Rosa davurica Pall. as a Functional Food in Ameliorating Acute Myocardial Ischemia: An Integrated Multi‐Omics Study on TNF‐α and Oxidative Stress

ABSTRACT

Rosa davurica Pall. (RDP), a rosa fruit rich in bioactive compounds, has shown potential in alleviating cardiovascular diseases, yet its chemical composition and mechanisms against acute myocardial ischemia (AMI) remain unclear. This study comprehensively characterized 99 compounds in RDP extracts using ultra‐high performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UPLC‐Q‐TOF‐MS/MS), including flavonoids, terpenoids, and phenolic acids. By integrating transcriptomics and network pharmacology, we identified the tumor necrosis factor‐alpha (TNF‐α) signaling pathway as a key target in AMI progression. In vivo experiments demonstrated that RDP significantly ameliorated isoproterenol (ISO)‐induced myocardial injury by reducing oxidative stress and suppressing the release of inflammatory cytokines (IL‐6, IL‐2, IL‐17). Molecular docking and dynamics simulations further revealed that bioactive components in RDP, such as rutin and hyperoside, effectively bind to TNF‐α and its receptor TNF receptor 1 (TNFR1), thereby modulating inflammatory responses. These findings provide valuable insights into the multi‐target cardioprotective effects of RDP, supporting its application as a promising functional food.

Rosa davurica Pall. (RDP) effectively ameliorates acute myocardial ischemia by neutralizing oxidative stress and disrupting the pro‐inflammatory TNF‐α/TNFR1 signaling axis. By integrating multi‐omics profiling, molecular dynamics, and in vivo models, this study uncovers the multi‐target cardioprotective mechanisms of RDP, establishing a robust scientific foundation for its application as a novel cardiovascular functional food.

1. Introduction

Intense exercise and severe emotional fluctuations represent two common types of physiological and psychological stressors. These factors can trigger a sudden release of catecholamines, leading directly to coronary artery spasm and the onset of acute coronary syndrome [1]. This process abruptly disrupts the myocardial oxygen supply‐demand balance and sharply increases myocardial oxygen consumption, ultimately inducing acute myocardial ischemia(AMI) [2]. As a global health challenge, acute myocardial ischemia has become a leading cause of cardiovascular‐related mortality worldwide, with a notably rising incidence in China [3, 4]. Although conventional pharmacological therapies, such as β‐blockers and antiplatelet agents, are widely utilized in clinical practice, their long‐term application is often limited by adverse effects like bradycardia, gastrointestinal bleeding, and drug resistance, rendering current treatment strategies suboptimal in terms of both efficacy and safety [5].

Within the evolving landscape of health and nutrition, antioxidant foods have garnered significant interest for their ability to modulate oxidative stress and inflammation. A growing body of evidence highlights the remarkable antioxidant properties of fruits belonging to the Rosaceae family. Rosa roxburghii Tratt ameliorates hepatic injury induced by oxidative, Aronia melanocarpa is known to mitigate oxidative stress across various tissues [6, 7]. Furthermore, sprig Rosa rugosa extract not only protects hepatocytes from oxidative damage, inflammation, and apoptosis but also, in the form of its total flavonoids, confers cardioprotection against myocardial ischemia/reperfusion injury by alleviating associated oxidative stress, underscoring its pleiotropic health benefits [8].

Rosa davurica Pall. (RDP) are a type of medicinal plant that is primarily found in northeastern China, South Korea, and eastern Siberia, Russia. They belong to the Rosaceae family and the Rosa genus. These fruits are characterized by a high content of bioactive compounds, including flavonoids, terpenoids, and organic acids [9, 10, 11, 12]. RDP has been demonstrated to have therapeutic effects in the treatment of diabetes, hyperlipidemia, and skin inflammation [13, 14, 15]. In addition, the extracts from this plant have been shown to possess remarkable antioxidant and anti‐inflammatory properties [16, 17, 18]. Contemporary pharmacological studies have demonstrated that RDP fruit extracts possess the capacity to ameliorate DNCB‐induced skin inflammation by modulating the NF‐κB signaling pathway. It has been demonstrated by our research group that RDP has the capacity to protect against acute liver injury [19]. This finding serves to underscore the remarkable antioxidant and anti‐inflammatory properties of RDP, which may offer a promising avenue for the treatment of acute myocardial ischemia. However, the molecular basis underlying these pharmacological effects remains to be fully elucidated.

Tumor necrosis factor‐alpha (TNF‐α), a pleiotropic pro‐inflammatory cytokine, plays a critical regulatory role in cardiac stress responses and homeostatic maintenance [20]. Primarily produced by activated macrophages and cardiomyocytes themselves, TNF‐α expression is rapidly and markedly upregulated during acute myocardial ischemic injury [21]. Myocardial ischemia/reperfusion has been identified as one of the most potent inducers of TNF‐α expression. Under pathological conditions, excessive TNF‐α production binds to its specific receptor TNFR1, activating downstream signaling pathways such as NF‐κB and MAPK, thereby exacerbating local inflammatory responses, promoting cardiomyocyte apoptosis, and negatively regulating cardiac contractile function [22]. These pathological processes collectively contribute to expanded myocardial infarction areas and compromised cardiac function.

Given that the core pathogenesis of AMI is fundamentally driven by oxidative stress mediated inflammatory cascades. RDP has been extensively recognized for its potent antioxidant and anti‐inflammatory capacities, we reasonably speculate that RDP could mitigate AMI by therapeutically targeting these interconnected pathways. To systematically test this hypothesis and unravel the underlying multi‐target mechanisms, our research strategy was initiated by establishing a high‐resolution RDP component database via ultra‐high performance liquid chromatography‐quadrupole time‐of‐flight mass spectrometry (UPLC‐Q‐TOF‐MS/MS). This chemical profiling was subsequently integrated with transcriptomic and network pharmacology analyses to map a holistic component‐disease interaction network and predict core therapeutic targets. Guided by these multi‐omics predictions, we then evaluated the mitigating effects of RDP in an ISO‐induced AMI mouse model to validate its macroscopic cardioprotective phenotypes and underlying molecular mechanisms. Furthermore, molecular docking and molecular dynamics simulations were deployed to deeply explore the dynamic interactions between key active constituents and their respective targets at the atomic level. By elucidating the active component basis and systemic mechanism of RDP in alleviating AMI, this study provides a crucial theoretical framework for its future application in functional foods and health‐promoting supplements.

2. Results and Discussions

2.1. Identification and Classification of RDP Compounds

The offline data was based on the comprehensive analysis of RDP extracts on the Ms‐dial metabolomics data analysis platform, and the secondary fragments were sorted and matched, and finally matched with match score as the match evaluation, and finally matched 99 compounds, as shown in the figure and the table. According to the classification of compounds, it contains 28 flavonoids, 24 terpenoids, 5 plant‐derived amino acids, 4 carotene, 4 phenolic acid derivatives, 3 lignans, 11 fatty acid esters, 3 oligosaccharides, and 17 other substances. The table of retention times corresponding to the compounds is shown in Table 1. The total ion chromatogram (TIC) diagram of positive and negative ions was shown in Figure S1A,B, and the compound classification diagram based on secondary fragmentation are shown in Figure S1C. The TIC profiles acquired in positive and negative ion modes are displayed in Figure 1A,B, respectively, while the compound annotations based on MS/MS matching are shown in Figure S1.

