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Abstract

Objectives: This study aimed to investigate the potential of Dendrobium officinale Kimura & Migo (DOKM) in ameliorating high cholesterol-induced atherosclerosis (AS) in zebrafish, focusing on the development of safe and effective drugs with low toxicity for AS treatment. Methods: A zebrafish AS model was established by feeding zebrafish a high cholesterol diet (HCD). Treatment was administered using 1 mg/L and 10 mg/L DOKM water extract. Fluorescence microscopy was utilised to observe the effects of DOKM on HCD-induced cholesterol accumulation, neutrophil accumulation, and macrophage infiltration in the vasculature. The impact of DOKM on HCD-induced disorders of lipid metabolism was assessed using Nile red staining. Total cholesterol (TC), triglycerides (TG), and low-density lipoprotein (LDL-C) levels in zebrafish were measured using kits. The expression of pcsk9, srebf2, and hmgcr genes in zebrafish was determined using RT-PCR. HE staining and Elvitegravir (EVG) staining were employed to assess the effect of DOKM on plaque formation in the blood vessels of AS zebrafish. Results: A total of 145 compounds were collected from Dendrobium officinale, along with 680 corresponding targets. Additionally, 1583 atherosclerosis-related targets were identified, with 202 representing interaction targets between Dendrobium officinale and atherosclerosis-related targets. Key compounds identified included Gigantol, Chrysotobibenzyl, Naringenin Chalcone, Hesperetin, and Tangeretin. The core targets of Dendrobium officinale for atherosclerosis treatment were STAT3, PTPN11, JAK2, epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor alpha (PDGFRA), proto-oncogene tyrosine-protein kinase Src (SRC), STAT1, TP53, ESR1, and AKT1. GO functional enrichment analysis revealed a total of 2090 GO biological functional entries. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed 200 AS signalling pathways associated with DOKM treatment for atherosclerosis. DOKM significantly attenuated cholesterol accumulation, neutrophil aggregation, and macrophage infiltration in the blood vessels of AS zebrafish, ultimately leading to reduced plaque formation. AS zebrafish exhibited lipid metabolism disorders, characterised by significant increases in TC, TG, and LDL-C levels, as well as mRNA expression of pcsk9, srebf2, and hmgcr genes. Treatment with DOKM significantly improved these lipid metabolism disorders. Conclusion: DOKM effectively attenuates HCD-induced AS, and this therapeutic effect on AS may be related to its role in improving lipid metabolism dysfunction.

Keywords

atherosclerosis zebrafish high cholesterol diet network pharmacology

Introduction

Atherosclerosis (AS) is a primary cause of cardiovascular morbidity and mortality worldwide. Accounting for approximately 20% of total global deaths, cardiovascular diseases pose a significant threat to public health.^1,2 Despite the global increase in living standards, the incidence of AS has rapidly increased in low- and middle-income countries. With extended lifespans, this trend is expected to increase. AS is the leading cause of mortality worldwide. According to data from 2019, cardiovascular diseases, notably coronary and cerebral AS, accounted for approximately 18 million deaths globally, representing over 30% of the total fatalities.³ In China, cardiovascular diseases are the primary cause of mortality in both urban and rural populations. As of 2020, the mortality rate attributed to cardiovascular diseases was 48.00% and 45.86% in rural and urban areas, respectively. The escalating prevalence of cardiovascular disease has substantially increased the economic burden on both residents and society, highlighting its significance as a major public health concern.⁴ Consequently, there is an urgent need to develop safe and effective strategies for the prevention and treatment of AS.

Current approaches for AS treatment predominantly involve surgery and drug therapies. Surgical interventions such as percutaneous transluminal coronary artery angioplasty (PTCA), percutaneous intracoronary stenting (PCI), coronary artery bypass grafting, and transcatheter atherosclerotic plaque exfoliation (TAE), are commonly employed.^5–7 Despite their ability to swiftly alleviate stenosis induced by AS and restore blood flow in obstructed vessels, these surgical procedures have notable limitations, including the occurrence of vessel restenosis postoperatively. Consequently, AS cannot be effectively treated with single surgical interventions.

Currently, several lipid-regulating and antithrombotic drugs are clinically used for AS treatment, including statins, probucol, fibrates, aspirin, clopidogrel, and certain anticoagulants.⁸ Among these, statins are particularly notable and currently the preferred choice for clinical AS management.⁹ Clinical research has demonstrated the effectiveness of statins in ameliorating high blood lipid and cholesterol levels in AS patients, thereby stabilising atherosclerotic plaques and preventing their progression. Nevertheless, lipid-lowering medications have limitations such as incomplete AS eradication and potential long-term toxicity.⁹ Consequently, monotherapy with lipid-lowering drugs alone may not result in complete remission of AS. Therefore, there is an urgent need to explore alternative medications with enhanced safety profiles, efficacy, and low toxicity for the treatment of AS.

Dendrobium officinale Kimura & Migo (DOKM), a traditional Chinese medicinal herb, has a long history and wide applications. In traditional Chinese medicine (TCM), DOKM is revered as one of the “Nine Immortal Herbs,” primarily used for nourishing yin, moisturising the lungs, generating body fluids, improving eyesight, and strengthening the waist.¹⁰ According to TCM theory, DOKM is slightly cold in nature and sweet in taste, entering the stomach and kidney meridians. It is commonly used to treat symptoms such as thirst caused by febrile disease, dry cough, dry throat, and hoarseness.¹¹ DOKM contains various bioactive components, including polysaccharides, biphenyl compounds, and flavonoids.¹² These components confer DOKM with multiple pharmacological properties. Recent studies have shown that DOKM exhibits significant antioxidant, anti-inflammatory, immunomodulatory, and anticancer activities.^13–15 Polysaccharides possess antioxidant activity, which can be used to protect against skin photoaging.^16,17 Additionally, polysaccharides from DOKM can prevent alcohol-induced mucosal damage via the AMPK/mTOR signalling pathway.¹⁸ Zhao et al¹⁹ discovered, through LC-MS and network pharmacology analysis, that the biphenyl compounds in DOKM have potential therapeutic value for lung cancer. Furthermore, Piao et al²⁰ found that the biphenyl compounds dendrocandin U, dendronbisline B, and (-)-syringaresinol exhibited anti-inflammatory effects by inhibiting nitric oxide secretion induced by lipopolysaccharide (LPS)/interferon-gamma (IFN-γ). Flavonoids have been proven to possess radical-scavenging and antioxidant capabilities.²¹

In this study, we primarily employed a high-cholesterol diet (HCD)-induced AS model in zebrafish to assess the anti-atherosclerotic properties of DOKM.

Results

To analyse potential targets of DOKM active ingredients for the treatment of AS, 145 active ingredients were screened and numbered. The correspondence between the numbers and chemical names is provided in Attachment Table 1. A total of 680 targets were identified using target prediction. After de-weighting the individual database genes, 1583 disease targets were identified (Figure 1A). Subsequently, 680 active ingredient action targets and 1583 disease targets were screened, and the predicted targets were mapped to the disease targets to draw a Venn Diagram (Figure 1B). This process yielded 202 intersecting genes that represent potential therapeutic targets of DOKM for AS.

Figure 1.

A. Venn diagram of each database target. B. Venn diagram of drug and AS disease targets.

Table 1.

