Abstract Myocardial ischemia (MI) is one of the most common cardiovascular diseases with high incidence and mortality. Huang-Lian-Jie-Du-Tang (HLJDT) is a classic traditional Chinese prescription to clear “heat” and “poison”. In this study, we used a deliberate strategy integrating the methods of network pharmacology, pharmacodynamics, and metabonomics to investigate the molecular mechanism and potential targets of HLJDT in the treatment of MI. Firstly, by a network pharmacology approach, a global view of the potential compound-target-pathway network based on network pharmacology was constructed to provide a preliminary understanding of bioactive compounds and related targets of HLJDT for elucidating its molecular mechanisms in MI. Subsequently, in vivo efficacy of HLJDT was validated in a rat model. Meanwhile, the corresponding metabonomic profiles were used to explore differentially induced metabolic markers thus providing the metabolic mechanism of HLJDT in treating MI. The results demonstrated the myocardial protection effect of HLJDT on ischemia by a multicomponent-multitarget mode. This study highlights the reliability and effectiveness of a network pharmacology-based approach that identifies and validates the complex of natural compounds in HLJDT for illustrating the mechanism for the treatment of MI. Keywords: HLJDT, myocardial ischemia, network pharmacology, metabonomics, multitarget Introduction Acute myocardial ischemia (AMI) refers to a pathological state in which blood perfusion is reduced due to coronary artery occlusion, insufficient blood flow, or oxygen supply. It is one of the most common cardiovascular diseases with a high incidence rate and high mortality rate ([43]Turer and Hill, 2010; [44]Davidson et al., 2019). Studies have shown that the pathogenesis of AMI is closely related to biological pathways including energy metabolism, oxidative stress ([45]Kaul and Ito, 2004; [46]Luqman et al., 2007), apoptosis ([47]Garg et al., 2003), calcium homeostasis ([48]Katopodis et al., 1997), angiogenesis ([49]Lee et al., 2000), and inflammatory and immune responses ([50]Rui et al., 2005; [51]Arslan et al., 2011). In the theory of traditional Chinese medicine (TCM), AMI belongs to the category of “chest obstruction” and “cardialgia”, which can be treated by “invigorating the circulation of Qi and blood”, “dispersing stagnation”, and “dredging collaterals”. Various Chinese herbal medicines, such as Salvia miltiorrhiza, Rhodiolarosea, Forsythia suspensa, Sini Tang, and Tongxinluo, have been reported to improve myocardial ischemia and protect the heart ([52]Liu et al., 2013; [53]Guo et al., 2016), which indicates the advantage of TCM in myocardial protection. Nevertheless, as the multi-component, multi-target, and multi-channel synergistic characteristics of TCM, it is inappropriate to use the mode of “one drug-one target” to elaborate its mechanism of action. Moreover, it is difficult to carry out systematic research on the level of tissues, organs, cells, and molecules, which remain an obstacle for the modernization of TCM ([54]Wang et al., 2012). Network pharmacology is one of the emerging strategies based on multi-disciplinary technologies such as system biology, multi-directional pharmacology, computational biology, and network analysis, which systematically reveals the core molecular targets and pharmacodynamics methods of TCM ([55]Hopkins, 2008; [56]Li and Zhang, 2013). In this approach, the multi-level relationship between “drug-compound-target-pathway-disease” is established by using various technologies including