Abstract
Hyperlipidemia is a metabolic disorder characterized by abnormal lipid
metabolism, resulting in lipid accumulation in the plasma. According to
reports, medicinal and edible plants can reduce the risk of metabolic
diseases such as hyperlipidemia. This study investigates the effects
and mechanisms of Astragalus membranaceus extract (AME), Hippophae
rhamnoides L. extract (HRE), and Taraxacum mongolicum Hand. Mazz
extract (TME) on hyperlipidemia. Active compounds and potential gene
targets of AME, HRE, and TME were screened using LC-MS and TCMSP
databases, and hyperlipidemia targets were detected from the OMIM and
DisGeNet databases. A drug-target pathway disease network was
constructed through protein interactions, GO enrichment, and KEGG
pathway analysis. Finally, the lipid-lowering effects of three extracts
were validated through in vitro HepG2 cell and in vivo animal
experiments. The results show that LC-MS and network pharmacology
methodologies identified 41 compounds and 140 targets. KEGG analysis
indicated that the PI3K-Akt and MAPK signaling pathways significantly
treat hyperlipidemia with AHT. In vitro experiments have shown that AHT
is composed of a ratio of AME:HRE:TME = 3:1:2. HepG2 cell and animal
experiments revealed that AHT exhibits strong lipid-lowering and
antioxidant properties, significantly regulating the levels of total
cholesterol (TC), triglycerides (TG), high-density lipoprotein
cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C),
superoxide dismutase (SOD), and total antioxidant capacity (T-AOC). It
is worth noting that AHT can effectively downregulate the protein
expression levels of p-AKT/AKT and p-PI3K/PI3K and upregulate the
protein expression levels of p-AMPK/AMPK and SIRT1, verifying the
results predicted by network pharmacology. This study presents a novel
approach to utilizing these natural plant extracts as safe and
effective treatments for hyperlipidemia.
Keywords: hyperlipidemia, network pharmacology, plant extracts, HepG2,
antioxidant
1. Introduction
Hyperlipidemia (HLP) is a metabolic disorder caused by abnormal lipid
metabolism or transport, resulting in one or more lipids in the plasma
being higher than the normal range [[34]1]. Its characteristics are an
increase in total cholesterol (TC), triglycerides (TGs), and
low-density lipoprotein cholesterol (LDL-C) levels or a decrease in
circulating high-density lipoprotein cholesterol (HDL-C) levels
[[35]2]. Hyperlipidemia is recognized as a significant risk factor for
nonalcoholic fatty liver disease, atherosclerosis, diabetes, and
various other metabolic disorders [[36]3]. Currently, statins are the
primary treatment for hyperlipidemia. While they act quickly, long-term
use may result in side effects, including a single treatment mechanism,
liver and kidney complications, and muscle-related issues [[37]4].
Consequently, developing a treatment plan with good efficacy and
minimal side effects has become a research hotspot in recent years.
Medicinal and edible plants contain a variety of natural active
ingredients. In comparison to traditional chemically synthesized drugs,
traditional Chinese medicine, which boasts extensive experience in
pharmacotherapy, is noted for its reduced adverse reactions and more
pronounced long-term effects [[38]5,[39]6]. Previous studies have
indicated that medicinal and edible plants mainly treat hyperlipidemia
by improving blood lipids, antioxidation, and regulating gut microbiota
[[40]7,[41]8,[42]9]. Astragalus membranaceus, Hippophae rhamnoides L.,
and Taraxacum mongolicum Hand. Mazz are typical medicinal and edible
plants rich in active ingredients such as flavonoids, saponins,
polysaccharides, and triterpenes. They have the characteristics of
lowering blood lipids, lowering blood sugar, regulating immunity,
antioxidation, and being anti-inflammatory. Multiple studies have
demonstrated that Astragalus membranaceus, Hippophae rhamnoides L., and
Taraxacum mongolicum Hand. Mazz can inhibit cholesterol synthesis and
regulate lipid metabolism [[43]10,[44]11,[45]12]. They demonstrate
significant potential in the treatment of hyperlipidemia and metabolic
disorders. In recent years, due to the advancement of databases related
to drugs and diseases, research in the field of bioinformatics has
become increasingly popular. Network pharmacology has become widely
used in studying medicinal and edible plants, which can construct
networks and interactions related to drugs, targets, diseases, and
pathways. Network pharmacology can conduct in-depth analysis and
explore the pharmacological mechanisms of various active ingredients
and targets in medicinal and edible plants [[46]13]. Therefore, network
pharmacology can be used to further elucidate the chemical composition
and mechanism of the three extracts’ lipid-lowering effects.