Binding Energy of Compound to Core Target.

compound	Target	binding energy(kcal/mol)
Gigantol	JAK2	−7.8
Gigantol	PDGFRA	−9
Chrysotobibenzyl	JAK2	−6.2
Chrysotobibenzyl	ESR1	−8.2
Naringenin Chalcone	AKT1	−8.9
Naringenin Chalcone	EGFR	−7.6
Hesperetin	SRC	−5.9
Hesperetin	ESR1	−7.1
Tangeretin	SRC	−5.5
Tangeretin	EGFR	−7.9

Note: The table presents the binding energies of the five compounds with 10 core target molecules through molecular docking. The binding energy (measured in kcal/mol) indicates the strength of the interaction between the compound and target molecule, with lower values representing tighter binding. A binding energy of −5 kcal/mol was considered the threshold for a stable interaction.

Abbreviations: EGFR, epidermal growth factor receptor; JAK2, Janus Kinase-2; PDGFRA, PDGFRα, Platelet-Derived Growth Factor Receptor-α; SRC, Proto-oncogene tyrosine-protein kinase Src.

Network Diagram of “Active Ingredient-Target-Disease”

The “active ingredient-target-disease” network graph comprises 349 nodes and 1788 edges. Analysis by degree using the “analyze Network” function of Cytoscape 3.9.1 software revealed the top five compounds to be gigantol, chrysotobibenzyl, naringenin chalcone, hesperetin, and tangeretin, respectively. The network diagram of “active ingredient-target-disease” is depicted in Figure 2.

Figure 2.
Network diagram of “active ingredient-target-disease”.

Protein-Protein Interaction (PPI) Network and Core Targets

The 202 overlapping targets obtained from the Venn analysis were uploaded to the STRING database, which was utilised to construct a PPI network with non-overlapping targets hidden. A PPI network model comprising 154 target protein nodes was generated. The resulting Tabular Separated Values (TSV) format file was saved and imported into Cytoscape 3.9.1 software for visual analysis (Figure 3A). Subsequently, the maximal clique centrality (MCC) algorithm was employed for the calculation, and the top 10 targets were identified as core targets (Figure 3B). The colours of the core targets, arranged in descending order, transitioned from dark to light shades. These core targets included STAT3, PTPN11, JAK2, epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor alpha (PDGFRA), proto-oncogene tyrosine-protein kinase Src (SRC), STAT1, TP53, ESR1, and AKT1, indicating a significant association with DOKM in AS treatment.

Figure 3.
A. Network diagram of PPI visualisation of AS treatment with DOKM. B. MCC algorithm screens top 10 targets.

GO Function and KEGG Pathway Enrichment Analysis

Using the Metascape database, potential therapeutic targets for AS in DOKM were subjected to GO function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and the results were visualised. GO functional enrichment analysis yielded 2090 GO biofunctional entries. Among these, the biological process (BP)-related entries were the most numerous, comprising 1822. These entries were primarily involved in responses to hormones, cellular responses to nitrogen compounds, cellular responses to organonitrogen compounds, peptides, cellular responses to hormone stimuli, cellular responses to peptide stimuli, peptide hormones, cellular responses to peptide hormone stimuli, insulin, and cellular responses to insulin stimuli. Entries related to cellular components (CC) totalled 85, encompassing membrane rafts, membrane microdomains, caveolae, plasma membrane rafts, glutamatergic synapses, postsynapses, late endosomes, receptor complexes, external sides of plasma membranes, and sides of membranes. There were 183 entries associated with molecular functions (MF), including nuclear receptor activity, ligand-activated transcription factor activity, transcription factor binding, DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, transcription coregulator binding, transcription coactivator binding, nuclear receptor binding, DNA-binding transcription activator activity, and RNA polymerase II-specific DNA-binding transcription activator activity. Based on the P-values, the top 10 enriched genes of Gene Ontology Biological Process (GOBP), Gene Ontology Cellular Component (GOCC), and Gene Ontology Molecular Function (GOMF) were selected to create bar graphs, and the results are depicted in Figure 4.

Figure 4.
Go function enrichment analysis graph. DOKM, Dendrobium officinale Kimura & Migo; MCC, maximal clique centrality; PPI, Protein-protein interaction.

KEGG pathway enrichment analysis revealed 200 AS signalling pathways associated with AS treatment in DOKM. These pathways primarily included: Lipid and AS, PI3K-Akt signalling pathway, AGE-RAGE signalling pathway in diabetic complications, mitogen-activated protein kinase (MAPK) signalling pathway, EGFR tyrosine kinase inhibitor resistance, endocrine resistance, efferocytosis, hepatitis B, thyroid hormone signalling pathway, and insulin resistance. The KEGG pathway data were filtered based on high P-value and gene number criteria, excluding extensive signalling pathways. The top 20 pathways were selected to generate bubble maps, as shown in Figure 5.

Figure 5.
KEGG pathway enrichment analysis.

Molecular Docking Results

The five compounds with the highest degree, namely gigantol, chrysobibenzyl, naringenin chalcone, hesperetin, and tangeretin, were identified as the top 10 PPI analysis targets using the MCC algorithm. Molecular docking validation was performed and the binding energies of the main active ingredients to the key core targets were obtained, as shown in Table 1. Lower binding energy values indicate more stable conformations, with binding energies of all active ingredients to key core targets below −5.0 kcal/mol, suggesting good binding affinity and stable conformations.²² Figure 6 visually presents the molecular docking of compounds with core targets.

Figure 6.
Molecular docking. Note: The black box contains the 3D molecular docking image, whereas the red box contains the 2D molecular docking image. The images include amino acid residues of the interacting protein molecules.

DOKM Mitigates Cholesterol Accumulation in the Blood Vessels of AS Zebrafish

As shown in Figure 7, there was no significant cholesterol accumulation in the blood vessels of the zebrafish in the control group. Compared to the control group, a large amount of cholesterol accumulation was observed in the AS model group. Compared to the AS model group, both 1 mg/L and 10 mg/L DOKM treatments were effective in reducing cholesterol accumulation in blood vessels.

Figure 7.
DOKM alleviates cholesterol accumulation in AS zebrafish vessels (n = 6). Note: Orange fluorescent label for cholesterol. DOKM, Dendrobium officinale Kimura & Migo.

DOKM Attenuates Macrophage Infiltration in the Blood Vessels of AS Zebrafish

As shown in Figure 8, there was no obvious macrophage infiltration into the blood vessels of the zebrafish in the control group. Compared to the control group, a large number of macrophages appeared in the blood vessels of zebrafish in the AS model group. Compared to the AS model group, both 1 mg/L and 10 mg/L DOKM treatments were effective in reducing the number of macrophages in the blood vessels.

Figure 8.
DOKM alleviates macrophage infiltration in AS zebrafish vessels (n = 6). Note: Red fluorescent labelling for macrophages. DOKM, Dendrobium officinale Kimura & Migo.

DOKM Attenuates Neutrophil Accumulation in the Blood Vessels of AS Zebrafish

The results are shown in Figure 9, which indicates that there was no obvious accumulation of neutrophils in the blood vessels of zebrafish in the control group. Compared to the control group, a large number of neutrophils appeared in the blood vessels of zebrafish in the AS model group. Both 1 and 10 mg/L DOKM treatments were effective in reducing the number of neutrophils in blood vessels when compared to the AS model group.

Figure 9.
DOKM alleviates neutrophil accumulation in AS zebrafish vessels (n = 6). Note: Green fluorescent labelling for neutrophils. DOKM, Dendrobium officinale Kimura & Migo.

DOKM Attenuates Plaque Formation in the Blood Vessels of AS Zebrafish

Plaque formation in the blood vessels of zebrafish in each group was detected by haematoxylin and eosin (HE) staining and Verhoeff's Van Gieson (EVG) staining. The results are shown in Figure 10. Plaques were not formed in the blood vessels of zebrafish in the control group and no blockage occurred in the blood vessels. Compared to the control group, plaques were formed in the blood vessels of zebrafish in the AS model group, and severe vascular blockage occurred. Compared to the AS model group, both 1 mg/L and 10 mg/L DOKM treatments were effective in reducing plaque formation in blood vessels.

