be implicated in the development of DCM illness. Following the administration of Celastrol, however, P38 MAPK expression declined significantly. P38 MAPK activity was suppressed in comparison with baseline, while myocardial disruption, cell swelling, and fibrosis were alleviated, indicating the potential of Celastrol to protect against DCM by inhibiting P38 MAPK activation and acting on the insulin resistance pathway.

5. Conclusions

We examined the major targets and potential molecular mechanisms of Celastrol's reverse regulation of energetic remodeling of DCM from a variety of perspectives in this study and concluded that Celastrol may exert protective effects on DCM via a variety of signaling pathways. By modulating insulin resistance signaling, P38 MAPK may have a complicated regulatory function in numerous areas of energy remodeling and inflammation in DCM. This gives theoretical justification for the innovative use of Celastrol in DCM and directs future research.

6. Limitations

The limitations of this study include the small sample size and the lack of experimental studies on activation and inhibition at the cellular level for the specific mechanism of Celastrol's reverse regulation of early energy remodeling in DCM.