Omics technology, high-throughput screening, network visualization, or network analysis. It helps us understand the molecular basis of disease, predicts the pharmacological mechanism, and finds herbal compounds of high efficiency and low toxicity. Huang-Lian-Jie-Du-Tang (HLJDT) is a classic traditional Chinese prescription to clear “heat” and “poison”, which was first published in Gehong’s “elbow reserve emergency prescription”. It consists of the rhizoma of Coptis chinensis Franch (Huanglian 9 g), the radix of Scutellaria baicalensis Georgi (Huangqin 6 g), the cortex of Phellodendron chinense C.K. Schneid (Huangbo 6 g) and the fructus of Gardenia jasminoides J. Ellis (Zhizi, 9 g) ([57]Shen et al., 2003). Modern pharmacology research shows that HLJDT has obvious anti-inflammatory, antibacterial, anti-endotoxin, and antipyretic effects ([58]Li et al., 2012; [59]Lv et al., 2017). Recent studies showed that HLJDT could significantly reduce cerebral ischemia injury, cholesterol, etc. ([60]Sekiya et al., 2005; [61]Wang et al., 2013) Studies showed that the effective components in HLJDT could mainly be divided into three categories: 1) alkaloids: mainly from the rhizoma of Coptis chinensis Franch and the cortex of Phellodendron chinense C.K. Schneid. Berberine and Phellodendron amurense are the focuses of current studies. 2) Flavonoids: mainly from the radix of Scutellaria baicalensis Georgi. The representative components are baicalein, scutellarin, and baicalin. 3) Iridoids: mainly from the fructus of Gardenia jasminoides J. Ellis and its representative component is gardenside ([62]Han et al., 2007; [63]Ma et al., 2009; [64]Chen et al., 2010). Although there have been many studies on the efficacy and mechanism of HLJDT, most of them focus on a single component and the multi-component and multi-target mechanism is still unclear. In this study, we used a deliberate strategy integrating the methods of network pharmacology, pharmacodynamics, and metabonomics to investigate the molecular mechanism and potential target of HLJDT for treatment of MI. Firstly, an MI-related “potential compounds-target-pathway” regulatory network was generated by network pharmacology to identify potential bioactive compounds and related targets of HLJDT. Subsequently, in vivo experimental verification of the efficacy was performed in a rat MI model by ligation of the coronary artery. The metabonomics approach was applied to explore differentially induced metabolic markers, thus providing the metabolic mechanism of HLJDT in treating MI. This study highlights the reliability and effectiveness of a network pharmacology-based approach that identifies and validates the complexity of natural compounds in HLJDT for illustrating the mechanism of action in the treatment of MI. The overall flowchart for elucidating the mechanism of HLJDT in the treatment of MI is illustrated in [65]Figure 1. FIGURE 1. [66]FIGURE 1 [67]Open in a new tab Integrated flowchart for elucidating the mechanism of HLJDT in the treatment of MI. Firstly, a global view of the potential compound–target–pathway network based on network pharmacology was constructed to provide a preliminary mechanisms prediction the of HLJDT for elucidating its molecular mechanisms in MI. Subsequently, in vivo efficacy of HLJDT was validated in a rat model. Meanwhile, the corresponding metabonomic profiles were used to explore differentially induced metabolic markers thus providing the metabolic mechanism of HLJDT in treating MI. These results will demonstrate the myocardial protection effect and illustrating the mechanism