Figure 10.
DOKM alleviates plaque formation in AS zebrafish vessels (n = 6). Note: The blood vessels marked with circles show reticular structures within, indicating areas of atherosclerotic plaque formation.

DOKM Attenuates Lipid Levels in AS Zebrafish

The results are shown in Figure 11, compared with the control group, the lipid levels of the zebrafish in the AS model group were significantly higher than those in the control group. Compared to the AS model group, both 1 mg/L and 10 mg/L DOKM treatments were effective in reducing lipid levels. The results showed that the total cholesterol (TC), TG, and low-density lipoprotein (LDL)-C levels of zebrafish in the AS model group were significantly elevated compared to those in the control group, whereas both 1 mg/L and 10 mg/L DOKM treatments were effective in reducing lipid TC, TG, and LDL-C levels (Table 2). These results suggested that DOKM attenuates AS by improving lipid metabolism.

Figure 11.
DOKM alleviates lipid levels in AS zebrafish (n = 6). Note: The yellow fluorescence represents lipid accumulation and is used solely for qualitative analysis. DOKM, Dendrobium officinale Kimura & Migo.

Table 2.
Effect of DOKM on Lipid of AS Zebrafish (n = 6).

Groups TC (μmol/gprot) TG (μmol/gprot) LDL-C (μmol/gprot)

Control 195.76 + 18.46 195.37 + 37.65 377.48 + 24.59

AS model 521.47 + 36.81 ^## 570.28 + 59.48 ^## 921.19 + 70.65 ^##

1 mg/L DOKM 414.38 + 29.32 * 348.91 + 41.50 * 750.51 + 47.81 *

10 mg/L DOKM 328.69 + 24.97 279.64 + 36.83 651.80 + 43.36 *

#
P < .05, ^##P < .01 compared to the control group; *P < .05, **P < .01, compared to the AS model group. DOKM dose-dependently reduced triglyceride (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C).

Abbreviation: DOKM, Dendrobium officinale Kimura & Migo.

Effect of DOKM on pcsk9, srebf2, and hmgcr Genes in AS Zebrafish

To further investigate the possible mechanism of action of DOKM, its effect on lipid metabolism-related genes (mainly zebrafish pcsk9, srebf2, and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (hmgcr) was investigated. As shown in Table 3, the mRNA expression of pcsk9, srebf2, and hmgcr in zebrafish in the AS model group was significantly higher than that in the control group, while DOKM significantly reduced the mRNA expression of pcsk9, srebf2, and hmgcr in zebrafish.

Table 3.
Effect of DOKM on mRNA Expression of pcsk9, srebf2, and hmgcr in AS Zebrafish (n = 3).

Groups pcsk9 srebf2 hmgcr

Control 1.00 + 0.06 1.00 + 0.02 1.00 + 0.076

AS model 3.77 + 0.32 ^## 4.10 + 0.40 ^## 3.65 + 0.38 ^##

1 mg/L DOKM 2.69 + 0.25 * 2.98 + 0.49 2.55 + 0.51

10 mg/L DOKM 2.43 + 0.25 2.16 + 0.09 2.15 + 0.03 **

Note: ^# P < .05, ^##P < .01 compared to the control group; *P < .05, **P < .01, compared to the AS model group.

Abbreviation: DOKM, Dendrobium officinale Kimura & Migo.

Discussion

Hypercholesterolaemia is a significant risk factor for AS, and one of the most common methods for creating animal models of AS involves the use of a HCD.^23,24 In hypercholesterolaemia, excess cholesterol in the bloodstream directly damages the vessel intima, leading to the deposition of fatty streaks on the vessel wall, an early sign of AS.²⁵ As cholesterol accumulation in the intima worsens and endothelial damage occurs, the infiltration of neutrophils and macrophages is triggered, inducing vascular inflammation and gradually leading to plaque formation, ultimately resulting in blood vessel stenosis.²⁵ In the present study, we used an HCD to induce AS in a zebrafish model. We observed that HCD led to cholesterol accumulation, neutrophil aggregation, macrophage infiltration, and plaque formation in zebrafish blood vessels, causing vascular stenosis. This finding suggests that constructing an AS model using an HCD is feasible. Treatment with the aqueous extract of DOKM significantly mitigated HCD-induced cholesterol accumulation, neutrophil aggregation, macrophage infiltration, and plaque formation in the zebrafish vasculature. These results indicated that DOKM effectively attenuated HCD-induced AS.

Lipid metabolism disorders are common risk factors for AS, particularly low-density lipoprotein cholesterol (LDL-C), which is an independent risk factor.^26,27 LDL-C enters the vascular endothelial cells and undergoes oxidative modification, resulting in oxidised LDL (OX-LDL), which can directly damage the vascular endothelial cells and contribute to AS. OX-LDL is further phagocytosed to form foam cells, initiating early lipid streaks in AS, which may progress to atherosclerotic plaques under the influence of inflammatory mediators and fibroblast growth factors.²⁸ In this study, we observed significantly elevated lipid levels and LDL-C content in zebrafish fed an HCD, along with increased expression of genes involved in lipid metabolism, such as PCSK9, SREBF2, and HMGCR.^29–31 These findings indicated a lipid metabolism disorder in the zebrafish AS model, resembling the phenotype of AS disease, further supporting zebrafish as a suitable animal model for AS model construction. Treatment with DOKM significantly reversed the lipid metabolism disorder phenotype in atherosclerotic zebrafish, as evidenced by reduced lipid levels, decreased LDL-C content, and significantly reduced expression of the PCSK9, SREBF2, and HMGCR genes. These results suggest that DOKM effectively improves lipid metabolism disorders and that its therapeutic effect on AS may be related to its effect in improving lipid metabolism disorders.

Gigantol exerts anti-inflammatory effects by inhibiting the release of inflammatory mediators and reducing the inflammatory response, thereby reducing inflammation-induced tissue damage and disease progression.³² Previous studies demonstrated that gigantol partially alleviates CCl₄-induced liver inflammation by inhibiting the c-Jun N-terminal Kinase (JNK)/cPLA2/12-LOX pathway.³³ Moreover, gigantol targets cancer stem cells (CSCs) in tumours and reduces tumour integrity by inhibiting the PI3 K/AKT/mTOR and Janus Kinase (JAK)/STAT pathways.³⁴ Fu et al³⁵ proposed that inhibiting the activation of the JAK/STAT pathway in vivo may alleviate atherosclerotic inflammation, thus presenting a novel target for AS treatment. Platelet-derived growth factor D (PDGFD,) a recognised molecular characteristic of AS, mediates the risk of AS by promoting inflammatory responses.³⁶ Molecular docking results suggested that gigantol may treat AS by targeting JAK and PDGFD. The results demonstrated that gigantol tightly binds to the JAK2 and PDGFRα targets. Gigantol forms one hydrogen bond each with glutamic acid (GLU)-930 and leucine (LEU)-932 on the JAK2 target. Additionally, it interacts with PDGFRα by forming one hydrogen bond each with lysine (LYS)-627, cysteine (CYS)-677, and aspartic acid (ASP)-836. These interactions suggest that gigantol's binding to these specific residues is a potential mechanism for its therapeutic effects on AS.

Chrysotobibenzyl inhibits the migration of lung cancer cells and sensitises them to cisplatin-mediated apoptosis.³⁷ A meta-analysis revealed a correlation between polymorphisms in ESR1 and the severity of coronary artery disease (CAD).³⁸ Molecular docking results indicated that chrysotobibenzyl has strong binding energy with both JAK2 and ESR1 targets, suggesting its potential as a compound for AS treatment. Specifically, Chrysotobibenzyl interacts with the JAK2 target by forming a hydrogen bond with LYS-912. Additionally, chrysotobibenzyl exhibits strong binding affinity to the ESR1 target by forming two hydrogen bonds with Asparagine (ASN)-348. These interactions highlight chrysotobibenzyl's significant binding capabilities with these targets.