TABLE 1.

Compound information characterized in RDP.

No.	RT	m/z	Type	Name	Height	Area
1	0.76	104.10773	[M]+	Choline hydroxide	29 092.00	108 858.85
2	0.76	175.11838	[M + H]+	l(+)‐Arginine	6841.22	44 745.77
3	0.83	365.1032	[M + Na]+	Methylophiopogonanone A	10 524.78	45 474.75
4	0.86	543.13056	[M + K]+	Mellzitose	13 183.78	90 562.55
5	0.93	116.07065	[M + H]+	l‐Proline	7345.44	39 233.54
6	0.95	118.08607	[M + H]+	Betaine	6806.44	66 615.27
7	0.97	182.08041	[M + NH4]+	l‐Tyrosine	2950.78	14 363.34
8	0.99	365.10454	[M + Na]+	Trehalose	7942.56	51 601.72
9	1	360.1485	[M + NH4]+	Palatinose	10 165.33	100 785.88
10	1.07	130.08628	[M + H]+	d(+)‐Pipecolinic acid	4795.33	24 852.45
11	1.94	294.15295	[M + H]+	N‐Fructosyl isoleucine	13 922.78	194 167.07
12	6.39	243.0471	[M + Na]+	Canthin‐6‐one	4067.89	52 787.30
13	6.51	367.14938	[M + Na]+	Usnic acid	18 358.22	201 768.01
14	6.8	205.09612	[M + H]+	Dl‐Tryptophan	6554.67	87 785.10
15	7.04	185.04337	[M + H]+	3,4‐Dihydroxymandelic acid	20 834.00	227 458.90
16	7.04	453.13714	[M + H]+	Catechin‐3‐O‐Β‐d‐glucoside	7856.33	127 686.54
17	7.08	903.25293	[M + H]+	Ternatumoside II	14 548.22	129072.28
18	7.2	579.14822	[M + H]+	Procyanidin B1	3473.56	40 330.92
19	7.28	453.13796	[M + H]+	Catechin‐3‐O‐glucoside	33 711.22	337 810.63
20	7.28	291.08543	[M + H]+	Catechin	8585.11	84 750.16
21	7.28	475.11948	[M + H]+	Tectorigenin‐7‐O‐xylosylglucoside	1610.78	16 352.84
22	7.92	139.03847	[M + H]+	Salicylic acid	2849.11	29 312.50
23	7.93	291.08545	[M + H]+	Catechin‐E‐O‐glucoside	19 524.00	224 483.13
24	8.13	344.13261	[M + NH4]+	Caffeic acid 4‐O‐glucoside	1905.89	21 852.66
25	9.08	389.21534	[M + H]+	Icariside B5	5870.78	53 754.50
26	9.14	291.08536	[M + H]+	(+)‐Epicatechin	2059.56	21 743.97
27	9.5	305.06461	[M + H]+	Taxifolin	2138.44	23 740.14
28	9.54	153.05426	[M + H]+	Vanillin	1779.22	24 545.32
29	9.97	193.08512	[M + CH3OH + H]+	Myristicin	1364.00	16 180.99
30	10.13	434.20104	[M + NH4]+	Sayaendoside	1249.11	14 021.22
31	10.39	617.11241	[M + H]+	Quercetin 3‐(2‐galloylglucoside)	5389.33	47 411.35
32	10.52	289.06976	[M + H]+	Dihydrokaempferol	4533.44	53 068.20
33	10.52	451.12221	[M + H]+	Maesopsin 4‐O‐Beta‐d‐glucoside	1497.00	19 246.93
34	10.52	473.1041	[M + Na]+	(−)‐Egcg‐3′′‐O‐Me	1549.44	18 317.75
35	10.72	303.01294	[M + H]+	Ellagic acid	6404.56	77 326.41
36	10.82	611.1585	[M + H]+	Rutin	4846.33	54 071.33
37	11.03	566.42603	[M]+	Eschscholtzxanthin	50 835.78	493 173.26
38	11.09	305.06472	[M + H]+	(−)‐Taxifolin	1489.22	11 129.88
39	11.12	479.08	[M + H]+	Miquelianin	6074.00	62 439.33
40	11.15	465.10071	[M + H]+	Hyperoside	8134.22	80 663.03
41	11.21	451.12195	[M + H]+	Eriodictyol‐7‐O‐glucoside	1149.67	18 763.46
42	11.8	679.50985	[M + H]+	Thermoactinoamide_J	55 043.22	521 916.51
43	11.8	701.49178	[M + Na]+	Cimiracemoside D	19 368.22	172 483.72
44	11.91	435.09008	[M + H]+	Guaijaverin	1290.22	14 780.37
45	12	598.24791	[M + NH4]+	Acanthoside B	1781.56	20 561.17
46	12.01	449.10647	[M + H]+	Kaempferol‐3‐O‐galactoside	4634.33	41 603.34
47	12.01	287.05422	[M + H]+	Kaempferol	1745.89	15 302.87
48	12.44	435.12747	[M + H]+	Naringenin‐7‐O‐glucuronide	1696.00	18 811.16
49	12.8	905.67849	[M + Na]+	SCH 60057‐31‐O‐glucoside	3858.78	32 521.95
50	13.22	275.09034	[M + H]+	Phloretin	2323.78	24 773.27
51	13.24	309.0963	[M − H2O + H]+	1‐[3‐(3,4‐Dihydroxyphenyl)‐2‐propenoate]	1922.22	20 437.61
52	13.62	331.15315	[M + H]+	Tetrahydrosappanone A trimethyl ether	1711.22	19 584.45
53	13.78	441.2453	[M + Na]+	Ophiopogonside A	2449.22	25 389.83
54	14.18	543.32824	[M + Na]+	Cucurbitacin IIB	1431.78	14 446.90
55	14.74	303.04962	[M + H]+	Quercetin	38 122.22	511 884.39
56	14.8	595.14369	[M + H]+	Tiliroside	88 149.56	889 911.65
57	14.8	596.14768	[M + H]+	Kaempferol‐3‐O‐(6″‐P‐coumaroyl)‐glucoside	28 159.44	292 403.66
58	16.6	668.43486	[M + NH4]+	1,11R‐Dihydroxy‐oleanolic acid Β‐d‐glucoester	25 798.67	229 769.04
59	18.91	373.12717	[M + H]+	Sinensetin	7754.11	88 231.98
60	20.05	527.33238	[M + Na]+	2alpha,19alpha,23‐Trihydroxyoleanolic acid	4478.56	52 064.04
61	20.36	505.35111	[M + H]+	1R,12‐Dihydroxy‐maslinic acid	11 992.11	157 889.92
62	20.37	527.33169	[M + Na]+	Arjungenin	4946.67	71 598.37
63	20.37	1031.67862	[2M + Na]+	5Β‐Cholane‐3Α,6Α,24‐triethyl ester	1141.11	7764.56
64	21.44	274.27313	[M + H]+	N‐Lauryldiethanolamine	28 949.44	405 830.23
65	22.24	471.34543	[M − H2O + H]+	1,10‐Dihydroxy‐oleanic acid	31 218.78	307 218.46
66	22.24	489.35601	[M + H]+	(3Β,6Α,19Α)‐3,6,19‐Trihydroxy‐12‐ursen‐28‐oic acid	14 235.56	117 452.81
67	22.24	511.33766	[M + Na]+	Asiatic acid	11 511.22	76 076.13
68	22.71	445.21062	[M + H]+	Aurantiamide acetate	2656.78	34 676.37
69	23.2	503.33534	[M + H]+	Ilexgenina	10 720.22	164 348.46
70	23.36	415.21005	[M − H2O + H]+	Niranthin	5344.33	59 967.68
71	24.27	359.11143	[M + H]+	Penduletin 4′‐methyl ether	11 362.89	146 107.25
72	25.05	518.32212	[M + H]+	(2‐Hydroxy‐3‐octadeca‐6,9,12‐trienoyloxypropyl) 2‐(trimethylazaniumyl)ethyl phosphate	58 193.11	373 529.61
73	25.3	487.339	[M + H]+	Emmolic acid	65 771.33	1 432 043.55
74	25.3	509.32088	[M + Na]+	Quillaic acid	11 311.56	197 009.37
75	25.75	471.34504	[M + H]+	Glycyrrhetinic acid	1898.33	13 784.66
76	26.7	473.35972	[M − H2O + H]+	Compound NP‐019715	11 607.67	56 412.70
77	26.71	495.34218	[M + Na]+	Anhydroalisol A	6607.44	45 642.16
78	26.71	437.33912	[M − H2O + H]+	3‐Hydroxy‐11‐ursen‐28,13‐olide	32 638.89	319 528.62
79	26.87	532.34586	[M + NH4]+	Panaxcerol B	3172.78	42 752.52
80	27.72	473.36047	[M + H]+	Corosolic acid	5098.89	66 597.00
81	28.72	471.34542	[M + H]+	7‐Hydroxy‐betulinic acid	8756.89	88 773.16
82	28.72	453.33485	[M + H]+	Dehydromangiferonic acid	5922.78	63 304.23
83	28.79	522.35311	[M + H]+	1‐Oleoyl‐SN‐glycero‐3‐phosphocholine	11 002.78	111 557.01
84	28.99	205.08501	[M + H]+	3‐Prenyl‐4‐Hydroxyacetophenone	30 976.67	377 343.14
85	31.09	439.35561	[M − H2O + H]+	Oleanolic acid	122 529.22	1 354 668.63
86	31.31	425.37668	[M − H2O + H]+	Cycloart‐25‐ene‐3,24‐diol	11 200.33	108 366.46
87	31.63	457.36603	[M + H]+	Ursolic acid	19 467.89	201 472.57
88	32.46	282.27804	[M + H]+	Laurocapram	6814.89	111 230.23
89	32.61	443.38679	[M + H]+	Betulin	5777.33	62 020.30
90	32.67	281.24665	[M + H]+	Linoleic acid	8652.56	63 116.51
91	32.91	663.94621	[M + NH4]+	Haploperoside E	19 879.44	142 616.77
92	32.97	564.40076	[M]+	Torularhodin	4339.67	32 204.12
93	33.16	593.27335	[M + H]+	Pheophorbide A	19 490.33	129 829.82
94	33.51	427.39147	[M + H]+	Lupeol	2493.89	23 664.61
95	33.51	785.49932	[M + Na]+	Ginsenoside Rg2	1643.11	14 043.72
96	34.51	568.42519	[M]+	Lutein	5733.67	49 129.03
97	34.51	585.42662	[M + H]+	Antheraxanthin	2130.89	17 335.69
98	34.8	628.19221	[M + NH4]+	Kaempferol 3′4′‐di‐O‐glucoside	17 830.56	295 903.47
99	34.84	359.31408	[M + H]+	(S)‐1‐Monostearin	1877.33	16 055.56