of HLJDT for the treatment of MI. Materials and Methods Network Pharmacology Analysis Predicting Potential Gene Targets of Chemical Composition in Huang-Lian-Jie-Du-Tang The chemical compositions of HLJDT were collected from the Traditional Chinese Medicine Systems Pharmacology database (TCMSP, [68]http://lsp.nwu.edu.cn/) ([69]Ru et al., 2014), which is a unique database platform of Chinese herbal medicine system pharmacology for capturing the relationship among drugs, targets, and diseases. Those meeting the criteria of bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.1 ([70]Xu et al., 2012) were used for further analysis. The HPLC chromatogram of HLJDT obtained from the methanol extract of HLJDT is shown in [71]Supplementary Figure S1. The potential gene targets of the major compounds in HLJDT were searched from the TCMSP and PubMed databases. Totally, 448 HLJDT-related targets were obtained ([72]Supplementary Table S2). MI-Associated Target Genes The target gene number (PDB ID number) was converted into a gene name corresponding to the Genecards database through the UniProt database. The key words “myocardial ischemia” or “myocardial infarction” were input into the Genecards database ([73]http://www.genecards.org, a database providing a detailed genome, proteome, transcription, genetic, and functional overview of all known and predicted human genes) and the Online Mendelian Inheritance in Man database (OMIM) to collect target genes related to MI. The common target genes were screened out as the target of HLJDT in the treatment of MI. Pathway Enrichment and Construction of the C-T-P Network In order to explain the potential role of the active components in HLJDT in gene function and signaling pathways, we used DAVID (database for Annotation, Visualization and Integrated Discovery) to analyze the KEGG pathways of the predicted targets. The results of KEGG enrichment analysis were visualized using the Omicshare online analysis platform. The components-targets-pathway (C-P-T) network of HLJDT for treating MI was constructed by utilizing the network-visualization software Cytoscapev3.6.1. Experimental Validation of Huang-Lian-Jie-Du-Tang on MI in vivo Animals Male Sprague–Dawley (SD) rats (180–200 g) were purchased from the SLAC Laboratory Animal Co., Ltd. (Shanghai, China). The animals were housed in standard pathogen-free cages in a climate-controlled environment with a 12 h light/12 h dark cycle. Sterile food and water were naturally provided. All procedures were conducted in accordance with the guidelines for care and use of laboratory animals from the Second Military Medical University and government. Animals Grouping and Drug Administration The experimental procedure of the in vivo MI rat model is shown in [74]Figure 2A. Fifty SD rats were randomly divided into five groups (10 rats per group): 1) a sham group (vehicle-treated + sham); 2) an MI group (vehicle-treated + MI); 3) a high-dose HLJDT group (800 mg/kg/day); 4) a middle-dose HLJDT group (400 mg/kg/day); and 5) a low-dose HLJDT group (200 mg/kg/day). The vehicle and HLJDT were pretreated by gavage once per day for 14 consecutive days before the establishment of the MI model. FIGURE 2. [75]FIGURE 2 [76]Open in a new tab Overlaps between different gene sets. (A) Overlaps between MI diseases genes from HLJDT-related genes; (B) Overlaps between different gene sets from MI and 4 main herbs in HLJDT prescription. RC‐Rhizoma coptidis, RS-Radix scutellariae, CP‐Cortex phellodendri, FG-Fructus gardenia. Establishment of the Rat MI Model The 50 experimental rats were