The Mechanism of Dendrobium officinale as a Treatment for Hyperlipidemia Based on Network Pharmacology and Experimental Validation

Materials and Methods The active compounds in DO, their targets, and targets associated with hyperlipidemia were screened across various databases, and the hidden targets of DO in treating hyperlipidemia were forecast. The compound-target (C-T), protein-protein interaction (PPI), and compound-target-pathway (C-T-P) networks of DO were set up with Cytoscape software. The hub genes and core clusters of DO predicted to be active against hyperlipidemia were calculated by Cytoscape. The DAVID database was adopted for Gene Ontology (GO) analysis and KEGG pathway enrichment analysis. Next, we used the high-sucrose-fat diet and alcohol (HFDA)-induced hyperlipidemia rats to evaluate the hypolipidemic effect of DO. Results In this study, we obtained 264 compounds from DO, revealed 11 bioactive compounds, and predicted 89 potential targets of DO. The network analysis uncovered that naringenin, isorhamnetin, and taxifolin might be the compounds in DO that are mainly in charge of its roles in hyperlipidemia and might play a role by modulating the targets (including PPARG, ADIPOQ, AKT1, TNF, and APOB). The pathway analysis showed that DO might affect diverse signaling pathways related to the pathogenesis of hyperlipidemia, including PPAR signaling pathway, insulin resistance, AMPK signaling pathway, and non-alcoholic fatty liver disease simultaneously. Meanwhile, in the HFDA-induced hyperlipidemia rat model, DO could significantly decrease the level of TC, TG, LDL-c, and ALT in serum, and increase HDL-c as well. The liver pathological section indicated that DO could ease liver damage and lipid cumulation. Conclusion In summary, the biological targets of the main bioactive compounds in DO were found to distribute across multiple metabolic pathways. These findings suggest that a mutual regulatory system consisting of multiple components, targets, and pathways is a likely mechanism through which DO may improve hyperlipidemia. Validation experiments indicated that DO may treat hyperlipidemia by affecting NAFLD-related signaling pathways.


Introduction
As an abnormality of lipid metabolism, hyperlipidemia has characteristics such as an increase of total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-c), and/or a decrease in high-density lipoprotein cholesterol (HDL-c) in circulating plasma. Hyperlipidemia is associated with etiopathogenesis of different diseases, including atherosclerosis, hypertension, metabolic syndrome, and cardiovascular disease (CVD) [1]. Numerous studies have demonstrated that dyslipidemia characterized by elevated LDL-c or TC presents a high risk of CVD [2]. Indeed, lowering LDL-c levels can significantly reduce the risk of arteriosclerotic cardiovascular disease (ASCVD) morbidity and mortality [3]. Other types of dyslipidemia, such as elevated TG or decreased HDL-c, are also associated with increased ASCVD risk [4]. erefore, the effective control of dyslipidemia is important for the prevention of cardiovascular and cerebrovascular diseases.
Monotherapy with pharmacologic agents is inherently challenging for the treatment of hyperlipidemia. Indeed, hyperlipidemia requires a comprehensive approach including changes in diet, exercise, and pharmaceutical therapy. Currently, the commonly used lipid-lowering drugs such as statins and fibrates usually have side effects and contraindications with long-time application, and many clinicians would prefer their patients to regulate dyslipidemia through healthy lifestyle modifications [5]. In contrast to the potential toxicity posed by pharmacologic agents, the alternative treatments and traditional herbal medicines used to treat hyperlipidemia are exceptionally safe [6]. In China and many East Asian countries, traditional Chinese medicine (TCM) is extensively adopted for the prophylaxis and treatment of hyperlipidemia in clinical practice [7]. Indeed, TCM is thought to present several unique advantages for prevention or treatment of hyperlipidemia, due to its action through multiple components, approaches, and targets [8].
Dendrobium officinale (DO) is the most valuable species in the Dendrobium genus, which is mainly observed in the Zhejiang, Yun-nan, and Guangxi Province of China [9]. Its stem has been conventionally used as both food and medicine for centuries of clinical use, and it is also noted in the "Pharmacopoeia of the People's Republic of China" [10]. Recent pharmacological studies have suggested that it may have a hepatoprotective effect, enhance immunity, antioxidant properties, and hypoglycemic effect, and protect against gastric ulcers [11][12][13][14][15]. Remarkably, increasing evidence has shown that DO can also modulate the blood-lipid disorder in ApoE −/− mice [16] and in other hyperlipidemic models [17]. Our previous studies [11,18] revealed that DO reduces serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT). ose outcomes indicate that DO has strong potential as a therapeutic for hyperlipidemia. However, the material basis and mechanism of action remain unclear and require further study.
Network pharmacology is a new approach of "active compound-target-disease" interaction network based on multidisciplinary integration theory [19]. Complicated diseases such as metabolic diseases are not due to single mutations or dysfunction of a single signaling pathway. Instead, they are brought by multiple mutations or dysfunction of an entire regulatory network. Unlike the "one drug, one target" approaches, network pharmacology pays attention to the reality that a lot of active ingredients may interact with various diverse genes or proteins, this analytical principle shared with the holistic approach of TCM [20]. Network pharmacology can predict the effects of drugs on potential molecular disease networks in systematical and comprehensive way, enabling observation of the of multicomponent, multi-pathway, and multi-target synergies that may be afforded by TCM [21]. erefore, the molecular mechanism of DO as a treatment for hyperlipidemia was clarified with network pharmacology. Furthermore, we validated experiments in hyperlipidemic rats induced with high-sucrose-fat diet and alcohol (HFDA) to further verify the effect of DO in ameliorating hyperlipidemia.
ese findings may lay a foundation for further exploration of the therapeutic value of DO as a treatment for hyperlipidemia and other clinical applications.