FIGURE 1.

UHPLC‐Q‐TOF‐MS/MS UHPLC‐Q‐TOF‐MS/MS total ion chromatogram. (A) TIC diagram of the positive mode, (B) TIC diagram of the negative mode.

2.2. Network Pharmacology and Transcriptomics Assay Analysis Results

A comprehensive strategy integrating transcriptomic profiling and network pharmacology was deployed to decode the molecular targets and therapeutic pathways of RDP. Based on the screening criteria of log₂FC ≥ 1 and p < 0.05, a volcano plot was generated to illustrate the differential gene expression between the control and model groups of BALB/c mice (Figure 2A). This transcriptomic analysis identified 453 overexpressed genes and 832 underexpressed genes in the model group. Utilizing the differentially expressed upregulated genes, Gene Set Enrichment Analysis (GSEA) was performed (Figure 2B), revealing that the model group exhibited significantly higher expression levels in the TNF, NF‐κB, and IL‐17 signaling pathways compared to the blank group, with the TNF and NF‐κB pathways demonstrating the highest enrichment levels. Subsequent Gene Ontology Enrichment Analysis (GO) and KEGG Pathway Enrichment Analysis (KEGG) analyses at the transcriptional level (Figure 2C,D) categorized these genes into biological processes (BP) and molecular functions (MF), highlighting pathways such as PI3K‐AKT, cAMP, and TNF signaling, as well as focal adhesion.

FIGURE 2.

Transcriptomics and network pharmacology diagrams: (A) Differential gene volcano diagram, (B) GSEA Pathway Analysis Chart of differentially expressed genes, (C) GO enrichment analysis diagram of differential genes, (D) KEGG enrichment analysis diagram of differential genes, (E) a ternary Venn diagram of differentially expressed genes, disease gene targets, and RDP compound targets. (F) Core target network diagram of 91 intersecting genes. (G) Chord diagram of KEGG enrichment analysis for core targets. (H) Go analysis diagram of core targets.

In parallel, RDP's compound targets against AMI were systematically predicted. A three‐way Venn diagram was established to illustrate the intersection among the transcriptome analysis (TSA) genes, acute myocardial infarction disease targets (AMI‐DT), and compound targets (CPT) (Figure 2E). This intersection yielded a total of 91 common targets. Based on these 91 intersecting targets, a protein–protein interaction (PPI) network was constructed, facilitating the identification of 20 core targets (Figure 2F). Finally, these 20 core targets were utilized to generate a specific KEGG string diagram (Figure 3G) and a GO enrichment analysis diagram (Figure 2H).

FIGURE 3.

Pharmacological results. (A) ECG graph of the ISO‐induced mouse AMI model. (B) CMI measurement diagram (n = 8). (C) Myocardial TTC staining diagram (n = 3). (D) Quantification of myocardial ischemia area. (E) HE staining of myocardial tissue (n = 3). ^### p < 0.01 versus the Control; *p < 0.05, **p < 0.01, ***p < 0.001 versus the Model.

The integrated omics approach provides profound insights into the underlying mechanisms of AMI progression and the multi‐target intervention of RDP. Our transcriptomic profiling revealed that the differentially expressed genes are predominantly associated with intense inflammatory responses. Specifically, upregulated genes such as Acot1 and Chi3l1 exhibit a strong correlation with massive inflammation and tissue remodeling. Furthermore, genes like Irf4 and Slc8b1 have been demonstrated to play critical roles in regulating cardiac muscle cell membrane potential and lymphocyte chemotaxis [23, 24]. Crucially, Tnfsf10, Tnfsf1b, and Sbk2 are directly implicated in the production and release of the master pro‐inflammatory cytokine TNF‐α [25].