Naringenin chalcone inhibits cholesteryl ester transfer protein (CETP), leading to an increase in high-density lipoprotein (HDL) cholesterol levels against AS.³⁹ CETP is a key lipid transfer protein involved in the exchange of cholesteryl esters between HDL and LDLs. Increased CETP activity is typically associated with reduced levels of HDL-C, which are known to have anti-atherosclerotic properties. Furthermore, excessive CETP activity may promote inflammatory responses and oxidative stress, both of which are critical factors in the development of AS.⁴⁰ Uncontrolled EGFR expression promotes tumourigenesis by inhibiting apoptosis, angiogenesis, and tumour-associated inflammation.⁴¹ Molecular docking studies suggested that naringenin chalcone may be involved in AS treatment by targeting EGFR and AKT1. Specifically, naringenin chalcone forms four hydrogen bonds with the AKT1 target at GLU-228, Alanine (ALA)-230, GLU-234, and Asparagine (ASN)-279, indicating strong interactions. Additionally, Naringenin-Chalcone forms four hydrogen bonds with the EGFR target at Methionine (MET)-742, MET-769, Threonine (THR)-830, and ASP-831. These interactions suggest that Naringenin-Chalcone may have potential therapeutic value.

Hesperetin is a natural flavonoid with antioxidant and anti-inflammatory properties.⁴² Studies have shown that hesperetin attenuates cardiovascular oxidative and pro-inflammatory effects induced by diesel exhaust particles in Wistar rats.⁴³ Moreover, Hesperetin exhibits antioxidative stress and anti-inflammatory activities by blocking sphingosylphosphorylcholine (SPC)-induced tyrosine kinases of the SPC/Src family, thereby inhibiting SPC-induced contraction of porcine coronary artery smooth muscle.⁴⁴ SRC kinase (Src) plays a key role in macrophage activation and is implicated in immune responses and inflammatory diseases, such as AS.⁴⁵ Molecular docking results suggested that hesperetin targeted SRC and ESR1, indicating its therapeutic potential. Specifically, hesperetin forms two hydrogen bonds with Arginine (ARG)-178 and HIS-204 on the SRC target. Additionally, it interacts with the ESR1 target by forming two hydrogen bonds with GLU-353 and LEU-346. These interactions indicate that Hesperetin engages effectively with these targets.

Tangeretin has antioxidant, anti-inflammatory, antitumour, hepatoprotective, and neuroprotective effects.⁴⁶ Studies have shown its therapeutic effect on osteoarthritis (OA) mediated through Nrf2/NF-κB and MAPK/NF-κB pathways.⁴⁷ Molecular docking studies indicated strong binding energy between tangeretin and SRC and EGFR targets, suggesting its potential as a new therapeutic agent. Specifically, Tangeretin forms two hydrogen bonds each with HIS-236 and HIS-242 on the SRC target, and it also forms two hydrogen bonds with Arginine (ARG)-172. Additionally, Tangeretin interacts with the EGFR target by forming two hydrogen bonds with Lysine (LYS)-721. These interactions highlight Tangeretin's strong binding affinity with these targets.

Network pharmacology has identified targets, such as JAK2, PDGFRA, ESR1, AKT1, EGFR, and SRC, which play roles in AS through various signalling pathways. JAK2 functions via the JAK/STAT pathway by phosphorylating STAT3 to regulate gene expression, which is crucial for inflammatory responses and vascular smooth muscle cell (VSMC) proliferation. Additionally, EGFR activation enhances inflammatory responses via the JAK2-STAT3 pathway.^48,49 PDGFRA promotes VSMC proliferation and migration leading to arterial wall thickening and plaque formation. PDGFRA and EGFR signalling pathways jointly facilitate VSMC proliferation and AS progression.⁵⁰ ESR1, an oestrogen receptor, reduces inflammatory responses and oxidative stress, thereby inhibiting the onset and progression of AS. Cross-regulation of the ESR1 and AKT1 signalling pathways enhances its anti-inflammatory effects.⁵¹ AKT1 operates via the PI3 K/AKT pathway and promotes cell survival, proliferation, and metabolic activity. AKT1 modulates endothelial cell function, suppresses inflammatory responses, and along with SRC, regulates cell migration and proliferation.^49,50 EGFR and SRC activate the rat sarcoma virus protein (Ras)-rapidly accelerated fibrosarcoma kinase (RAF)-mitogen-activated protein kinase kinase (MEK)-extracellular signal-regulated kinase (ERK) and PI3K-AKT pathways, promoting cell proliferation, migration, and survival, which exacerbate the formation and instability of atherosclerotic plaques. Moreover, EGFR interacts with JAK2 and SRC pathways to regulate inflammation and cell proliferation.^51,52

SREBF2 is a transcription factor that regulates cholesterol synthesis and uptake by activating downstream genes, such as HMGCR and low-density lipoprotein receptor (LDLR), thereby promoting cholesterol production and absorption. In AS, the overactivation of SREBF2 leads to increased cholesterol synthesis, which contributes to cholesterol deposition in the arterial walls and plaque formation.⁵³ HMGCR is the rate-limiting enzyme in cholesterol synthesis that catalyses the conversion of HMG-CoA to mevalonate. High HMGCR expression results in excessive cholesterol synthesis and accumulation and promotes plaque formation and progression. Inhibition of HMGCR activity is an effective method for reducing cholesterol levels.⁵⁴ PCSK9 regulates cholesterol metabolism by promoting LDLR degradation, thereby reducing the number of LDLRs on the cell surface. This decreases the ability of the liver to clear LDL cholesterol from the blood, leading to elevated blood cholesterol levels. The inhibition of PCSK9 can increase LDLR levels, enhance cholesterol clearance, and reduce the risk of AS.⁵⁵ Experimental results show that DOKM significantly reduced the expression of SREBF2, HMGCR, and PCSK9. However, the specific active constituents responsible for this regulation have yet to be identified. Further research is needed to determine whether the reduction in gene expression is due to the direct interactions of DOKM compounds with these targets or through indirect regulatory mechanisms. This will be an important area for future research to fully understand the therapeutic potential of DOKM for the treatment of AS.

Limitations: Owing to the lack of antibodies against zebrafish, we did not perform relevant detection studies at the protein level. This was only a preliminary study of DOKM using network pharmacology and was not validated in molecular experiments. Additionally, a significant limitation is the absence of chromatographic analysis to directly confirm the presence of the bioactive compounds identified via network pharmacology in the Dendrobium officinale extract used in the animal experiments. The identification of these compounds was based solely on existing literature and computational predictions. Consequently, the actual presence and concentrations of these bioactive compounds in our specific extract remain unverified. In future studies, we aim to incorporate detailed chemical analyses using chromatography to ascertain the presence and quantify the levels of these bioactive compounds. This will enhance our understanding of the pharmacological mechanisms and ensure the reliability of our experimental results.

Conclusions

In conclusion, this study delineated gigantol, chrysobibenzyl, naringenin chalcone, hesperetin, and tangeretin as active constituents within DOKM, along with core therapeutic targets comprising STAT3, PTPN11, JAK2, EGFR, PDGFRA, SRC, STAT1, TP53, ESR1, and AKT1 for AS management, as elucidated through comprehensive network pharmacological analyses. Experimental validation in zebrafish underscored the anti-atherosclerotic efficacy of DOKM, which was potentially mediated by its ameliorative effects on lipid metabolism. These insights provide a foundational rationale for the therapeutic exploration of DOKM for AS management and prophylaxis. Nonetheless, this study has some limitations, including the elusive mechanistic underpinnings of DOKM's anti-atherosclerotic actions and the inherent challenges associated with detecting protein-level alterations in zebrafish models because of the unavailability of species-specific antibodies.