Materials and Methods
is research adopted network pharmacology and validation experiments to unveil the biochemistry basis and underlie mechanisms of DO as a treatment for hyperlipidemia. Figure 1 shows an overview of the experimental steps. en, analyzing and collating the literature to obtain the chemical constituents of DO were performed.

Network Pharmacology
All DO's compounds were input into PubChem (https:// pubchem.ncbi.nlm.nih.gov/) [22] and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/index.php) [23] to get the 3D molecular structure files. Because we failed to predict the targets of the compounds without accurate structural data successfully, these chemical data without precise structural information are deleted.
We used the TCMSP to analyze the DO compounds collected above, and two silico ADME models were used to obtain the active ingredients in DO, including Per OB (forecast of oral bioavailability) and Per DL (forecast of drug-likeness). e thresholds of the two screening models were set as Per OB ≥ 30% and Per DL ≥ 0.18. e obtained active compounds were adopted as the candidate active compounds [24].

Targets for the Active Compounds.
TCMSP was adopted to screen the targets of candidate active substances in DO. e collected targets were confirmed with the Uniprot protein sequence resource (http://www.Uniprot. org/), including name, gene ID, and organism. e active compounds without targets were eliminated; the active compound-target dataset was set up.

Construction of Active Compound-Target Network.
Cytoscape 3.7.0 platform (http://cytoscape.org/) [25] provided the candidate active compounds and their potential targets to set up the active compound-target network. e key architecture of the Cytoscape 3.7.0 platform is a network with a gene, protein, or molecule as each node, and the associations between the nodes refer to the mutual effect between these biological explorations [26].
Disease targets were collected using "hyperlipidemia" or "HLP" as keywords. In GeneCards database, target genes were chosen with the Relevance Score ≥1, and in DisGeNET database, target genes were chosen with the Gene-Disease Score ≥0.1. After duplicates were deleted, hyperlipidemiarelated targets were obtained [31].

Protein-Protein Interaction (PPI) Network Construction and Hub Gene Analysis.
e Venn diagram was set up through an online website (https://bioinfogp.cnb.csic.es/ tools/venny/index.html) to acquire the common targets for DO bioactive compound targets and the hyperlipidemia associated targets. hyperlipidemia. e active compounds of DO were conducted with various bibliographical databases, and their potential targets were recognized by TCMSP database. Next, the genes associated with hyperlipidemia were filtered by the OMIM, DisGeNET, GeneCards, and PHGKB database. e "compound-target," protein-protein interaction (PPI), and "compound-target-pathway" network of DO was constructed by Cytoscape software. e hub genes and core clusters of DO against hyperlipidemia were calculated by Cytoscape software. DAVID database was adopted for Gene Ontology (GO) and KEGG pathway enrichment analyses. en, the common target genes were input into the STRING database (http://string-db.org/) [32] to explore the protein interaction, and the PPI network was constructed with the Cytoscape. e nodes in PPI network were performed by "Network Analyzer," and the Hub genes of DO against hyperlipidemia were calculated by CytoHubba (http://apps.cytoscape.org/apps/cytohubba) [33] plugin by MCC algorithm in this PPI network.
2.1.6. Identifying Core Clusters of PPI Network. In the complicated biological data network, several genes or proteins are closely associated with each other with the same or similar functions, so they can exert a significant biological coordination effect as a cluster. e data of every node in the network assists in analyzing clusters and constructing functional modules [34]. e functional modules of the PPI network in the hyperlipidemia treated with DO were selected by using the Molecular Complex Detection (MCODE) (a plugin in Cytoscape, https://apps.cytoscape.org/apps/ mcode).