These gene‐level observations are strongly corroborated by our GSEA and KEGG pathway results. The marked hyperactivation of the TNF, NF‐κB, and IL‐17 pathways, alongside PI3K‐AKT and cAMP cascades, underscores their critical role in driving the inflammatory response and cellular adhesion processes following myocardial injury. By mapping these transcriptomic alterations to the 20 core intersection targets identified via network pharmacology, we systematically confirm that the disease progression of AMI intricately weaves together inflammatory and oxidative stress pathways. Based on these comprehensive omics findings, we predict that the severe inflammatory damage observed in cardiomyocytes is tightly coupled with, and fundamentally facilitated by, an initial outburst of cellular oxidative stress. RDP appears to exert its therapeutic potential by systematically targeting this highly interconnected oxidative‐inflammatory axis.

2.3. The Mitigating Effect of RDP on AMI

Intending to validate the protective effects of RDP against ISO‐induced AMI, a series of physiological and histological assessments were conducted. ShengMai Yin (SMY) is a traditional Chinese medicine composed of red ginseng, ophiopogon, and schisandra. Previous studies have demonstrated its significant effect in alleviating acute myocardial ischemia and reperfusion injury [26]. ECG recordings confirmed successful model induction via significant ST‐segment elevation (Figure 3A). Quantitative analysis showed that the cardiac mass index (CMI) in the model group was significantly higher than that in the control group, whereas RDP treatment resulted in a dose‐dependent reduction in CMI (Figure 3B). 2,3,5‐Triphenyltetrazolium chloride (TTC) staining and subsequent quantification of the ischemic area demonstrated that both RDP and the positive SMY significantly minimized the area of ischemia, with RDP‐H achieving efficacy nearly equivalent to SMY (Figure 3C,D). Furthermore, HE staining (Figure 3E) revealed significant myocardial cell degeneration, fiber disorder, and massive inflammatory cell infiltration in the model group (red arrows). Notably, numerous vacuoles were observed between myocardial fibers, serving as markers of myocardial edema (blue arrows). RDP treatment progressively improved these pathological features, the RDP‐H group exhibited neatly arranged myocardial fibers and minimal inflammatory infiltration, showing no significant differences compared to the SMY group.

The pronounced ST‐segment elevation and marked increase in CMI observed in this study serve as classic hallmarks of acute ischemic injury and secondary myocardial edema [27]. The elevated CMI primarily reflects increased capillary permeability and interstitial exudation resulting from severe cardiomyocyte damage [28]. Our findings indicate that RDP‐H group effectively alleviates myocardial edema, exhibiting a therapeutic efficacy comparable to, or even surpassing, that of SMY. These results suggest that RDP exerts profound cardioprotective effects not merely by improving macroscopic anatomical lesions, but fundamentally by stabilizing cell membrane structures and mitigating acute ischemic exudation. By preserving membrane integrity, RDP successfully intercepts the pathological cascade of myocardial edema and local inflammatory infiltration during the progression of AMI.

2.4. Determination of the Antioxidant Capacity of RDP Extracts

To initially investigate the antioxidant capacity of RDP, both in vitro chemical assays and an ex vivo tissue model were employed. DPPH, ABTS, and hydroxyl radical scavenging assays were selected as indicators of direct chemical antioxidant activity. The results demonstrated that RDP extracts exhibit substantial in vitro free radical scavenging capacity (Figure 4A). Furthermore, to evaluate the protective effect against lipid peroxidation, catalase (CAT) content, and total antioxidant capacity (T‐AOC) were measured in a CCl₄‐induced isolated liver tissue model. Our findings revealed that RDP extracts dose‐dependently attenuated CCl₄‐induced lipid peroxidation and effectively restored CAT and T‐AOC levels in the isolated liver tissues (Figure 4B–C).

FIGURE 4.

Determination of RDP antioxidant capacity. (A) Determination of the free radical scavenging ability of RDP extract. (B) Determination of CAT content in a liver lipid peroxidation model using RDP extracts of different concentrations. (C) Determination of T‐AOC in a liver lipid peroxidation model using RDP extracts of different concentrations. Data are expressed as means ± SD (n = 3). ###p < 0.001 versus the Control; ***p < 0.001 vs. the Model.

As indicated by the GO analysis of the intersection targets, the therapeutic mechanisms of RDP are strongly correlated with the modulation of cellular oxidative stress. Our biochemical evaluations align with this prediction, revealing that RDP exerts robust antioxidant effects. To comprehensively assess its direct radical‐scavenging profile, three distinct chemical assays were utilized. Specifically, DPPH is a stable lipophilic radical primarily used to evaluate the hydrogen‐donating ability of antioxidants; ABTS is applicable in both aqueous and lipophilic systems, reflecting the electron‐transfer capacity of the extracts; and ^•OH is one of the most reactive and destructive ROS in biological systems, capable of inflicting severe damage on DNA, proteins, and cell membranes [29]. The profound ability of RDP to quench all three distinct radicals underscores its broad‐spectrum direct antioxidant potential. Furthermore, beyond direct scavenging, the restoration of CAT and T‐AOC in the CCl₄‐induced liver model indicates that RDP can fundamentally mitigate severe lipid peroxidation by bolstering endogenous enzymatic defenses. Collectively, RDP demonstrates a dual antioxidant capacity, not only by directly neutralizing ROS at the chemical level but also by exerting potent biological antioxidant effects to protect tissues from oxidative damage.

2.5. Serum and Tissue Biochemical Indicator Testing

2.5.1. Oxidative Stress‐Related Indicators

Guided by the network pharmacology analysis (Figure 2H), which demonstrated a strong association between the onset of AMI and cellular oxidative stress, we evaluated the levels of key oxidative stress biomarkers in myocardial tissue. Compared to the model group, RDP treatment significantly restored the levels and activity of the antioxidant enzyme CAT (Figure 5A,B). Concurrently, RDP effectively alleviated the depletion of superoxide dismutase (SOD) induced by myocardial ischemia, with the RDP‐M group exhibiting a recovery capacity nearly equivalent to that of the positive control SMY. Furthermore, RDP treatment significantly reduced the accumulation of malondialdehyde (MDA), a primary marker of lipid peroxidation (Figure 5C). Notably, the MDA levels in the RDP‐H were markedly lower than those in the SMY group, demonstrating RDP's potent efficacy against lipid peroxidation in vivo.

FIGURE 5.

Determination of mouse myocardial enzyme spectrum and serum ELISA determination. (A) Measurement of CAT content in myocardial tissue. (B) Measurement of SOD content in myocardial tissue. (C) Measurement of MDA content in myocardial tissue. (D) Measurement of cTNT levels in serum. (E) Measurement of LDH levels in serum. (F) Measurement of AST levels in serum. (G) Measurement of TNFα concentration in serum. (H) Measurement of IL‐6 concentration in serum. (I) Measurement of IL‐2 concentration in serum. (J) Measurement of IL‐17 concentration in serum. Data are expressed as means ± SD (n = 3). ##p < 0.01 versus the Control; *p < 0.05, **p < 0.01, ***p < 0.001 vs. the Model.

Oxidative stress serves as a primary early trigger in the pathogenesis of AMI. The metabolism of ISO generates excessive ROS, which peroxidize polyunsaturated fatty acids within the cardiomyocyte membrane, ultimately resulting in severe lipid peroxidation and subsequent MDA accumulation [30]. Our biochemical analyses confirm that RDP has the capacity to effect a substantial improvement in the oxidative microenvironment following myocardial ischemia. By significantly attenuating MDA levels while concurrently upregulating the activity of CAT and SOD, RDP effectively breaks the vicious cycle of oxidative damage. This robust antioxidant capacity is fundamentally attributed to the abundant bioactive flavonoids and terpenoids characterized in the RDP extract. Consequently, we postulate that RDP mitigates myocardial lipid peroxidation primarily by alleviating the burden on the endogenous antioxidant enzymatic defense system.