Materials and Methods

Construction and Screening of the Active Components of DOKM

To identify the active components of DOKM extract, a multi-step approach was employed. Initially, a comprehensive review of literature and public databases such as PubMed, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://old.tcmsp-e.com/tcmsp.php), and the Encyclopedia of Traditional Chinese Medicine (ETCM) (http://www.tcmip.cn/ETCM/index.php/Home/Index/index.htm) was conducted to collect information on the compounds contained in DOKM. Subsequently, the collected compounds were verified for canonical SMILES numbers through databases such as PubChem (https://pubmed.ncbi.nlm.nih.gov/) and ChemSrc (https://www.chemsrc.com/). These canonical SMILES numbers were then entered into the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) to predict the target of action of each potential active ingredient.

Analysis of the Targets of DOKM in the Treatment of AS

To identify the targets of DOKM in the treatment of AS, relevant target information was retrieved from multiple databases including GeneCards (http://www.genecards.org/), DrugBank (http://go.drugbank.com), OMIM (http://www.omim.org/), and the Therapeutic Target Database (https://db.idrblab.net/ttd/) using the keyword “atherosclerosis”. The gene search results from each database were then integrated and de-duplicated to obtain AS-related genes. These AS-related genes were further intersected with the targets of DOKM active ingredients to obtain the targets of action of DOKM for the treatment of AS. This integration and analysis were conducted using the Metascape platform for Wayne diagram analysis (https://metascape.org/gp/index.html).

Construction of the “Active Ingredient-Target-Disease” Network

To construct the “active ingredient-target-disease” network, two essential files were created: the network file containing the network structure and the attribute type file specifying the type of each node. These files were then imported into Cytoscape 3.9.1 software. In the network diagram, the active ingredients, targets, and diseases are represented by nodes, whereas the links between them are represented by edges, forming an effective network diagram. Subsequently, a software network analyser tool was used to calculate the degree of each node. Based on this analysis, core active ingredients with therapeutic effects were identified and screened from the network.

Construction of PPI Network and Screening of Core Targets

DOKM therapeutic AS targets were imported into the STRING database (http://string-db.org/). We selected the “Multiple proteins” option and set the species to “Homo sapiens”. We choose the “minimum required interaction score” option and set it to “highest confidence (0.900)”. Protein interactions were saved as TSV files. The TSV files were placed into Cytoscape 3.9.1 for visualisation. The network analyser tool of the software was used to calculate the topological features of the network. Nodes with higher degree values were considered more important. The network nodes were screened using the MCC algorithm. The top 10 targets identified by the MCC algorithm were selected as the core targets of DOKM therapy for AS.

GO function and KEGG pathway enrichment analysis. Using the Metascape database (http://metascape.org/gp/index.html), all targets screened by PPI analysis in Materials and Methods “Construction of protein interaction network” were inputted, and P < .01 was set to perform GO enrichment analysis and KEGG pathway analysis to further elucidate the molecular mechanism of DOKM active ingredient in the treatment of AS, and finally mapping was performed through the microbial letter platform (http://www.bioinformatics.com.cn/).

Molecular Docking

The structural formulas of the active ingredients were downloaded from the TCMSP database and converted to the appropriate format using the AutoDockTools software for use as ligands. Protein receptors were downloaded from the Protein Data Bank (PDB) database, and water molecules and small-molecule ligands were removed using the PyMOL software. Molecular docking was performed using the Vina software to dock the active compounds gigantol, chrysobibenzyl, naringenin chalcone, hesperetin, and tangeretin with their respective core targets identified among the top 10 by the MCC algorithm. The results were visualised and analysed using PyMOL software.

Reagents

Juvenile zebrafish basic feed was purchased from Shanghai Haisheng Biological Experimental Equipment Co. DOKM was purchased from the Anhui Dendrobium Shengji Biotechnology Co. An orange fluorescent-labelled cholesterol standard was purchased from Sigma-Aldrich. A cholesterol standard was purchased from Beijing Solebao Biotechnology Co. The remaining reagents were of analytical grade.

Instrumentation

Z-C-D6 centralised zebrafish culture units were purchased from Shanghai Haisheng Biological Experimental Equipment Co. An SZX7 Olympus stereoscope was purchased from Olympus Corporation, an HH-4 digital thermostatic water bath (Guohua Electric Co., Ltd), a LEGEND Micro 21R centrifuge (Thermo Scientific, USA), and a BS210S electronic balance (Sartorius) were also obtained.

Animals

Wild-type AB strain zebrafish, transgenic Tg(flila: enhanced green fluorescent protein (EGFP) zebrafish, Tg(lyz: DsRED2), and Tg(mpx: EGFP) zebrafish were purchased from the National Zebrafish Resource Centre. The ethical approval for the experimental procedure was obtained from Binzhou Medical College under approval number [2023–338]. Each experiment was divided into four groups, with each group consisting of six zebrafish.

Zebrafish AS Modelling

In this study, a zebrafish AS model was established by feeding zebrafish with a HCD [13]; 5 d post-fertilisation (dpf), zebrafish larvae were fed a normal basal diet for 3 d, followed by an HCD containing 8% cholesterol for 45 d.

Preparation of DOKM Water Extract

The stems of dried DOKM were crushed using a grinder. A total of 50 g was added to 500 mL distilled water for reflux extraction, and an extraction was taken again at 1 h. The aqueous extract was combined and filtered through a 200-mesh filter cloth. The aqueous extract was concentrated to 100 mL by using a rotary evaporator. The concentrate was freeze-dried into a powder and weighed. Finally, 4.68 g of powder was obtained with an extraction rate of 9.36%.

Experimental Grouping and Treatment

In this study, we divided zebrafish into the following four groups: control, AS model, and 1 and 10 mg/L DOKM. Each group was treated as follows: control group, 5 dpf zebrafish juveniles were selected and fed a normal basal diet for 3 d and then continued to feed on a normal basal diet for 45 d. AS model group: Zebrafish larvae at 5 dpf were fed a normal basal diet for 3 d and then an HCD containing 8% cholesterol for 45 d. One mg/L DOKM group: 5 dpf zebrafish larvae were fed a normal basal diet for 3 d and then fed an HCD enriched with 8% cholesterol for 45 d and treated with 1 mg/L DOKM. Ten mg/L DOKM group: 5 dpf zebrafish larvae were fed a normal basal diet for 3 d and then fed HCD enriched with 8% cholesterol for 45 d and treated with 10 mg/L DOKM.

Detection of Cholesterol Accumulation

The zebrafish AS model was established by selecting 5 dpf Tg(fli1a: EGFP) zebrafish larvae (zebrafish with green fluorescent labelling of blood vessels) and adding orange fluorescent-labelled cholesterol at a ratio of 10 ng/g to the fed diet, the experimental grouping and treatments were the same as those in Materials and Methods “Experimental grouping and treatment”. After feeding the HCD for 5 d, zebrafish in each group were fasted for 24 h and anaesthetised using tricaine, and cholesterol accumulation in the blood vessels was observed under a fluorescence microscope.

Detection of Macrophage Infiltration

The zebrafish AS model was established by selecting 5 dpf Tg(lyz: DsRED2) zebrafish larvae (zebrafish with red-coloured fluorescent labelling of macrophages), and the experimental groupings and treatments were the same as those in Materials and Methods “Experimental grouping and treatment”. After feeding the HCD for 5 d, the zebrafish in each group were fasted for 24 h and anaesthetised using tricaine, and changes in the number of macrophages in the blood vessels were observed under a fluorescence microscope.