anesthetized with isoflurane inhalation and needle electrodes were inserted into the subcutaneous limbs. A catheter containing 0.1% heparin saline was inserted into the left ventricle through the left common carotid artery. Electrocardiograph (ECG) and left ventricular hemodynamic changes were recorded by a BL-410 biological function experiment system and the changes of the S-T segment were used to determine the success of modeling. Endotracheal intubation and artificial ventilators were used for mechanical ventilation. The MI model was established by ligating the left anterior descending coronary artery (LADCA) ([77]Wang et al., 2002). After the LAD was exposed between the left auricle and the pulmonary conus, a small section of polyethylene tube was put through with a 4–0 black silk ligature to block LAD blood flow for 30 min. The sham group was handled using the same operation process without the ligation step. Five rats died throughout the experiment and the other 45 rats who survived were successfully modeled (the modeling rate was up to 90%), including 10 rats in the group (1), eight rats in group (2), 10 rats in group (3), eight rats in group (4), and nine rats in group (5). Sample Collection and Detection The experimental rats were sacrificed 30 min after modeling. Immediately, blood was collected from the abdominal aorta. The supernatant serum obtained was collected and stored at −20°C for analysis of the biochemical indexes including CK, MDA, SOD, LDH and IL-1β, IL-6, IL-17, and TNF-α. The remaining serum was stored at −80°C for metabonomic analysis. Simultaneously, the hearts were rapidly removed for 2,3,5-triphenyltetrazolium chloride (TTC) immersion to determine the myocardial infarct size. Metabonomic Analysis UPLC-Q-TOF/MS Measurement and Data Analysis Metabonomic analysis was performed by the Agilent 1,290 Infinity Ⅱ UHPLC system coupled to an Agilent 6545 UHD and Accurate-Mass Q-TOF/MS. Mobile phase: A: aqueous solution. B: acetonitrile solution. Flow rate: 0.35 ml/min. Column temperature: 25°C. Injection volume: 2 μL. Gradient elution condition optimized: 0–2 min, 1% B; 2–7 min, 1–15% B; 7–9 min, 15–50% B; 9–13 min, 50–95% B; 13–15 min, 95% B. Post time was set as 5 min. Mass spectrometry was operated in both positive and negative ion modes. Differential metabolites were further identified by MS/MS with collision energy of 10 v, 20 v, and 40 v. Raw data were converted by the self-contained software of the Agilent system. Then the peak was identified by the XCMS program in the R software platform. The peel data were subjected to internal standard normalization and weight normalization and the flesh data were subjected to internal standard normalization. Visualization matrices containing sample name, m/z-RT pair, and peak area were obtained. For the peel simples, 1,428 features were obtained in the positive mode. After editing, the data matrices were imported into SIMCA-P 13.0 After preprocessing, the data were analyzed by Orthogonal Partial Least Square Discriminate Analysis (OPLS-DA). Qualitative Analysis of Metabolites and Screening of Differential Metabolites The metabolites were qualitatively analyzed by searching the self-built standard substance database according to the results of the secondary spectrum and then the METLIN, HMDB, HMDB-SERUM, KEGG, CHEBI, LIPID, and other public databases were searched to provide alternative substances. In our study, the Variable Importance in Projection (VIP, threshold >1) of the first principal component of the OPLS-DA model and the p value (threshold <0.05) of Student’s