2.5.2. Cardiac Enzyme‐Related Indicators

In order to further evaluate the extent of myocardial injury, specific and non‐specific myocardial enzymes in the plasma were quantified. As shown in Figure 5D, the plasma levels of cardiac troponin T (cTNT) in the model group were significantly elevated compared to the control group. Following RDP intervention, cTNT levels were significantly reduced. Specifically, the reduction in the RDP‐M group was essentially equivalent to that observed in the SMY group, whereas the RDP‐H group exhibited a significantly more pronounced decrease. Furthermore, the levels of lactate dehydrogenase (LDH) and aspartate aminotransferase (AST) were significantly higher in the model group than in the control group (Figure 5E,F). Treatment with RDP significantly decreased the levels of both LDH and AST compared to the model group. Notably, the plasma AST levels in the RDP‐H group were markedly lower than those in the SMY group.

The assessment of circulating myocardial enzymes provides critical biochemical evidence for the severity of cardiac injury. cTNT is a highly specific marker for myocardial damage; upon cardiomyocyte necrosis or severe membrane damage, intracellular cTNT is rapidly released into the bloodstream, serving as a primary diagnostic indicator for acute myocardial ischemia [31]. The marked elevation of plasma cTNT in the model group indicates significant ISO‐induced myocardial injury, which is highly consistent with our macroscopic H&E histopathological findings. Although LDH and AST are non‐specific markers of myocardial injury, their combined evaluation is highly instrumental in systematically assessing the overall severity of tissue damage [32]. Our findings demonstrate that RDP‐H is even more effective than the positive control SMY at reversing these enzyme elevations. Based on the integrated evaluation of these specific and non‐specific markers, we can conclude that RDP effectively maintains the structural integrity of the cardiomyocyte membrane, thereby fundamentally preventing the pathological leakage of intracellular macromolecular enzymes into the circulation during ischemic stress.

2.5.3. Inflammation‐Related Indicators

To evaluate the systemic inflammatory response following AMI, the levels of key inflammatory cytokines in mouse serum were quantified. As shown in Figure 5G, plasma TNF‐α levels were substantially decreased in the RDP treatment groups compared to the model group, with the RDP‐H group attaining suppression levels comparable to those observed in the SMY group. Similarly, the levels of Interleukin‐6 (IL‐6) and Interleukin‐2 (IL‐2) were significantly elevated in the model group compared to the blank group (Figure 5H,I). RDP intervention consistently reduced both IL‐6 and IL‐2 levels, and the RDP‐H group achieved reductions comparable to the SMY group. Furthermore, serum IL‐17 levels were significantly elevated in the model group (Figure 5J). All RDP treatment doses effectively reduced plasma Interleukin‐17 (IL‐17) levels. Notably, while the reduction in the RDP‐M group was comparable to that of the SMY group, the RDP‐H group exhibited a significantly more pronounced decrease in IL‐17 levels than the positive control.

The profound reduction of systemic inflammatory cytokines observed in this study corroborates our histopathological findings and perfectly aligns with the predictions from our multi‐omics analysis. Guided by the GSEA of the network pharmacology (Figure 2B), we confirmed that AMI significantly upregulates the nuclear factor kappa‐B (NF‐κB) and IL‐17 signaling pathways. TNF‐α acts as a potent chemotactic and activating factor that drives the recruitment of mononuclear macrophages to the peri‐infarct zone [33]. By effectively mitigating TNF‐α release, RDP intercepts the primary inflammatory signal at its source. Furthermore, IL‐6 and IL‐2 are key downstream effectors of the NF‐κB signaling pathway; their marked reduction suggests that RDP successfully dampens this critical pro‐inflammatory axis. Additionally, the IL‐17 signaling pathway plays a focused role in amplifying sustained inflammatory responses and promoting neutrophil infiltration [34]. The profound suppression of IL‐17 by RDP‐H treatment even outperformed the SMY, highlights its robust anti‐inflammatory capacity.

By integrating transcriptomic predictions, phenotypic observations, and these serological confirmations, we conclude that RDP exerts its cardioprotective effects fundamentally by suppressing the excessive release of critical inflammatory factors, particularly via blunting the TNF‐α and IL‐17 cascades.

2.6. Western Blot Analysis

Subsequent to the serological analysis, western blot experiments were conducted to evaluate the protein expression of key inflammatory factors in mouse myocardial tissue. As shown in Figure 6A–D, the protein expression of the primary pro‐inflammatory factor TNF‐α, along with its downstream targets IL‐6 and IL‐2, was markedly elevated in the model group compared to the control group. RDP treatment effectively and significantly downregulated the expression of TNF‐α, IL‐6, and IL‐2 in the myocardial tissue. Furthermore, we assessed the expression of NF‐κB (Figure 6E). The results indicated that the total NF‐κB protein levels were elevated in the model group compared to the blank group. However, no statistically significant differences in total NF‐κB expression were observed between the RDP treatment groups and the model group.

FIGURE 6.

Expression of relevant inflammatory factor indicators in each experimental group. (A) Western bolting diagram. (B) Analysis of TNFα expression. (C) Analysis of IL‐6 expression. (D) Analysis of IL‐2 expression. (E) Analysis of NFkB expression (n = 3) ###p < 0.001 versus the Model; *p < 0.05, **p < 0.01, ***p < 0.001, n.s., no significance, vs. the Model.

The protein expression profiles in myocardial tissue strongly corroborate our multi‐omics predictions, specifically the GSEA findings which indicated a marked upregulation of the TNF‐α and NF‐κB signaling pathways following ISO induction. As a primary initiating signal in the inflammatory cascade, TNF‐α binds to its cognate receptors on the myocardial cell membrane. Following receptor engagement, subsequent intracellular signal transduction triggers the dissociation and rapid nuclear translocation of NF‐κB. Upon entering the nucleus, NF‐κB binds to specific promoter sequences, directly driving a robust surge of downstream secondary pro‐inflammatory cytokines, including IL‐6, IL‐2, and IL‐17 [35].

Notably, our experimental data revealed that while RDP profoundly suppressed the expression of TNF‐α and its downstream effectors, it did not significantly alter the total protein expression levels of NF‐κB. This critical finding suggests that RDP exerts its anti‐inflammatory effects not by depleting the intracellular NF‐κB, but rather by strongly downregulating the upstream initiating signal. By effectively suppressing TNF‐α expression, RDP intercepts the pathological cascade at the receptor level, thereby preventing the activation and subsequent nuclear translocation of NF‐κB. This targeted blockade ultimately mitigates the downstream outburst of myocardial inflammation, highlighting RDP's precise molecular intervention in AMI progression.