Detection of Macrophage Infiltration

Zebrafish with 5 dpf of Tg(mpx: EGFP) zebrafish (neutrophils with green fluorescent labelling) were selected to establish the zebrafish AS model, the experimental grouping and treatments were the same as those in Materials and Methods “Experimental grouping and treatment”. After feeding HCD for 5 d, zebrafish in each group were fasted for 24 h and anaesthetised with tricaine, and changes in the number of neutrophils in the blood vessels were observed under a fluorescence microscope.

Detection of Lipid Levels

Wild-type AB strain zebrafish were used to construct the AS model, and the experimental groups and treatments were consistent with those in Materials and Methods “Experimental grouping and treatment”. After feeding HCD for 5 d, zebrafish in each group were fasted for 24 h. Using Nile red staining, the changes in lipid levels of zebrafish in each group were observed under a fluorescence microscope, which can be used to assess the lipid levels of zebrafish by observing the intensity of fluorescence.

Wild-type AB strain zebrafish were used to construct the AS model, and the experimental groups and treatments were consistent with those in Materials and Methods “Experimental grouping and treatment”. After feeding the HCD for 5 d, the zebrafish in each group were fasted for 24 h. Five zebrafish were randomly selected from each group as a sample. They were homogenised with saline (0.5 mL), the supernatant was collected, and the protein concentration was measured using Bicinchoninic Acid Assay (BCA). TC, triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) kits were used to detect TC, TG, and LDL-C in the supernatant, respectively.

Detection of Plaques

Wild-type AB strain zebrafish were used to construct the AS model, and the experimental grouping and treatment were the same as in Materials and Methods “Experimental grouping and treatment”. After feeding with an HCD for 45 d, the zebrafish in each group were fasted for 24 h. Zebrafish were euthanised using excess tricaine and fixed in 4% paraformaldehyde for more than 24 h. Zebrafish were then treated with an aqueous solution of 4% paraformaldehyde. Plaque formation in the blood vessels of the zebrafish in each group was detected using HE and EVG staining.

Detection of pcsk9, srebf2, and hmgcr Gene Expression in Zebrafish

Wild-type AB strain zebrafish were used to construct the AS model, and the experimental groups and treatments were consistent with those in Materials and Methods “Experimental grouping and treatment”. After feeding for 5 d, zebrafish in each group were fasted for 24 h. Five zebrafish were randomly selected from each group as three samples. Total RNA was extracted from the cells of each group, and quantitative real time polymerase chain reaction (q-PCR) was used to detect pcsk9, srebf2, hmgcr, and gapdh in zebrafish in each group. Primers were designed and synthesised using Primer3 software, and the sequences and other information about the primers are shown in Table 4.

Table 4.
Primer Sequence.

Gene name Species Primer sequence (5ʹ → 3ʹ)

pcsk9 Danio rerio Forward CTGGAGCGGATTTTCCCTGT

Reverse CACTCCGGATGCTTGTGTCT

srebf2 Danio rerio Forward CTCTAAGCCCCTCCCAGACT

Reverse GGGGTCCGCTTTATCTCTCG

hmgcr Danio rerio Forward ATTGGTCAAAGGCTCGTCCG

Reverse TCAAAACATCCTCCAGATCTCACT

gapdh Danio rerio Forward CAAGCGTGTGGTTGTGTCTG

Reverse TAGGCGTGGACTGTGGTCAT

Reverse transcription reaction of RNA: RT reaction solution (5 × PrimeScript RT Buffer 2 μL, total RNA, RNase Free dH₂O supplemented to 10 μL) was prepared according to the instructions, and placed in a reverse transcription apparatus to reverse into cDNA.

Using the above reverse transcription cDNA as a template, RT-PCR was used to detect the expression of pcsk9, srebf2, hmgcr, and gapdh in zebrafish. A total of 25 μL of the reaction system was used: 12.5 μL of SYBR Premix Ex Taq Ⅱ, 1 μL each of the upstream and downstream primers, 2 μL of the template, and sterilised double-distilled water was added up to 25 μL. The conditions were pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 5 s, and annealing at 60 °C for 30 s. The two-step amplification cycle described above was performed as follows: The fluorescence signal was collected at the end of each cycle, and the amplification and melting curves were confirmed at the end of the reaction.

Data Analyses

Statistical treatment was performed by applying SPSS 10.0 software, origin 8.0 to process the images, data were expressed as the $\bar{x}$ ± sd, a t-test was used for comparison between groups, ANOVA was used for comparison of multiple means, and when P ≤ .05, it indicated a significant difference.

Supplemental Material

sj-docx-1-npx-10.1177_1934578X241282497 - Supplemental material for Dendrobium Officinale Kimura & Migo Attenuates High Cholesterol Diet-Induced Atherosclerosis

Supplemental material, sj-docx-1-npx-10.1177_1934578X241282497 for Dendrobium Officinale Kimura & Migo Attenuates High Cholesterol Diet-Induced Atherosclerosis by Yang Zhang, Peng Zhao, Aiyun Guo, Jichun Han, Lin Dong, Minghao Feng, Song Han, Huanhuan Ren, Qiusheng Zheng and Lei Zheng in Natural Product Communications

Supplemental Material

sj-jpg-2-npx-10.1177_1934578X241282497 - Supplemental material for Dendrobium Officinale Kimura & Migo Attenuates High Cholesterol Diet-Induced Atherosclerosis

Supplemental material, sj-jpg-2-npx-10.1177_1934578X241282497 for Dendrobium Officinale Kimura & Migo Attenuates High Cholesterol Diet-Induced Atherosclerosis by Yang Zhang, Peng Zhao, Aiyun Guo, Jichun Han, Lin Dong, Minghao Feng, Song Han, Huanhuan Ren, Qiusheng Zheng and Lei Zheng in Natural Product Communications

Groups	TC (μmol/gprot)	TG (μmol/gprot)	LDL-C (μmol/gprot)
Control	195.76 + 18.46	195.37 + 37.65	377.48 + 24.59
AS model	521.47 + 36.81 ^##	570.28 + 59.48 ^##	921.19 + 70.65 ^##
1 mg/L DOKM	414.38 + 29.32 *	348.91 + 41.50 *	750.51 + 47.81 *
10 mg/L DOKM	328.69 + 24.97 **	279.64 + 36.83 **	651.80 + 43.36 *

Groups	pcsk9	srebf2	hmgcr
Control	1.00 + 0.06	1.00 + 0.02	1.00 + 0.076
AS model	3.77 + 0.32 ^##	4.10 + 0.40 ^##	3.65 + 0.38 ^##
1 mg/L DOKM	2.69 + 0.25 *	2.98 + 0.49	2.55 + 0.51
10 mg/L DOKM	2.43 + 0.25 **	2.16 + 0.09 **	2.15 + 0.03 **

Gene name	Species		Primer sequence (5ʹ → 3ʹ)
pcsk9	Danio rerio	Forward	CTGGAGCGGATTTTCCCTGT
Reverse	CACTCCGGATGCTTGTGTCT
srebf2	Danio rerio	Forward	CTCTAAGCCCCTCCCAGACT
Reverse	GGGGTCCGCTTTATCTCTCG
hmgcr	Danio rerio	Forward	ATTGGTCAAAGGCTCGTCCG
Reverse	TCAAAACATCCTCCAGATCTCACT
gapdh	Danio rerio	Forward	CAAGCGTGTGGTTGTGTCTG
Reverse	TAGGCGTGGACTGTGGTCAT

Footnotes

Acknowledgments

This work was supported by the Shihezi University.