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis.
As an effective bioinformatics tool, the GO analysis can characterize molecular function (MF), cellular components (CC), and biological process (BP) of genes [35]. e KEGG enrichment exploration collects databases illustrating biological paths, genomes, drugs, and diseases [36]. e Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/), which is an integrated functional annotation tool, was used to know the biological significance behind the large gene datasets [37]. In this study, the GO function and KEGG pathway enhancement of proteins taking part in PPI network was analyzed with DAVID database.

Compound-Target-Pathway for DO as a Treatment for
Hyperlipidemia Network Construction. We used Cytoscape software to construct the "Compounds-targets-pathways (C-T-P)" network for DO as a treatment for hyperlipidemia according to the active compounds in DO, intersecting the targeted genes related hyperlipidemia with the DO active compounds through the pathway from KEGG analysis. In this network, every compound, target, or pathway is represented by a node, and every interaction by an edge. At the same time, the plugin Network Analyzer of Cytoscape 3.7.0 was adopted to analyze the degree representing the number of edges interacting with the node. Zhejiang Senyu Co., Ltd. (Zhejiang China) offered DO, the origin is Zhejiang (Yiwu, Zhejiang), and the growing age is three years old. DO was pulverized into powder (D 90 size distribution of 35.01 ± 1.19 μm) and pure water used to prepare with a concentration of 60 mg/mL (crude drug) for reserve. High-sucrose-fat diet (lard 10%, cholesterol 2%, bile salt 0.5%, egg yolk powder 5%, sucrose 10% and basic diet 72.5%; w/w) and basic diet were all produced by Zhejiang Academy of Medical Science (Hangzhou, China).

Animals and Experimental Design. Animal Supply
Center of Zhejiang Academy of Medical Science (20200907Aazz0100018868, Hangzhou, China) offered Sprague-Dawley (SD) rats (n � 30). e hyperlipidemia model of rats induced by high-sucrose-fat diet and alcohol (HFDA) and the specific methods were as follows: the rats were fed with high-sugar and high-fat diet (normal diet 76.5%, fructose 10%, edible lard 10%, cholesterol 1.2%, bile salt 0.25%); at the same time, Red Star (Hongxing) and Erguotou (alcohol volume fraction gradually increased from 4% to 22%) were added into the drinking water of the rats; 4% alcohol was given for 4 consecutive days at the beginning of modeling, grew to 8% on the fifth day, and then increased by 4% every other 3 days until 22% (Table 1). Once the extents of TC, TG, and LDL-C in serum greatly grew, and the differences were significant compared with the normal group (NG), indicating that the hyperlipidemia model was successfully constructed, all animals are raised under standard environmental conditions and comply with the Rules for the Use and Care of Laboratory Animals. en, SD rats were firstly fallen into 3 groups (n � 10): (1) normal control group (NG); (2) hyperlipemia model group (MG); (3) DO treatment group (DO, 600 mg/kg/d, P.O.). e NG rats received the basic diet and water throughout the whole experiment, and the remaining twenty rats were supplied with HFDA. After modeling for 8 weeks, the HFDA-induced hyperlipidemic rats were randomly assigned to 2 groups (MG and DO) according to the serum TC level, and then the MG and DO groups continued to be supplied with HFDA. NG and MG were given corresponding distilled water by intragastric administration, and DO was given DO (600 mg/kg/d, P.O.). During the experiment, the record of body weight was made every week. After 6 weeks of administration, the blood was collected through orbital vein after 12 hours of fasting. After the experiment, the livers were weighed, and the biggest lobes of livers were fixed with 4% paraformaldehyde, and the rest were kept at −80°C until the next use.

Determination of TC, TG, LDL-c, HDL-c, AST, and ALT in Serum.
e serum was centrifuged at 3500 rpm for 10 min at 4°C. e levels of TC, TG, HDL-C, ALT, and AST in serum were detected by automatic biochemical analyzer (Hitachi 7020, Japan). Friedewald's formula as LDL-c � TC -(HDL-c + TG/2.2) [38] was adopted to calculate serum LDL-c.

Histological Analysis.
Liver histopathology was evaluated with hematoxylin-eosin staining (H&E) and Oil red O staining. e liver specimens were fixed in 4% paraformaldehyde, embedded in paraffin, and sliced at 3 µm thickness, and then, the staining of sections acquired was made with hematoxylin and eosin (H&E) for the histological test [39]. 0.2% Oil-Red O was employed to stain cryosections of liver, followed by counterstaining with hematoxylin for visualizing the lipid droplets. Biological microscope (Olympus BX43, Japan) was adopted to observe tissue sections, followed by analysis by Image-Pro Plus software.

Statistical Analysis
All measurements in this research were shown as means ± standard deviation and subjected to one-way analysis of variance (ANOVA). P < 0.05 was of statistical significance. SPSS 17.0 statistical software was adopted to perform all analyses.