2.7. Molecular Docking and Molecular Dynamics Validation

2.7.1. Molecular Docking Results

With a view to further exploring the molecular interactions between RDP components and core targets, molecular docking simulations were performed. The distribution trend of the docking scores is visually represented in a heatmap (Figure 7A), which revealed that multiple RDP compounds exhibit strong binding affinities with the TNF‐α protein. Subsequent targeted molecular docking studies evaluated the binding capacities of RDP components for the TNF‐α receptors, TNFR1 and TNFR2. As demonstrated in Figure 7B, the docking binding energies of RDP components for TNFR1 were notably lower than those for TNFR2. Among the docked compounds, rutin exhibited the lowest binding energy with TNFR1. Detailed post‐docking interaction analysis (Figure 7C) showed that the rutin molecule fit almost entirely within the binding pocket of the TNFR1 receptor. Specifically, the TRP67 residue engaged in π–π stacking with the flavonoid core of the rutin molecule, stabilizing its rigid framework. Additionally, the positively charged ARG187 residue formed π–cation interactions with both the B ring of the flavonoid skeleton and the n‐electrons of the pyran ring's oxygen. Furthermore, a network of amino acid residues, including ASP150, GLN153, HIE124, GLU66, and ASN65, strongly stabilized the glycosyl moiety of the rutin molecule.

FIGURE 7.

Molecular docking diagram. (A) RDP component and core target docking heat map. (B) Heat map of docking scores between RDP compounds and TNFR1 and TNFR2. (C) Docking interaction diagram of Rutin and TNFR1 molecule

Our molecular docking simulations provide crucial molecular‐level insights into the precise anti‐inflammatory mechanisms of RDP. TNF‐α is a master pro‐inflammatory cytokine involved in systemic inflammation, exerting its biological functions primarily through two distinct receptors: TNFR1 and TNFR2. TNFR1 signaling is predominantly responsible for macrophage activation and the massive release of downstream inflammatory factors, whereas TNFR2 activation generally mediates tissue‐protective and immunomodulatory pathways [36, 37]. The markedly stronger binding affinity of RDP components for TNFR1 compared to TNFR2 is highly significant. This selective affinity indicates that RDP can not only directly interact with the free TNF‐α ligand but also competitively occupy the TNFR1 pocket. The highly stable conformation of rutin within the TNFR1 pocket, which is structurally reinforced by extensive π–π stacking, π–cation interactions, and specific residue stabilization. By achieving a dual‐blockade against both the ligand and the pro‐inflammatory receptor, RDP components can precisely influence and suppress inflammatory signal transduction at the membrane level without antagonizing the protective TNFR2 pathway

2.7.2. Molecular Dynamic Analysis

For the purpose of validating the stability of the molecular docking poses and investigate the dynamic binding processes, 50 ns molecular dynamics (MD) simulations were performed under physiological conditions. Based on the initial docking heatmap, TNF‐α‐hyperoside, TNFR1‐rutin and TNFR2‐kaempferol complex were selected for MD simulations, respectively.

For the TNF‐α and hyperoside complex, the root‐mean‐square deviation (RMSD) plot (Figure 6B) indicated minor fluctuations between 0.5 Å and 1.5 Å over 50 ns, suggesting a highly stable binding state within the TNF‐α inhibitor pocket. Hydrogen bond analysis revealed sustained and robust interactions between the ligand and receptor throughout the simulation (Figures 7B and 8A). Furthermore, the solvent‐accessible surface area (SASA) remained minimal (Figure 8C), confirming that the hyperoside molecule was tightly encapsulated with minimal solvent exposure.

FIGURE 8.

Molecular dynamics simulation diagram. (A) RMSD diagram of TNFa and hyperoside complex. (B) Hydrogen bond interaction diagram of TNFa and hyperoside complex. (C) Hyperoside in TNFa on kinetic parameters in the interface pocket. (D) RMSD diagram of TNFR1 and rutin complex. (E) Hydrogen bond interaction diagram of TNFR1 and rutin complex. (F) RMSD diagram of TNFR2 and kaempferol complex. (G) Hydrogen bond interaction diagram of TNFR2 and kaempferol complex.

Regarding the receptor interactions, the MD simulation of rutin with TNFR1 demonstrated a relatively stable RMSD fluctuation within the range of 4–8 Å over the 50 ns trajectory (Figure 8D). Hydrogen bond analysis (Figure 8E) showed that multiple amino acid residues of TNFR1 continuously interacted with rutin, frequently exceeding four hydrogen bonds at various time points. Conversely, the MD simulation for TNFR2‐kaempferol, exhibited significant RMSD fluctuations (Figure 8F) and a lack of stable hydrogen bond formation (Figure 8G), indicating notable repulsion and unstable binding over the 50 ns period.

Crucially, our molecular docking and 50 ns MD simulations provide a robust physicochemical foundation for the macroscopic cardioprotective phenomena observed in vivo. The computational data precisely elucidate how the core components of RDP intervene in the inflammatory cascade at the atomic level.

The trajectory analyses reveal that rutin can precisely and stably anchor within the binding pocket of TNFR1. This structural stabilization is heavily mediated by powerful π–π stacking interactions with the TRP67 residue and strong cation–π interactions with ARG187. We postulate that this highly stable binding induces a critical steric hindrance within the receptor pocket. This hindrance competitively precludes exogenous TNF‐α from recognizing and binding to TNFR1, thereby thoroughly intercepting the transduction of pro‐inflammatory signals into the intracellular space right at the receptor interface.

Simultaneously, hyperoside demonstrates a persistent, high‐affinity interaction with the intrinsic inhibitor pocket of the TNF‐α ligand itself, as evidenced by the sustained hydrogen bonding and minimal solvent exposure (SASA) during the MD simulation. Ultimately, this synergistic, multi‐component, and multi‐target blockade simultaneously neutralizing the circulating TNF‐α ligand and competitively occupying the TNFR1 receptor, provides a comprehensive mechanistic explanation for how RDP, as a functional therapeutic agent, successfully dismantles the complex inflammatory networks associated with AMI.

3. Conclusions

In conclusion, this study successfully employed a multi‐omics‐guided approach to elucidate the therapeutic mechanisms of RDP in ameliorating AMI. Initially, chemical profiling identified 99 bioactive compounds, establishing the material basis of RDP. Based on integrated transcriptomics and network pharmacology analyses, we predicted that the TNF‐α/NF‐κB signaling axis and oxidative stress networks are the primary therapeutic targets. Guided by these theoretical predictions, subsequent in vivo experimental validations definitively confirmed that RDP administration significantly ameliorates ISO‐induced AMI phenotypes, remarkably reducing the myocardial infarction area and alleviating myocardial edema. Mechanistically, our biochemical and molecular validations demonstrated that RDP robustly suppresses lipid peroxidation and restores endogenous antioxidant capacity, while profoundly intercepting the predicted TNF‐α/NF‐κB signaling cascade to downregulate the release of secondary pro‐inflammatory cytokines. Furthermore, molecular docking and 50‐ns molecular dynamics simulations provided atomic‐level evidence corroborating these findings, demonstrating that key active components achieve a dual‐blockade by stably anchoring to the TNF‐α and the TNFR1 receptor. Collectively, this study perfectly integrates transcriptomic predictions with rigorous experimental validations, firmly supporting the potential of RDP as a multi‐target functional food for cardiovascular protection.

4. Experimental Section

4.1. Characterization of RDP Components and Methods

4.1.1. RDP Plant Source

Air dried RDP samples were obtained from Da Hinggan Ling (Heilongjiang Province, China). The authentication of the plant material was performed by Prof. Zhen Yue Wang at Heilongjiang University of Traditional Chinese Medicine in Harbin, China. A voucher specimen [RDP‐201912‐05] has been deposited in the Herbarium of Heilongjiang University of Traditional Chinese Medicine. MeOH (HPLC grade) and isoprenaline were sourced from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China).