t-test were used to find differentially expressed metabolites. Statistical Analysis Data were analyzed by one-way analysis of variance (ANOVA) followed by a Dunnett's test or a Kruskal-Wallis ANOVA on Ranks followed by a Dunn’s test for multiple comparisons and expressed as the means ± SEM. *p < 0.05 was considered a significant difference. Results Prediction of the Pharmacological Mechanism of Huang-Lian-Jie-Du-Tang on MI by a Network Pharmacology Approach Herbal Compounds in Huang-Lian-Jie-Du-Tang Using the TCMSP database, 429 compounds were retrieved, where 48 were in Rhizoma coptidis, 143 were in Radix scutellariae, 140 were in Cortex phellodendri, and 98 were in Fructus gardenia. With the criteria of OB larger than 30% and DL larger than 0.18, 106 chemical ingredients were screened out, where 14 were in Rhizoma coptidis, 36 were in Radix scutellariae, 37 were in Cortex phellodendri, and 15 were in Fructus gardenia. After taking out the duplicated parts, 84 chemical constituents were accepted, and their structures were retrieved from the PubChem database ([78]Supplementary Table S1). Finally, 59 chemical ingredients were screened out for further target prediction analysis ([79]Table 1). TABLE 1. Compounds in HLJDT with oral bioavailability (OB) larger than 30% and drug-likeness (DL) larger than 0.18. Comp Herbal compound OB% DL Herb Comp Herbal compound OB% DL Herb 1 Fumarine 59.26 0.83 CP1 31 Berberine 36.86 0.78 RC6 2 Cavidine 35.64 0.81 CP2 32 (R)-canadine 55.37 0.77 RC7 3 Chelerythrine 34.18 0.78 CP3 33 Berberrubine 35.74 0.73 RC8 4 (S)-canadine 53.83 0.77 CP4 34 Palmatine 64.6 0.65 RC9 5 Delta 7-stigmastenol 37.42 0.75 CP5 35 Sitosterol 36.91 0.75 RS1 6 Poriferast-5-en-3beta-ol 36.91 0.75 CP6 36 5,7,2,5-Tetrahydroxy-8,6-dimethoxyflavone 33.82 0.45 RS2 7 Beta-sitosterol 36.91 0.75 CP7 37 NEOBAICALEIN 104.34 0.44 RS3 8 Thalifendine 44.41 0.73 CP8 38 Skullcapflavone II 69.51 0.44 RS4 9 Campesterol 37.58 0.71 CP9 39 Diop 43.59 0.39 RS5 10 Rutaecarpine 40.3 0.6 CP10 40 Rivularin 37.94 0.37 RS6 11 Isocorypalmine 35.77 0.59 CP11 41 5,2′-dihydroxy-6,7,8-trimethoxyflavone 31.71 0.35 RS7 12 Phellavin_qt 35.86 0.44 CP12 42 Salvigenin 49.07 0.33 RS8 13 Dehydrotanshinone II A 43.76 0.4 CP13 43 5,2′,6′-trihydroxy-7,8-dimethoxyflavone 45.05 0.33 RS9 14 phellamurin_qt 56.6 0.39 CP14 44 Pangolin 76.26 0.29 RS10 15 Phellopterin 40.19 0.28 CP15 45 5,7,4′-trihydroxy-6-methoxyflavanone 36.63 0.27 RS11 16 Stigmasterol 43.83 0.76 FG1 46 5,7,4′-trihydroxy-8-methoxyflavone 36.56 0.27 RS12 17 Sudan III 84.07 0.59 FG2 47 5,7,4′-trihydroxy-8-methoxyflavanone 74.24 0.26 RS13 18 5-Hydroxy-7-methoxy-2-(3,4,5-trimethoxyphenyl)chromone 51.96 0.41 FG3 48 Moslosooflavone 44.09 0.25 RS14 19 3-Methylkempferol 60.16 0.26 FG4 49 Ent-epicatechin 48.96 0.24 RS15 20 Crocetin 35.3 0.26 FG5 50 Eriodyctiol (flavanone) 41.35 0.24 RS16 21 Kaempferol 41.88 0.24 FG6 51 Carthamidin 41.15 0.24 RS17 22 Ammidin 34.55 0.22 FG7 52 5,7,2′,6′-tetrahydroxyflavone 37.01 0.24 RS18 23 Mandenol 42 0.19 FG8 53 Acacetin 34.97 0.24 RS19 24 Worenine 45.83 0.87 RC1 54 DIHYDROOROXYLIN 66.06 0.23 RS20 25 Quercetin 46.43 0.28 RC10 55 Oroxylin a 41.37 0.23 RS21 26 Coptisine 30.67 0.86 RC2 56 Wogonin 30.68 0.23 RS22 27 Berlambine 36.68 0.82 RC3 57 Dihydrobaicalin_qt 40.04 0.21 RS23 28 CorchorosideA_qt 104.95 0.78 RC4 58 Norwogonin 39.4 0.21 RS24 29 Epiberberine 43.09 0.78 RC5 59 Baicalein 33.52 0.21 RS25 30 Fumarine 59.26 0.83 CP1 [80]Open in a new tab Identification of Huang-Lian-Jie-Du-Tang-Related Targets Overall, 2,138 known distinct MI-related targets were eventually collected from the