Screening for Active Compounds in DO.
According to comprehensive search by using various bibliographical databases, we obtained about 264 compounds in DO; the main chemical compounds in DO are phenanthrenes, bibenzyls, phenols, acids, esters, amides, saccharides, glycosides, essential oils, and so on (for more detailed information, see Supplementary Table S1: chemical compounds in DO).
We input 264 compounds of DO into PubChem and TCMSP to get the 3D molecular structure files and removed these chemical data that were without precise chemical structural information. Eventually, 139 compounds with chemical structural information were reserved for further study (Supplementary Table S2: 139 compounds with structural information in DO). Two silico ADME models (Per OB ≥ 30% and Per DL ≥ 0.18) were used to analyze the DO compounds collected above, and we have got 11 active compounds finally ( Table 2).

Compound-Target (C-T) Network Analysis.
e active compounds with no targets on basis of the TCMSP database or that had no related gene name on basis of the Uniprot database were removed. In the end, 10 compounds and 89 candidate targets were obtained according to Table 3.
A visualized Compound-Target network (C-T network) diagram was established on basis of hidden ingredients and targets by Cytoscape software, and it is shown in Figure 2.
ere were 99 nodes (10 bioactive compound nodes, 89 target nodes) and 141 edges in this network. e mean extent of per compound was 7.8, and naringenin (MOL004328, degree � 37) and isorhamnetin (MOL000354, degree � 37) have a higher extent, showing more mutual effects with targets, and might be the core active compounds on antihyperlipidemia.

Potential Targets of Hyperlipidemia.
rough these four databases, GeneCards, DisGeNET, OMIM, and PHGKB, we obtained 802, 69, 102, and 545 hyperlipidemia-associated targets, respectively. Concluding the outcomes of different databases and eliminating duplicate genes, 1210 targets associated with hyperlipidemia were picked up. e details of hyperlipidemia-associated targets were offered in Supplementary (Supplementary Table S3: the detailed information of hyperlipidemia-related targets).

Integration of the PPI Network and Analysis of Hub Gene.
e Venn diagram was set up by an online website (https:// bioinfogp.cnb.csic.es/tools/venny/index.html) to acquire the 44 common targets for DO bioactive compound targets and the hyperlipidemia related targets (Figure 3(a)).
We input these common genes into the STRING online website (PPI score >0.4) to create the PPI network, and finally the network made up of 44 interaction nodes and 245 interaction edges were created. As shown in Figure 3(b), the size of the nodes and edges correspond to the value of degree and integrate mark. e color of the nodes indicates the value of degree. In case of darker color (red), higher degree was indicated.
e Hub genes were screened by CytoHubba (the plugin based on Cytoscape) from the interaction network. And then, we used MCC algorithm to find out the top 6 Hub genes of DO as a treatment for hyperlipidemia ( Figure 3(c)), which were RAC-alpha serine/threonine-protein kinase (AKT1), Tumor necrosis factor (TNF), Peroxisome proliferator activated receptor gamma (PPARG), Adiponectin (ADIPOQ), Apolipoprotein B-100 (APOB), and Nitricoxide synthase endothelial (NOS3). ese hub genes were input into the DisGeNET database to acquire the protein class of genes according to Table 4.

Identification of Core PPI Clusters.
On basis of the MCODE clustering exploration, the key PPI network of DO for hyperlipidemia could be fallen into 3 modules (Figure 4(a)). ere were 21 genes in cluster 1 (MCODE 1), mark 7.17, and the key gene was Apolipoprotein B-100 (APOB); there were 7 genes in cluster 2 (MCODE 2), mark 5.80, and the key gene was Glucocorticoid receptor (NR3C1); there were 4 genes in cluster 3 (MCODE 3), mark 4.33, and the key gene was Retinoic acid receptor RXR-alpha (RXRA).
KEGG enrichment exploration was conducted on cluster 1 (McOde1). According to the P value, top 10 KEGG enrichment pathways were acquired and framed in a bubble plot (Figure 4(b)). As shown in figure, those genes of cluster Evidence-Based Complementary and Alternative Medicine  6 Evidence-Based Complementary and Alternative Medicine Evidence-Based Complementary and Alternative Medicine 1 were related with the insulin signaling pathway, AMPK signaling pathway, and nonalcoholic fatty liver disease (NAFLD).

GO Functional Enrichment and KEGG Pathway Analysis.
To elucidate the multiple mechanisms of DO in treating hyperlipidemia from an integrated level, GO enrichment analysis was made on the biological process, molecular function, and cellular component of the 44 common targets. Figure 5 showed the top 10 greatly enhanced GO terms of these targets (FDR < 0.05). e outcomes showed that the targets of DO were greatly related to 5 biological processes (BP): cholesterol homeostasis, low-density lipoprotein particle clearance, cholesterol metabolic process, and transcription DNA-templated and circadian rhythm; 5 molecular functions (MF): peroxisome, nuclear chromatin, perinuclear region of cytoplasm, receptor complex, and plasma membrane; 5 cellular components (CC): steroid binding, steroid hormone receptor activity, sequence-specific DNA binding, heme binding, and zinc ion binding. As shown in Figure 6, we analyzed the top 20 significantly enriched KEGG pathways of these targets (FDR < 0.05). e result indicated that the targets are mostly associated with signal pathways including PPAR signaling pathway, insulin resistance, AMPK signaling pathway, nonalcoholic fatty liver disease (NAFLD), and thyroid hormone signaling pathway.