4.1.2. RDP Extraction Method

Ten grams of dried RDP fruits were weighed and pulverized using an IKA high‐speed grinder. The resulting powder was then transferred into a 250 mL round‐bottom flask. A rod‐shaped magnetic stir bar was added to provide continuous stirring, thereby ensuring uniform heat distribution and enhancing extraction efficiency. The extraction was performed using 100 mL of 70% aqueous ethanol at a constant temperature of 80°C. This procedure was repeated three times, with each cycle lasting 1.5 h. Subsequently, the extracts from the three cycles were pooled and concentrated using a rotary evaporator at 55°C, ultimately yielding a red, paste‐like substance with a distinct apricot‐peel‐like aroma.

4.1.3. UHPLC‐Q‐TOF‐MS/MS Chromatographic Conditions

UHPLC‐Q‐TOF/MS analysis was performed by using AB Sciex UHPLC‐Q‐TOF‐MS/MS 5600 mass spectrometer equipped with electrospray ionization (ABSCIEX, USA). A Waters Acquity UHPLC BEH C18 column (2.1 mm × 100 mm, 1.7 µm) was used at 40°C with a flow rate of 0.3 mL/min. The mobile phases were 0.1% formic acid–water (A) and 0.1% formic acid acetonitrile (B). The gradient elution conditions for serum were: 0–5 min: 2%–35% B; 5–8 min: 37%–67% B; 8–14 min: 67%–87% B; 14–40 min: 87%–100% B. Data were collected using Analyst TF 1.6 software (ABSCIEX, USA) [38].

4.1.4. Compound Identification and Matching

Mass spectrometry data were compared against the compound database using MsDial software (version 5.2.24.1113) [39]. Precise matching of compound secondary mass spectra using public databases.

4.2. Network Pharmacology Analysis Methods

4.2.1. Transcriptomics

Collected myocardial tissue from model group and control group mice for transcriptomic analysis. First, total RNA was extracted from the tissue using Trizol reagent (Invitrogen Life Technologies). Subsequently, mRNA was enriched using magnetic beads with Oligo (dT). Fragmented mRNA was then used as a template to synthesize double‐stranded cDNA. Finally, the library was sequenced using the Illumina MiSeq platform. Clean reads were obtained using HISAT2 software and aligned to the mouse reference genome (rn6) [40]. Gene expression levels were quantified using RSEM software [41]. Differential expression analysis was performed using DESeq2 software [42].

4.2.2. Network Pharmacology Methods

The screening of the absorbed components was according to the toxicological parameters and the pharmacological parameters. The putative targets of RDP were discerned from Swiss Target Prediction database (www.swisstargetprediction.ch/index.php) with “Probability” > 0. The disease targets of inflammatory and oxidation stress were derived from GeneCards (https://www.genecards.org/), DISGENET (http://www.disgenet.org/) Online Mendelian Inheritance in Man (OMIM: https://omim.org/) databases, and search terms included “Acute Myocardial Infarction.” Protein–protein interaction network was constructed using the STRING (https://string‐m.org) database. The network construction was performed using Cytoscape 3.6. The gene annotation and enrichment analysis platform WeiShengXin (https://www.bioinformatics.com.cn/) conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) Analysis.

4.3. Antioxidant Determination

4.3.1. Free Radical Scavenging Experiment

The DPPH and ABTS radical scavenging experiments of RDP extracts were conducted according to the methods described in [43], with slight modifications. The hydroxyl radical experiment was conducted using the Fe‐salicylic acid colorimetric method, according to the methods described in Ref. [44], with slight modifications. The radical scavenging capacity was calculated as follows:

$$\begin{array}{ccc}\hfill \mathrm{Scavenging}\mathrm{rate}\left(\%\right)& =& \left[1-\left(\mathrm{O}{\mathrm{D}}_{\mathrm{sample}}-\mathrm{O}{\mathrm{D}}_{\mathrm{blank}}\right)/\mathrm{O}{\mathrm{D}}_{\mathrm{control}}\right]\hfill \\ & & \times 100\%\hfill \end{array}$$

OD_sample represents the OD value which measured after sample solution mixed with DPPH solution; OD_blank represents the OD value which measured after sample solution mixed with anhydrous EtOH; OD_control represents the OD value which measured after DPPH solution mixed with anhydrous EtOH.

4.3.2. Determination of Anti‐lipid Peroxidation Capacity

A mouse model of acute liver injury was used, with the model group employing a carbon tetrachloride‐induced in vitro liver lipid peroxidation model. The method followed that described in Ref. [45] with minor modifications. All animals were anesthetized with sodium pentobarbital (30 mg/kg) via intraperitoneal injection and euthanized by cervical dislocation. Liver tissue samples were collected for CAT and T‐AOC content analysis, with CAT and T‐AOC serving as lipid peroxidation indicators to assess the lipid peroxidation‐inhibiting capacity of RDP.

4.4. Animal Feeding and Administration

All animal procedures were conducted in strict accordance with the standards of the Animal Ethics Committee of Heilongjiang University of Traditional Chinese Medicine (approval number 2019121101). Specific pathogen‐free (SPF) BALB/C mice (n = 48, weighing 20 g, with equal numbers of males and females) were procured from the Drug Safety Evaluation Center of Heilongjiang University of Traditional Chinese Medicine (SYXK (Hei) 2020–0001). The animals were housed in an SPF‐grade laboratory with controlled temperature (20°C–25°C) and humidity (40%–60%). Throughout the experiment, the animals had free access to food and water.

Following acclimatization, SPF‐grade BALB/C mice were randomly divided into equal numbers of male and female mice and assigned to the following groups: the control group, the model group, the RDP‐H group (1451 mg/kg/day, n = 8), the RDP‐M (725 mg/kg/day, n = 8) group, the RDP‐L (362 mg/kg/day, n = 8) group, and the SMY group (1820 mg/kg/day, n = 8). The RDP dosage was calculated on the basis of the daily intake of RDP in Shanmei capsules (79.7 mg/kg/day) and was subsequently adjusted in accordance with body surface area to determine the mouse dosage. Consequently, SMY was selected as the positive control drug and administered at a daily dosage of 200 mg/kg/d (n = 8). The ISO‐induced AMI mouse model was established in accordance with the method described by with ST‐segment elevation serving as the criterion for successful modeling (Figure S2). The control and model groups received an equivalent volume of physiological saline. From 17 days to 19 days, all groups except the control group were administered ISO (150 mg/kg/day) via intraperitoneal injection. This specific dosage was selected based on established experimental protocols to ensure the consistent and stable induction of myocardial ischemic injury [27]. The electrocardiograms were recorded, with ST‐segment elevation serving as the marker for successful modeling [46]. Forty eight hours after the final administration, sodium pentobarbital (30 mg/kg) was administered via intraperitoneal injection, and blood was collected from the eyeball 15 min later.