GenesCard database ([81]Supplementary Table S3) and 375 were collected from the OMIM database ([82]Supplementary Table S4), which included almost all the target genes related to MI that have been identified or are being studied. The shared potential target genes of HLJDT with MI-related target genes that correspond to disease progression and treatment were considered potential targets of HLJDT. Among the 2485 MI-related target genes, 75 genes were closely related to HLJDT ([83]Table 2). [84]Figure 3 shows the number of shared overlap targets by HLJDT and MI-related targets. TABLE 2. Myocardial ischemia (MI) related genes targeted by active compounds of HLJDT. No. GC id Gene symbol Description Gifts Relevance score 1 GC06P043770 VEGFA Vascular endothelial growth factor A 56 42.46 2 GC07M095297 PON1 Paraoxonase 1 53 34.38 3 GC01M159682 CRP C-reactive protein 53 30.85 4 GC07P022765 IL6 Interleukin 6 55 29.21 5 GC07P150990 NOS3 Nitric oxide synthase 3 57 28.06 6 GC13P113105 F7 Coagulation factor VII 53 25.95 7 GC06P151656 ESR1 Estrogen receptor 1 60 22.36 8 GC04M184627 CASP3 Caspase 3 57 21.69 9 GC19P010270 ICAM1 Intercellular adhesion molecule 1 57 21.05 10 GC14P061695 HIF1A Hypoxia inducible factor 1 subunit alpha 54 20.97 11 GC01M169722 SELE Selectin E 50 18.15 12 GC03P012328 PPARG Peroxisome proliferator activated receptor γ 59 15 13 GC01P100719 VCAM1 Vascular cell adhesion molecule 1 52 14.66 14 GC01M226360 PARP1 Poly (ADP-ribose) polymerase 1 56 13.37 15 GC18M063123 BCL2 BCL2 apoptosis regulator 59 13.22 16 GC09P122370 PTGS1 Prostaglandin-endoperoxide synthase 1 53 12.81 17 GC04M148078 NR3C2 Nuclear receptor subfamily 3 group C2 54 11.91 18 GC10P094938 CYP2C9 Cytochrome P450 family 2 subfamily C9 56 11.21 19 GC01M015565 CASP9 Caspase 9 54 8.69 20 GC07P116524 CAV1 Caveolin 1 54 8.02 21 GC07M134442 AKR1B1 Aldo-keto reductase family 1 member B 54 6.9 22 GC10P045338 ALOX5 Arachidonate 5-lipoxygenase 54 6.8 23 GC14P075278 FOS Fos proto-oncogene 57 6.58 24 GC10P048306 MAPK8 Mitogen-activated protein kinase 8 56 6.33 25 GC07M045912 IGFBP3 Insulin like growth factor binding protein 3 51 6.13 26 GC02M177227 NFE2L2 Nuclear factor, erythroid 2 like 2 54 5.98 27 GC07P076302 HSPB1 Heat shock protein family B (small) member 1 57 5.76 28 GC07P144396 MGAM Maltase-glucoamylase 48 5.03 29 GC11M100943 PGR Progesterone receptor 57 5.02 30 GC02P188974 COL3A1 Collagen type III alpha 1 chain 53 4.9 31 GC03M012583 RAF1 Raf-1 proto-oncogene 62 4.42 32 GC17P066302 PRKCA Protein kinase C alpha 57 4.28 33 GC10P073909 PLAU Plasminogen activator, urokinase 58 3.96 34 GC05M132481 IRF1 Interferon regulatory factor 1 54 3.8 35 GC03M119821 GSK3B Glycogen synthase kinase 3 beta 57 3.78 36 GC11M002130 IGF2 Insulin like growth factor 2 54 3.72 37 GC11M063003 CHRM1 Cholinergic receptor muscarinic 1 52 3.64 38 GC02P201233 CASP8 Caspase 8 59 3.57 39 GC04M085990 MAPK10 Mitogen-activated protein kinase 10 57 3.56 40 GC11M001752 CTSD Cathepsin D 59 3.2 41 GC07P136868 CHRM2 Cholinergic receptor muscarinic 2 54 3.14 42 GC11P067583 GSTP1 Glutathione S-Transferase pi 1 57 3.13 43 GC11M065671 RELA RELA proto-oncogene, NF-KB subunit 56 3.07 44 GC05M143241 NR3C1 Nuclear receptor subfamily 3 group C1 57 2.76 45 GC11P113974 HTR3A 5-Hydroxytryptamine receptor 3A 53 2.65 46 GC07M099759 CYP3A4 Cytochrome P450 family 3 subfamily A4 56 2.6 47 GC17M037084 ACACA Acetyl-CoA carboxylase alpha 55 2.53 48 GC17P007282 SLC2A4 Solute carrier family 2 member 4 53 2.51 49 GC01P109687 GSTM1 Glutathione S-Transferase mu 1 48 2.42 50 GC07M100889 ACHE Acetylcholinesterase (cartwright blood group) 