Compound-Target-Pathway for DO against Hyperlipidemia Network Analysis.
To construct the "Compound-target-pathway (C-T-P)" network as shown in Figure 7, we assembled the key pathways by analyzing C-T-P network. Naringenin (MOL004328, degree � 37), isorhamnetin (MOL000354, degree � 37), and taxifolin (MOL004576, degree � 12) possess higher degrees, showing that more mutual effects with targets and signaling pathways might be the core active compounds on anti-hyperlipidemia. ere were 264 compounds acquired from DO by various bibliographical databases, and 11 candidate molecules were selected on basis of coefficients of ADME nature (Per OB ≥ 30%, Per DL ≥ 0.18). TCMSP: e traditional Chinese medicine systems pharmacology database and analysis platform (http:// tcmspw.com/index.php). ADME: Absorption (OB), distribution (DL), metabolism, and excretion. MW: molecular weight.  By analyzing C-T-P network, we picked out 5 important signaling pathways that were significantly associated with DO as a treatment for hyperlipidemia. As shown in Table 5, the 5 chosen pathways included insulin resistance (degree � 8), nonalcoholic fatty liver disease (degree � 8), pathways in cancer (degree � 8), AMPK signaling pathway (degree � 7), and thyroid hormone signaling pathway (degree � 7). Calmodulin CAM Figure 2: Compound-Target network. ere were 99 nodes (10 bioactive compound nodes, 89 target nodes) and 141 edges in this network, and the red node refers to the compounds, and the green stands for the targets. Mean extent of per compound was 7.8, aringenin (MOL004328, degree � 37) and isorhamnetin (MOL000354, degree � 37) have a higher degree, showing more mutual effects with targets and might be the core active compounds on anti-hyperlipidemia. Besides, ingredients were actived by correlating candidate targets of PTGS2 (degree � 9), PTGS1 (degree � 7), HSP90 (degree � 7), PIK3CG (degree � 5) and PRKACA (degree � 4).

Effect of DO on TC, TG, LDL-c, and HDL-c in Serum.
To examine the effect of DO on the blood lipid degree in HFDA-induced hyperlipidemia rats, serum TC, TG, LDL-c, and HDL-c both were measured before and after DO administration. As shown in Figure 8, after modeling for 8 weeks, by comparing with NG before treatment, the serum TG, TC, and LDL-c all greatly grew, and the serum HDL-c greatly decreased in HFDA-induced hyperlipidemia rats (P < 0.01). After administrating the DO for 6 weeks, the serum TC, TG, and LDL-c significantly decreased, and the serum HDL-c was elevated in a significantly different way (P < 0.05 − 0.01) by comparing with the MG. ese results indicate that DO can decrease the serum TC, TG, and LDL-c and increase the serum HDL-c to influence the blood lipid.

Effect of DO on Liver Function Biomarkers.
Long-term disturbed lipid homeostasis can lead to hepatic lipid lesions. We detected serum AST and ALT levels after administration of DO to decide the liver function. Figure 9 displays that, by comparing with NG, the serum ALT (Figure 9(c)) grew greatly in MG rats (P < 0.01) without impact on AST (Figure 9(d)). After administration of DO for 6 weeks, the serum AST in DO group significantly decreases compared with MG (P < 0.05) (Figure 9(c)), but no impact on AST (Figure 9(d)) was shown. ese outcomes suggest that DO can improve liver damage caused by lipid metabolism disorder to a certain extent. Compared with NG, the liver weight was greatly grown in MG rats, and by comparing with MG, the liver index in treatment group was significantly decreased (Figure 9(b)). e liver of NG rats was reddish-brown, while the liver of MG rats was yellow with obvious white spots on its surface, which shows hepatic steatosis. On the contrary, the liver improved significantly in the DO group, as shown in Figure 9(a).