4.5. ECG and Cardiac Index Measurement

Mouse electrocardiogram (ECG) data were collected using the BL‐420F biological function experimental system to obtain data from the II lead. To evaluate myocardial edema and pathological changes, the Cardiac Mass Index (CMI) was determined. Briefly, the body weight of each mouse was accurately recorded prior to euthanasia. Following sacrifice, the heart was rapidly excised, rinsed in ice‐cold phosphate‐buffered saline (PBS) to remove residual blood, and carefully blotted with sterile filter paper to remove surface moisture [47]. The total cardiac mass was then measured using an analytical balance.

$$\mathrm{CMI}=\frac{\mathrm{Cardiac}\mathrm{mass}}{\mathrm{Body}\mathrm{weight}}\times 100\%$$

4.6. Antioxidant and Myocardial Enzyme Spectrum Determination

The DPPH, ABTS, and hydroxyl radical scavenging assays were performed according to the described method [15], with Vc used as the positive control. Antioxidant enzymes and cardiac enzymes were measured using commercially available kits to determine SOD(A001‐3‐2), MDA(A003‐1‐2), CAT(A007‐1‐1), T‐AOC(A015‐2‐1), LDH(A020‐2‐2), and AST(C010‐2‐1) levels (Nanjing Jiancheng Biotechnology). The assays were conducted using a 96‐well plate according to the kit instruction (http://www.njjcbio.com/products.asp).

Specifically, the levels of SOD, LDH, CAT, and AST were determined in mouse serum, whereas MDA levels were quantified in myocardial tissue homogenates. For the isolated liver tissues, CAT activity and T‐AOC were measured.

4.7. ELISA Detection of TNFα, IL‐6, IL‐2, and IL‐17 Levels in Serum

Quantification of TNF‐α, IL‐6, c‐TNT, IL‐1, and IL‐17 levels in rat plasma using enzyme‐linked immunosorbent assay (ELISA). TNF‐α, IL‐6, and cTNT were measured using commercially available ELISA kits (Wuhan Baidikon Biotechnology Co., Ltd., BQE‐M0111, BQE‐M0187, BQE‐M0165), IL‐2 (Shanghai Fenshi Biotechnology, FXs00247), and IL‐17 (Xiamen Luchangshuo Biotechnology, ED‐202290) levels. A 96‐well microplate was used, and absorbance was measured at 450 nm using an ELISA reader. All of the inflammatory markers were determined in mouse serum.

4.8. TTC and HE Staining

TTC staining solution 2% (Wuhan Sevier Biotechnology Co., Ltd.) was used to assess myocardial ischemia in mice. The isolated mouse hearts were washed with PBS at 4°C and rapidly frozen at −80°C. After 20 min, they were removed, fixed in a mold, and sectioned with a surgical knife. The sections were immersed in TTC staining solution and incubated at 37°C for 30 min. After PBS washing, they were fixed with 4% paraformaldehyde for 2 h.

For hematoxylin and eosin (H&E) staining, the rapidly excised mouse myocardial tissues were washed with ice‐cold PBS and fixed in 4% paraformaldehyde for 24 h. Following fixation, the tissues were dehydrated through a graded ethanol series (70%, 80%, 90%, 95%, and 100% for 1 h each), cleared in xylene for two changes (1 h each), and embedded in paraffin. The paraffin‐embedded tissue blocks were then sectioned into 4–5 µm thick slices using a rotary microtome. Prior to staining, the sections were deparaffinized in xylene (two changes, 10 min each) and rehydrated through descending concentrations of ethanol (100%, 95%, 80%, and 70%, 5 min each) to distilled water. The sections were then stained with hematoxylin solution (Wuhan Servicebio Technology Co., Ltd.) for 5 min, rinsed in running tap water, differentiated briefly in 1% acid alcohol for 10 s, and blued in ammonia water. Subsequently, the sections were counterstained with eosin solution for 2 min. Finally, the slides were dehydrated through ascending ethanol grades, cleared in xylene, and permanently mounted with a resinous mounting medium. The histopathological changes, including inflammatory cell infiltration and myocardial fiber arrangement, were observed and captured under a light microscope [48].

4.9. Western Blotting Experiment

10X TBST purchased from Servicebio (catalog number G0004) (Wuhan, China). Pre‐stained colored protein standards (10–180 kDa) (catalog number G2058) purchased from Servicebio. The antibodies used in this study include rat anti‐TNF‐α (catalog number GB310031), rat anti‐IL‐6 (catalog number GB300007), rabbit anti‐NF‐κB p65 (catalog number GB11997), rabbit anti‐β‐actin (catalog number GB15003‐100) was purchased from Servicebio (Wuhan, China). Anti‐rabbit (catalog number GB23303) horseradish peroxidase (HRP), and enhanced chemiluminescence (ECL) (catalog number G2014) were purchased from Servicebio (Wuhan, China).

4.10. Molecular Docking

Based on the results of the network pharmacology analysis, we finally selected TNF‐α (PDBID: 7KPA), NfκB (PDBID: 7KEU), BCL2 (PDBID: 7Y90), AKT1 (PDBID: 6HHG), and STAT3 (PDBID: 4ZIA) pre‐docking ligands. We performed molecular docking on all compounds obtained, used the Glide package to generate docking interfaces, and viewed the docking structures using PyMol. Since TNFR1 (PDBID: 2ZJC) and TNFR2 (PDBID: 9CU8) had no pre‐docking ligands, the Sitemap package was used to construct their docking pockets. All molecular docking data were analyzed using WeiShengXin (http://www.bioinformatics.com.cn) for heatmap analysis of the docking results.

4.11. Molecular Dynamics Simulation

Based on the molecular docking results, we used the Desmond module to perform molecular dynamics analysis. The system was prepared by performing energy minimization using the OPLS5 force field and solvated with the TIP3P solvent model in an orthorhombic box with dimensions of 10 Å for each axis. Neutralization was achieved by adding 5Na⁺ ions, and a POPC (300 K) membrane model was employed. The simulation was run using an NPT ensemble at 300 K for 50 ns, adhering to predefined advanced parameters.

4.12. Statistics and Analysis

The differences between RDP and Vc IC₅₀ were assessed using a Student's t‐test. One‐way analysis of variance (ANOVA) was used to examine differences between groups, followed by Tukey's test. p‐Values < 0.05 were considered statistically significant. All analyses were performed using GraphPad Prism (version 5.01, GraphPad Software, San Diego, USA).

Author Contributions

Ze‐Yu Yi: writing – review and editing, resources, methodology, investigation, formal analysis. Si‐Yu Liu: visualization, validation, software, resources, methodology, investigation. Lincheng Bai: methodology, conceptualization. Fan Yang: supervision, software, data curation. Zhenkun Wang: methodology, investigation. Xiubo Zhu: project administration, methodology. Zun‐Peng Shu: supervision, funding acquisition. Hua Han: supervision, funding acquisition. Xiao‐Jin Yu: project administration, investigation, funding acquisition.

Funding

This study was financially supported by the National Natural Science Foundation of China (82141211 to Zun‐Peng Shu) (LH2020H094 to Hua Han), the Natural Science Foundation of Heilongjiang Province (SJGZ20220108 to Xiao‐Jin Yu), the Graduate Innovation Program of Heilongjiang University of Traditional Chinese Medicine (2024yjscx117 to Ze‐Yu Yi), and the Heilongjiang Provincial Administration of Traditional Chinese Medicine (ZHY2024‐085 to Xiao‐Jin Yu).

Ethics Statement

All animal procedures were conducted in strict accordance with the standards of the Animal Ethics Committee of Heilongjiang University of Traditional Chinese Medicine (approval number: 2019121101).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1: cbdv71355‐sup‐0001‐FigureS1.docx.

Supporting File 2: cbdv71355‐sup‐0002‐FigureS2.docx.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.