52 2.34 51 GC08M026747 ADRA1A Adrenoceptor alpha 1A 53 2.31 52 GC08P127735 MYC MYC proto-oncogene 59 2.23 53 GC04P003804 ADRA2C Adrenoceptor alpha 2C 51 2.17 54 GC17P078214 BIRC5 Baculoviral IAP repeat containing 5 53 2.16 55 GC0XP067544 AR Androgen receptor 59 2.11 56 GC11P069641 CCND1 Cyclin D1 59 2.01 57 GC14M035401 NFKBIA NFKB inhibitor alpha 57 1.98 58 GC14M064084 ESR2 Estrogen receptor 2 56 1.9 59 GC07P055019 EGFR Epidermal growth factor receptor 62 1.82 60 GC05P087267 RASA1 RAS P21 protein activator 1 53 1.66 61 GC17P039687 ERBB2 Erb-B2 receptor tyrosine kinase 2 62 1.64 62 GC02M038034 CYP1B1 Cytochrome P450 family 1 subfamily B1 56 1.63 63 GC16M069706 NQO1 NAD(P)H quinone dehydrogenase 1 56 1.5 64 GC08P042247 IKBKB Inhibitor of nuclear factor kappa B kinaseβ 59 1.42 65 GC01M070852 PTGER3 Prostaglandin E receptor 3 53 1.42 66 GC01P239386 CHRM3 Cholinergic receptor muscarinic 3 55 1.32 67 GC02P074796 HK2 Hexokinase 2 53 1.25 68 GC08P144291 HSF1 Heat shock transcription factor 1 51 1.25 69 GC11M065909 FOSL1 FOS like 1, AP-1 transcription factor subunit 50 1.2 70 GC15M074719 CYP1A1 Cytochrome P450 family 1 subfamily A1 53 0.78 71 GC17M082078 FASN Fatty acid synthase 56 0.73 72 GC07P016916 AHR Aryl hydrocarbon receptor 56 0.64 73 GC19P040991 CYP2B6 Cytochrome P450 family 2 subfamily B6 55 0.6 74 GC07M081946 CACNA2D1 Calcium voltage-gated channel auxiliary subunit α21δ 53 0.3 75 GC0XM047635 ELK1 ETS transcription factor ELK1 50 0.26 [85]Open in a new tab FIGURE 3. [86]FIGURE 3 [87]Open in a new tab KEGG enrichment analysis of the predicted targets of HLJDT for MI treatment. Dot plot showing the top 20 KEGG pathways: the size of the dots corresponds to the number of genes annotated in the entry, and the color of the dots corresponds to the corrected P‐value. Enrichment Analysis and Construction of a Regulatory Network The potential target genes of HLJDT obtained above were then imported for KEGG pathway enrichment to explore potential signaling pathways for HLJDT in treating MI. The top 20 potential signaling pathways are listed in [88]Figure 4. Among them, nine pathways including the MAPK signaling pathway (hsa04010), the PI3K-Akt signaling pathway (hsa04151), apoptosis (hsa04210), the IL-17 signaling pathway (hsa04657), Th17 cell differentiation (hsa04659), the sphingolipid signaling pathway (hsa04071), the Ras signaling pathway (hsa04014), the TNF signaling pathway (hsa04668), and fluid shear stress (hsa05418) were mainly found to be involved in apoptosis, inflammation, immunity, or oxidative stress biological processes related to MI. FIGURE 4. [89]FIGURE 4 [90]Open in a new tab Potential Compound-Targets-Pathway (pC-T-P) network of HLJDT in the treatment of MI. There were 4 kinds of herbs, 59 compounds, 42 predicted targets and 9 signaling pathways on the network. Green hexagon represents 4 kinds of herbs of in HLJDT, every color represents the single medicine. Diamond represents compounds, each color represents one herb in HLJDT; Gray circle represents potential targets, while the red triangle represents the predicted signaling pathways. RC, Rhizoma coptidis; RS, Radix scutellariae; CP, Cortex phellodendri; FG, Fructus gardenia. A global view of the potential compound–target–pathway (C-T-D) network including 114 nodes (4 herbs, 59 compounds, 42 predicted targets, and nine signaling pathways) and 270 edges was constructed ([91]Figure 5) to further clarify the specific mechanism of HLJDT. This network presented the complex relationship among the active components of HLJDT, the target genes, and the related predicted pathways. The