Histological Analysis of DO on Liver.
In this research, 10 photomicrographs of HE-staining were chosen from each group to calculate the NAS mark at a magnification of ×400. According to liver sections in NG, the structures of tissues were normal in polygonal edge, clear cell boundary, and clear round nucleus (Figure 10(a)). On the contrary, the MG showed visible histological variations such as cellular edema, focal degeneration, and necrosis. Similarly, despite degeneration, DO group is significantly better than the MG group. However, the tissues in DO have recovered to some extent, and cell edema was nearly observed with unique and clear tissue boundaries. Meanwhile, the NAS mark in the MG was greatly higher than the NG, while it declined after DO treatment; this showed that DO greatly attenuated inflammation, steatosis, and swelling of liver tissue caused by long-term dyslipidemia (Figure 10(c)).
Oil-red O staining showed the presence of lipid cumulation in both MG and DO groups compared with NG, but the lipid cumulation in DO group was less than that in MG group (Figures 10(b) and 10(d)).

Discussion
Hyperlipidemia is global threat to public health, contributing to significant annual mortality and enormous health care costs. DO is a tonic herb described in the Chinese Compendium of Materia Medica and has a longstanding history of use as a health food for the folk treatment of diseases related to yin-deficiency for decades, and modern pharmacological studies have confirmed that DO can lower blood lipids in animal models [16,17].
Network pharmacology approach is a useful approach to research the bioactive compounds and mechanisms of TCM in treating hyperlipidemia. is approach combines insights about the drugs, target proteins, and diseases to form drugtarget-disease networks, which are similar to the TCM principles of multi-component, multi-pathway, and multitarget synergy [40]. is research explored the hidden active compounds and useful mechanisms of DO in treating hyperlipidemia through network pharmacological exploration and experimental verification, aiming to provide theoretical evidence for developing DO as an adjuvant therapy for hyperlipidemia.
In this research, we obtained 11 bioactive compounds in DO from various bibliographical databases and predicted 89 potential targets, through the network (C-T, C-T-P, PPI) and KEGG pathway analyses, and specifically found that naringenin (MOL004328), isorhamnetin (MOL000354), and taxifolin (MOL004576) might be the main compounds in DO that are responsible for its effects on hyperlipidemia. ese compounds may act by modulating the above targets (including AKT1, TNF, PPARG, ADIPOQ, and APOB). e pathway exploration in our study suggests that DO may effect multiple signaling pathways related to the pathogenesis of hyperlipidemia, including the PPAR signaling pathway, insulin resistance, AMPK signaling pathway, nonalcoholic fatty liver disease (NAFLD), and thyroid hormone signaling pathways simultaneously. e GO enrichment analysis of targets revealed that the ingredients of DO may have a synergistic effect on the treatment of hyperlipidemia, mainly by regulating cholesterol homeostasis, affecting cholesterol metabolic processes, and aiding in the clearance of low-density lipoprotein particles. Meanwhile, in the HFDA-induced hyperlipidemia rat model, DO could significantly decrease the level of TC, TG, LDL-c, and ALT, increase HDL-c in serum as well, and ease liver damage and lipid cumulation.

ADIPOQ Q15848 Adiponectin
Important adipokine involved in the control of fat metabolism and insulin sensitivity, with direct antidiabetic, antiatherogenic, and anti-inflammatory activities.

APOB P04114
Apolipoprotein B-100 APOB is a major protein constituent of chylomicrons, LDL and VLDL.APOB functions as a recognition signal for the cellular binding and internalization of LDL particles by the apoB/E receptor.

NOS3
P29474 Nitric oxide synthase, endothelial Produces nitric oxide (NO) which is implicated in vascular smooth muscle relaxation through a cGMP-mediated signal transduction pathway.  Figure 4(a), the red node represents the MCODE 1, the green node represents the MCODE 2 and the yellow node represents the MCODE 3. KEGG enrichment analysis of cluster 1 (MCODE 1) was made. Top 10 KEGG enrichment pathways were framed in a bubble plot on basis of the P value (Figure 4(b)). Intersection targeted genes associated with hyperlipidemia and the DO active compounds associated as bits were adopted to fish corresponding functions from DAVID, import target genes into DAVID database for GO analysis biological process. Y-axis stood for greatly enhanced biological process categories associated with target genes, and X-axis referred to the log10 (P value), e size of the dot means the number of target genes in the pathway, and the color of the dot stands for various FDR scopes.

Active Compounds of DO against Hyperlipidemia.
Several of the active compounds predicted to be active by the analyses in this study have actually been shown to be effective in reducing serum TC and lipid deposition. For example, naringenin is a regulator for cholesterol efflux that enhances lipoprotein profiles and protects against cardiovascular disease, and that regulation is mediated by the ATF6 branch of the ER stress and PI3K/ AKT pathway [41]. Importantly, recent studies support the role for naringenin in the treatment of dyslipidemia, hepatic steatosis, obesity, and atherosclerosis [42]. Recent research has indicated that isorhamnetin reduces serum TC in rats fed with a cholesterol-enriched diet. Meanwhile, serum TC and LDL-C of mice fed with a high fat (HF) diet supplemented with isorhamnetin were significantly lower than those of mice fed with HF diet alone [43]; this suggests that isorhamnetin can improve lipid metabolism disorders. In vitro experiments have demonstrated that taxifolin inhibits cholesterol synthesis in a dose-and time-dependent manner. Mechanistic studies have suggested that taxifolin inhibits the activity of HMG-CoA. Further, cellular cholesterol esterification and synthesis of triacylglycerol and phospholipids were also significantly suppressed in the presence of taxifolin [44]. Meanwhile, taxifolin has obvious anti-oxidant reducing ability, as well as radical scavenging and metal-chelating activity [45], which prevent lipid peroxidation and thus protect the cardiovascular system. In a word, these studies support the efficacy and diversity of DO in treating hyperlipidemia and the diversity of active ingredients.