Data Availability

All data used to support the findings of this study are included within the article.

Authors’ Contributions

Yang Zhang and Peng Zhao contributed equally to the work. Yang Zhang and Peng Zhao conceived and designed the experiments and wrote the paper. Yang Zhang, Peng Zhao, Qing Hao, Aiyun Guo, Song Han, Huanhuan Ren, Jichun Han, Nannan Jiang, and Qiusheng Zheng provided technical support. Nannan Jiang and Qiusheng Zheng supervised the whole project.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Science and Technology Development Fund of Shanghai Pudong New Area, Young Medical Talents Training Program of Pudong Health Bureau of Shanghai, (grant number PW2022B-18, PWRq2021-26).

ORCID iD

Jichun Han

Supplemental Material

Supplemental material for this article is available online.

References

Libby

Buring

Badimon

, et al. Atherosclerosis. Nat Rev Dis Primers. 2019;5(1):56.

Wang

Xie

Zhang

, et al. An emerging factor in atherosclerosis. Biomed Pharmacother. 2019;115:108951. doi:https://doi.org/10.1016/j.biopha.2019.108951

Global Health Estimates 2020: Deaths by cause, age, sex, by country and by Region, 2000–2019. World Health Organization; 2020.

Writing Group of the “China Cardiovascular Health and Disease Report 2022”. Key points interpretation of “China cardiovascular health and disease report 2022”[J]. Chinese Journal of Cardiovascular Diseases. 2023;28(04):297‐312.

Lawton

Tamis-Holland

Bangalore

, et al. 2021 ACC/AHA/SCAI guideline for coronary artery revascularization: executive summary: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines [published correction appears in. Circulation. 2022 Mar 15;145(11):e771.

Angiolillo

Bhatt

Cannon

, et al. Antithrombotic therapy in patients with atrial fibrillation treated with oral anticoagulation undergoing percutaneous coronary intervention: a north American perspective: 2021 update. Circulation. 2021;143(6):583‐596.

Minici

Guzzardi

Venturini

, et al. Transcatheter arterial embolization (TAE) of cancer-related bleeding. Medicina (Kaunas). 2023;59(7):1323. doi:https://doi.org/10.3390/medicina59071323

Fan

Watanabe

. Atherosclerosis: known and unknown. Pathol Int. 2022;72(3):151‐160. doi:https://doi.org/10.1111/pin.13202

Almeida

Budoff

. Effect of statins on atherosclerotic plaque. Trends Cardiovasc Med. 2019;29(8):451‐455. doi:https://doi.org/10.1016/j.tcm.2019.01.001

10.

Peng

Qin

, et al. Progress in the pharmacological mechanism of dendrobium Officinale in the treatment of diabetes[J/OL]. World Science and Technology - Modernization of Traditional Chinese Medicine. 2024;06(14):1‐9. http://kns.cnki.net/kcms/detail/11.5699.R.20240529.1353.020.html. Available from.

11.

National Pharmacopoeia Commission. Pharmacopoeia of the People's Republic of China: Part I. China Medical Science Press; 2020:282‐283.

12.

Wang

Tong

Adejobi

Wang

Liu

. Research advances in multi-omics on the traditional Chinese herb dendrobium officinale. Front Plant Sci. 2022;12:808228. Published 2022 Jan 11. doi:https://doi.org/10.3389/fpls.2021.808228

13.

Qu W Chen

. Inhibitory effect of dendrobium ironpierre on myocardial fibrosis in rats with heart failure and heart qi deficiency after ischaemia-reperfusion. Chinese Journal of Experimental Formulas. 2019;25(15):83‐88. doi:https://doi.org/10.13422/j.cnki.syfjx.20191502

14.

Liu

Zhao

Gao

, et al. Antioxidant and anti-inflammatory effects of dendrobium officinale extract on DSS-induced ulcerative colitis mice. Chinese Journal of New Drugs. 2019;28(02):214‐220.

15.

Chen

Zhang

, et al. Traditional uses, phytochemistry, pharmacology, and quality control of dendrobium officinale Kimura et. Migo. Front Pharmacol. 2021;12:726528. Published 2021 Aug 6. doi:https://doi.org/10.3389/fphar.2021.726528

16.

Zhang

Liu

Sun

, et al. Comparison of the antioxidant activities and polysaccharide characterization of fresh and dry dendrobium officinale Kimura et Migo. Molecules. 2022;27(19):6654. Published 2022 Oct 7. doi:https://doi.org/10.3390/molecules27196654

17.

Guo

Yang

, et al. Dendrobium officinale Kimura & Migo polysaccharide and its multilayer emulsion protect skin photoaging. J Ethnopharmacol. 2024;318:116974. doi:https://doi.org/10.1016/j.jep.2023.116974

18.

Zhan

, et al. Corrigendum: polysaccharides of dendrobium officinale Kimura & Migo leaves protect against ethanol-induced gastric mucosal injury via the AMPK/mTOR signaling pathway in vitro and vivo. Front Pharmacol. 2022;13:948569. Published 2022 Aug 15. doi:https://doi.org/10.3389/fphar.2022.948569

19.

Zhao

Zhu

, et al. Integrated component identification, network pharmacology, and experimental verification revealed mechanism of dendrobium officinale Kimura et Migo against lung cancer. J Pharm Biomed Anal. 2024;243:116077. doi:https://doi.org/10.1016/j.jpba.2024.116077

20.

Piao

Feng

Zhao

, et al. Dendrocandin U from dendrobium officinale Kimura et Migo inhibits M1 polarization in alveolar macrophage by suppressing NF-κB signaling pathway. Chem Biodivers. 2023;20(11):e202300999. doi:https://doi.org/10.1002/cbdv.202300999

21.

Zhou

Yue

. Anti-cyclooxygenase, anti-glycation, and anti-skin aging effect of dendrobium officinale flowers’ aqueous extract and its phytochemical validation in aging. Front Immunol. 2023;14:1095848. Published 2023 Mar 17. doi:https://doi.org/10.3389/fimmu.2023.1095848

22.

Zhou

, et al. Network pharmacology and molecular docking technology-based predictive study of the active ingredients and potential targets of rhubarb for the treatment of diabetic nephropathy. BMC Complement Med Ther. 2022;2022:210. doi:https://doi.org/10.1186/s12906-022-03662-6

23.

Feng

. Hypercholesterolemia tunes hematopoietic stem/progenitor cells for inflammation and atherosclerosis. Int J Mol Sci. 2016;17(7):1162.

24.

Padró

Vilahur

Badimon

. Dyslipidemias and microcirculation. Curr Pharm Des. 2018;24(25):2921‐2926.

25.

Han

Zhang

, et al. Zebrafish model for screening antiatherosclerosis drugs. Oxid Med Cell Longev. 2021;2021:9995401. doi:https://doi.org/10.1155/2021/9995401

26.

Khatana

Saini

Chakrabarti

, et al. Mechanistic insights into the oxidized low-density lipoprotein-induced atherosclerosis. Oxid Med Cell Longev. 2020;2020:5245308.

27.

Hartley

Haskard

Khamis

. Oxidized LDL and anti-oxidized LDL antibodies in atherosclerosis - novel insights and future directions in diagnosis and therapy. Trends Cardiovasc Med. 2019;29(1):22‐26. doi:10.1155/2020/5245308.

28.

Rhoads

Major

. How oxidized low-density lipoprotein activates Inflammatory Responses. Crit Rev Immunol. 2018;38(4):333‐342.

29.

Yurtseven

Ural

Baysal

Tokgözoğlu

. An update on the role of PCSK9 in atherosclerosis. J Atheroscler Thromb. 2020;27(9):909‐918.