top components with the highest number of connections to target nodes were quercetin (RC10) in the rhizoma of Coptis chinensis, kaempferol (FG6) in the fructus of Gardenia jasminoides J. Ellis; oroxylin (RS21), wogonin (RS22), and baicalein (RS25) in the radix of Scutellaria baicalensis Georgi, and β-sitosterol (CP7) in the cortex of Phellodendron chinense C.K. Schneid. Meanwhile, some of the target genes were involved in diverse pathways, for example, MAPK8 participated in diverse pathways, including apoptosis, the PI3K-Akt pathway, the TNF pathway, and the MAPK pathway, while IL-17 was involved in the IL-17 signaling pathway, Th17 cell differentiation, and apoptosis, etc. These results indicated that HLJDT probably acted on MI in a multi-target, multi-pathway, and overall integrative mode. FIGURE 5. [92]FIGURE 5 [93]Open in a new tab HLJDT reduced infarct size after MI. (A)The experimental procedures of in vivo MI rat model; (B) Representative photographs of TTC stained heart slices obtained after MI; (C) Graphic representation of myocardial infarct size. (B) Bar graphic representation of statistical analysis myocardial infarct size (n < 6). All data expressed as mean ± SEM, n=6/group. **p<0.01, ***p<0.001 vs. MI group In vivo Validation of the Efficacy of Huang-Lian-Jie-Du-Tang The rat MI model was established to evaluate the potential myocardial protection of HLJDT. Representative photographs of TTC-stained heart slices and the corresponding quantitative statistical analysis of myocardial infarct size obtained after MI are shown in [94]Figures 2B,C, respectively. The myocardial infarction area of the MI group was significantly increased after ischemia-induced injury compared with the sham group. As expected, the average areas of infarct were significantly reduced after pretreatment with HLJDT in a dose dependent manner. Meanwhile, the MI group exhibited elevated levels of cardiac markers such as creatine kinase (CK), CM-KB, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), anti-oxidant enzymes superoxide dismutase (SOD), inflammation, and immune related factors: interleukin-6 (IL-6), IL-1β, IL-17, and TNF-α, while the MI group exhibited reduced levels of malondialdehyde (MDA). Oral administration of HLJDT could significantly improve the above related levels ([95]Figures 6A–H). The results showed that HLJDT exerted a potential preventive effect against MI. FIGURE 6. [96]FIGURE 6 [97]Open in a new tab The effect of HLJDT on the release of serum biochemical indicators. (A) Creatine kinase (CK); (B) CK-MB; (C) aspartate aminotransferase (AST); (D) lactate dehydrogenase (LDH); (E) malondialdehyde (MDA); (F) superoxide dismutase (SOD); (G) IL-6; (H) IL-1β; (I) TNF-α; and (J) IL-17 (n = 6). Results were presented as mean ± SEM. ##p < 0.01, ###p < 0.001 vs. Sham group; **p < 0.01, ***p < 0.001 vs. MI group. Metabonomic Analysis Identification of Potential Metabolites To identify the potential metabolic markers of HLJDT, the total ion chromatograms (TICs) of serum were extracted from the sham and MI groups. The OPLS score plot proved that the sham group were significantly different from the MI group (R2X = 0.168, [98]Figure 7A). Moreover, the relative S-plot ([99]Figure 7B) reflected the influences of variables on inter-group difference. Those points far from the origin contribute to inter-group difference significantly and correspond to larger VIP values. The S-plot showed the distribution of biomarkers with significant difference. The endogenous metabolites were identified by comparing them with standard references and mass