Potential Targets of DO as a Treatment for Hyperlipidemia.
After exploring the PPI network of DO for hyperlipidemia, the key targets of DO for hyperlipidemia included AKT1, TNF, PPARG, ADIPOQ, and APOB. ese targets are primarily involved in regulating cholesterol homeostasis, cholesterol metabolic processes, and clearance of low-density lipoprotein particles.
AKT is a serine/threonine protein kinase that has been implicated in numerous cellular processes [46]. AKT1 has been linked to cholesterol-sensitive signaling mechanisms [47], and research has suggested that Akt acts on its downstream target, mTORC1 (important for autophagy), to negatively regulate cholesterol efflux to apoA-1 and thus improve circulating cholesterol levels [48]. Juan Zhong et al. [49] found that activating the AMPK/Akt/mTOR signaling pathway can ameliorate hyperlipidemia and liver steatosis. In addition, the PI3K/Akt/mTOR signaling pathway controls lipid metabolism by regulating fatty acid synthesis and the transcription factor FoxO1 [50].  Intersection targeted genes associated with hyperlipidemia and the DO active compounds associated as bits were adopted to fish corresponding functions from DAVID, import target genes into DAVID database for KEGG pathway analysis. Y-axis referred to greatly improved biological process categories associated with target genes, and X-axis stood for the log10 (P value), e size of the dot means the number of target genes in the pathway, and the color of the dot shows the different FDR scope.
As an inflammatory element, TNF is generated by activating monocytes or macrophages. While oxidizing lowdensity lipoprotein (ox-LDL), TNF can grow the binding of ox-LDL to endothelial cells and further improve the expression of ox-LDL receptor [51]. Existing studies have reported that TNF-α inhibits cholesterol efflux by causing overexpression of micro-RNA-101and inhibiting the expression of ATP-binding cassette transporter A1 (ABCA1) [52].
PPARG is a significant regulator of lipid homeostasis. Activating or inhibiting PPARG expression causes changes in the activity of proadipogenic and antiadipogenic regulators. Lipid mobilization is controlled by these processes to adipocytes by boosting adipogenesis and controlling the expression of adipocyte-secreted proteins and adipocytokines including leptin and adiponectin, decreasing adipotoxicity [53]. : Compounds-targets-pathways for DO against hyperlipidemia network analysis. By assembling the core pathways acquired by analyzing C-T-P network (the green node refers to the compound, the red stands for the pathway, and the blue means the target), naringenin (MOL004328, degree � 37), isorhamnetin (MOL000354, degree � 37) and taxifolin (MOL004576, degree � 12) have a higher degree, indicating more mutual effect with targets and signaling pathway might be the core active compounds on anti-hyperlipidemia. By analyzing C-T-P network, we picked out 5 important signaling pathways that were significantly associated with DO treatment of hyperlipidemia. e 5 of chosen pathways including insulin resistance (degree � 8), Nonalcoholic fatty liver disease (degree � 8), Pathways in cancer (degree � 8), AMPK signaling pathway (degree � 7) and thyroid hormone signaling pathway (degree � 7).
Adiponectin (also known as ADIPOQ) is a unique adipocytokine that includes growing insulin sensitivity, boosting fatty acid oxidation, stopping inflammatory reactions, and inducing endothelium-dependent, nitric oxidemediated vasodilation [54]. Research shows that adiponectin promotes cholesterol efflux through interactions with the transmembrane receptors AdipoR1 and AdipoR2, suggesting that adiponectin may be an effective marker for atherosclerotic disease [55].
Apolipoprotein B (APOB) is a main structural protein in very low-density lipoprotein, intermediate-density lipoprotein, LDL, and lipoprotein (a) [56], which can remove the residue of lipid metabolism through low-density lipoprotein receptor (LDLR) mediated endocytosis on the surface of liver cells [57].  In summary, AKT1, TNF, PPARG, ADIPOQ, and APOB may be targets for the action of DO in the treatment of hyperlipidemia.