30.

García-García

Martínez-Hervás

Vernia

, et al. A very rare variant in SREBF2, a possible cause of hypercholesterolemia and increased glycemic levels. Biomedicines. 2022;10(5):1178.

31.

Wang

Yang

. Protective effect of atorvastatin meditated by HMGCR gene on diabetic rats with atherosclerosis: an in vivo and in vitro study. Biomed Pharmacother. 2018;104:240‐251. doi:https://doi.org/10.1016/j.biopha.2018.04.179

32.

Chen

Huang

Lin

Wei

. Gigantol attenuates the proliferation of human liver cancer HepG2 cells through the PI3 K/Akt/NF-κB signaling pathway. Oncol Rep. 2017;37(2):865‐870. doi:https://doi.org/10.3892/or.2016.5299

33.

Xue

Deng

Zhang

, et al. Gigantol ameliorates CCl₄-induced liver injury via preventing activation of JNK/cPLA2/12-LOX inflammatory pathway. Sci Rep. 2020;10(1):22265.

34.

Losuwannarak

Maiuthed

Kitkumthorn

Leelahavanichkul

Roytrakul

Chanvorachote

. Gigantol targets cancer stem cells and destabilizes tumors via the suppression of the PI3 K/AKT and JAK/STAT pathways in ectopic lung cancer Xenografts. Cancers (Basel). 2019;11(12):2032. Published 2019 Dec 17. doi:https://doi.org/10.3390/cancers11122032

35.

Sun

Long

, et al. Glycosides from Buyang Huanwu decoction inhibit atherosclerotic inflammation via JAK/STAT signaling pathway. Phytomedicine. 2022;105:154385. doi:https://doi.org/10.1016/j.phymed.2022.154385

36.

Konwerski

Gromadka

Arendarczyk

, et al. Atherosclerosis pathways are activated in pericoronary adipose tissue of patients with coronary artery disease. J Inflamm Res. 2021;14:5419‐5431. Published 2021 Oct 20. doi:https://doi.org/10.2147/JIR.S326769

37.

Petpiroon

Bhummaphan

Tungsukruthai

, et al. Chrysotobibenzyl inhibition of lung cancer cell migration through caveolin-1-dependent mediation of the integrin switch and the sensitization of lung cancer cells to cisplatin-mediated apoptosis. Phytomedicine. 2019;58:152888. doi:https://doi.org/10.1016/j.phymed.2019.152888

38.

Raina

Sharma

Panjaliya

Dogra

Bakaya

Kumar

. Association of ESR1 (rs2234693 and rs9340799), CETP (rs708272), MTHFR (rs1801133 and rs2274976) and MS (rs185087) polymorphisms with coronary artery disease (CAD). BMC Cardiovasc Disord. 2020;20(1):340. Published 2020 Jul 18. doi:https://doi.org/10.1186/s12872-020-01618-7

39.

Hirata

Takazumi

Segawa

, et al. Xanthohumol, a prenylated chalcone from Humulus lupulus L., inhibits cholesteryl ester transfer protein. Food Chem. 2012;134(3):1432‐1437. doi:https://doi.org/10.1016/j.foodchem.2012.03.043

40.

Deng

Liu

Niu

. HDL And cholesterol ester transfer protein (CETP). Adv Exp Med Biol. 2022;1377:13‐26. doi:https://doi.org/10.1007/978-981-19-1592-5_2

41.

Talukdar

Emdad

Das

Fisher

. EGFR: an essential receptor tyrosine kinase-regulator of cancer stem cells. Adv Cancer Res. 2020;147:161‐188. doi:https://doi.org/10.1016/bs.acr.2020.04.003

42.

Khan

Ikram

Hahm

Kim

. Antioxidant and anti-inflammatory effects of Citrus flavonoid hesperetin: special focus on neurological disorders. Antioxidants (Basel). 2020;9(7):609. Published 2020 Jul 10. doi:https://doi.org/10.3390/antiox9070609

43.

Olumegbon

Lawal

Oluyede

Adebimpe

Elekofehinti

Umar

. Hesperetin protects against diesel exhaust particles-induced cardiovascular oxidative stress and inflammation in Wistar rats. Environ Sci Pollut Res Int. 2022;29(35):52574‐52589. doi:https://doi.org/10.1007/s11356-022-19494-3

44.

Kishi

Zhang

Morita

Kobayashi

. Hesperetin inhibits sphingosylphosphorylcholine-induced vascular smooth muscle contraction by regulating the Fyn/Rho-Kinase pathway. J Cardiovasc Pharmacol. 2022;79(4):456‐466. Published 2022 Jan 5. doi:https://doi.org/10.1097/FJC.0000000000001210

45.

Byeon

Yoo

Hong

Cho

. The role of src kinase in macrophage-mediated inflammatory responses. Mediators Inflamm. 2012;2012:512926. doi:https://doi.org/10.1155/2012/512926

46.

Ashrafizadeh

Ahmadi

Mohammadinejad

Ghasemipour Afshar

. Tangeretin: a mechanistic review of its pharmacological and therapeutic effects. J Basic Clin Physiol Pharmacol. 2020;31(4):/j/jbcpp.2020.31.issue-4/jbcpp-2019-0191/jbcpp-2019-0191.xml. Published 2020 Apr 23. doi:https://doi.org/10.1515/jbcpp-2019-0191

47.

Shi

Chen

, et al. Tangeretin suppresses osteoarthritis progression via the Nrf2/NF-κB and MAPK/NF-κB signaling pathways. Phytomedicine. 2022;98:153928. doi:https://doi.org/10.1016/j.phymed.2022.153928

48.

Longhi

Silva

Pereira

, et al. PI3K-AKT, JAK2-STAT3 pathways and cell–cell contact regulate maspin subcellular localization. Cell Commun Signal. 2021;19:86. doi:https://doi.org/10.1186/s12964-021-00758-3

49.

Yin

Huang

. Stat3 signaling pathway: a future therapeutic target for bone-related diseases. Front. Pharmacol. 2022;13:897539. doi:https://doi.org/10.3389/fphar.2022.897539

50.

Zhao

Qian

Sun

, et al. Role of PI3 K in the progression and regression of atherosclerosis. Front. Pharmacol. 2021;12:632378. doi:https://doi.org/10.3389/fphar.2021.632378

51.

Zheng

Niu

, et al. Paeoniflorin improves ulcerative colitis via regulation of PI3K-AKT based on network pharmacology analysis. Exp Ther Med. 2024;27:125. doi:https://doi.org/10.3892/etm.2024.12414

52.

Ortiz

Mikhailova

, et al. Src family kinases, adaptor proteins and the actin cytoskeleton in epithelial-to-mesenchymal transition. Cell Commun Signal. 2021;19:67. https://doi.org/10.1186/s12964-021-00750-x

53.

Berg

Polyzos

Agardh

, et al. 3-Hydroxyanthralinic Acid metabolism controls the hepatic SREBP/lipoprotein axis, inhibits inflammasome activation in macrophages, and decreases atherosclerosis in Ldlr-/- mice. Cardiovasc Res. 2020;116(12):1948‐1957. doi:https://doi.org/10.1093/cvr/cvz258

54.

Yamada

Kawaguchi

Matsuoka

, et al. Development of a microminipig model of atherosclerosis for the evaluation of a HMGCR inhibitor. In Vivo. 2024;38(1):98‐106. doi:https://doi.org/10.21873/invivo.13415

55.

Popa-Fotea

N-M

Ferdoschi

C-E

Micheu

M-M

. Molecular and cellular mechanisms of inflammation in atherosclerosis. Front. Cardiovasc. 2023;10:1200341. doi:https://doi.org/10.3389/fcvm.2023.1200341

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