Molecular Mechanism of DO as a Treatment for
Hyperlipidemia. Based on the KEGG enrichment and C-T-P network analysis, DO has been hypothesized to influence several key pathways, which are important in antihyperlipidemia. ose signaling pathways include the PPAR signaling pathway, insulin resistance, the AMPK signaling pathway, and nonalcoholic fatty liver disease (NAFLD). e PPAR signaling is important in many diseases such as obesity, diabetes, and atherosclerosis. PPAR-α and PPARc mainly regulate lipid metabolism, insulin sensitivity, bile acid (BA), and glucose homeostasis [58]. Similarly, PPARβ/δ regulates lipid metabolism, glucose homeostasis, antiinflammatory effects, and fatty acid oxidation, which are keys areas for the action of drugs for hyperlipidemia. Experimental data have suggested that naringenin regulates the activity of nuclear receptors PPAR (α, c), resulting in decreased production of cholesterol and bile acid [59].
Insulin signaling is connected to Type 2 diabetes and related diseases like obesity, hyperlipidemia, and atherosclerosis that are caused by insulin resistance [60]. Published data have suggested that DO prevents insulin resistance in rats with diabetes mellitus (DM) [61], suggesting that DO may be effective as a treatment for hyperlipidemia through attenuating insulin resistance.
AMPK activation promotes the activation of signaling pathways related to fatty acid oxidation and inhibits anabolic ATP-consuming processes such as gluconeogenesis and synthesis of lipids and proteins [62]. AMPK has attracted widespread attention as a potential therapeutic target for metabolic diseases (including hyperlipidemia) [63]. Research has indicated that isorhamnetin (a bioactive compound contained in DO) affects AMPK activation during differentiation of 3T3-L1 adipocytes and reduces the accumulation of intracellular lipids and triglycerides, as well as decreasing glycerol-3-phosphate dehydrogenase (GPDH) activity [64]. e development of NAFLD is tightly tied to other clinical developments such as obesity, dyslipidemia, diabetes, and metabolic syndrome [65], associated with NAFLD extensively, such as PPAR, AMPK, PI3K-Akt, ER stress, TNF-α, and FAAs, and also associated with NAFLD progression. NAFLD also presents with atherosclerotic dyslipidemia, postprandial lipemia, and HDL dysfunction [66].

Experimental Validation of DO as a Treatment for Hyperlipidemia.
rough preliminary network pharmacology analysis, we used PPI and KEGG analysis to get potential targets and key signaling pathways of DO against hyperlipidemia. e results indicated that NAFLD signaling pathway and other NAFLD-related signaling pathways are involved in the treatment of hyperlipidemia by DO. en, we established a HFDA-induced hyperlipidemia rat model and supplied with DO to verify its antihyperlipidemic effect. e outcomes displayed that DO could significantly reduce TC, TG, and LDL-C, grow HDL-C in hyperlipidemia model rats, and improve the damage caused by liver and lipid metabolism disorder.
Dyslipidemia often co-existed with NAFLD/NASH, and the relationship between dyslipidemia and NAFLD is bidirectional. In this study, liver pathological section implied that DO can ameliorate hepatocyte ballooning, steatosis, and inflammation to stop NAFLD caused by lipid metabolism disorders. e lipid droplets in liver were stained with Oil red O, and it was found that DO reduced the lipid droplets, which showed that DO better influences serum TC and liver function; it is further verified that DO may treat hyperlipidemia by affecting NAFLD-related signaling pathways.

Conclusion
is research made network pharmacology and validation experiments to reveal the biochemistry foundation and underlying mechanisms of DO as a treatment for hyperlipidemia. ere are multiple active compounds in DO that act on multiple distinct targets through a variety of biological processes and pathways to treat hyperlipidemia; meanwhile, validation experiments indicated that DO may treat hyperlipidemia by affecting NAFLD-related signaling pathways. However, these conclusions are limited by their purely bioinformatic design and preliminary experimental verification. But these findings suggest that DO, a botanical treatment from TCM, may have a true pharmacological basis for a clinical effect. As such, we hope that these findings may motivate future network pharmacologybased investigations of other Chinese herbs that may be promising treatments for against hyperlipidemia and related disorders.

BP:
Biological processes CC: Cellular components C-T: Compound-target C-T-P: Compound-targets-pathway DL: Drug-likeness DO: Dendrobium officinale GO: Gene ontology H&E: Hematoxylin-eosin HFDA: High-sucrose-fat diet and alcohol KEGG: Kyoto Encyclopedia of Genes and Genomes MF: Molecular functions OB: Oral bioavailability PPI: Protein-protein interaction TCM: Traditional Chinese Medicine TCMSP: Traditional Chinese Medicine Systems Pharmacology.
Data Availability e datasets used and/or analyzed during the current study are available from Dr. Lin-Zi Li upon reasonable request.

Conflicts of Interest
e authors declare no conflicts of interest in association with this manuscript.