This study initially identified the active ingredients of three
extracts through liquid chromatography–mass spectrometry (LC-MS).
Subsequently, network pharmacology was employed to predict the
lipid-lowering mechanisms associated with these active ingredients,
utilizing databases to construct an interaction network relevant to the
three extracts’ treatment of hyperlipidemia. The potential
lipid-lowering mechanisms of the three extracts were validated through
assays conducted on HepG2 cells. Finally, a hyperlipidemia mouse model
was established to assess the lipid-lowering efficacy of the three
extracts. This research offers a novel strategy for the prevention and
treatment of hyperlipidemia.
2. Materials and Methods
2.1. Chemicals and Reagents
Astragalus membranaceus extract (AME), Hippophae rhamnoides L. extract
(HRE), and Taraxacum mongolicum Hand. Mazz extract (TME) were purchased
from Xi’an Ruihe Biotechnology Co., Ltd. (Xi’an, China). The MTT cell
proliferation and cytotoxicity assay kit and BCA protein quantification
kit were purchased from Beyotime Biotechnology Co., Ltd. (Shanghai,
China), while the total cholesterol (TC), triglycerides (TGs),
low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein
cholesterol (HDL-C), aspartate aminotransferase (AST), alanine
aminotransferase (ALT), superoxide dismutase (SOD), and total
antioxidant capacity (T-AOC) assay kits were purchased from Nanjing
Jiancheng Bioengineering Research Institute (Nanjing, China). Oleic
acid and simvastatin were purchased from MACKUN Biotech (Shanghai,
China).
2.2. LC-MS Analysis Conditions
AHT powder was measured using liquid chromatography (Vanquish, Thermo,
Waltham, MA, USA) and mass spectrometry (Orbitrap Exploris 120, Thermo,
Waltham, MA, USA). Firstly, AHT powder (10 mg) was dissolved in 90%
methanol (3 mL) and centrifuged (10,000× g, 10 min, 4 °C) to obtain the
supernatant. Then, 150 μL of supernatant was filtered through a 0.22 μm
filter membrane into an LC vial for LC-MS analysis.
The liquid chromatography conditions were as follows: The LC analysis
was performed on a Vanquish UHPLC System (Thermo Fisher Scientific,
Waltham, MA, USA). A 2 μL aliquot was injected into a 2.1 × 100 mm, 1.8
µm column (Waters, Milford, MA, USA) with a flow rate of 0.3 mL/min.
For LC-ESI (+)-MS analysis, the mobile phases consisted of (A) 0.1%
formic acid in acetonitrile (v/v) and (B) 0.1% formic acid in water
(v/v) under the following gradient: 0~1 min, 10% A; 1~5 min, 10~98% A;
5~6.5 min, 98% A; 6.5~6.6 min, 98~10% A; 6.6~8 min, 10% A. For LC-ESI
(−)-MS analysis, the mobile phases consisted of (C) acetonitrile and
(D) ammonium formate (5 mM) under the following gradient: 0~1 min, 10%
C; 1~5 min, 10~98% C; 5~6.5 min, 98% C; 6.5~6.6 min, 98~10% C; 6.6~8
min, 10% C.
Mass spectrometric detection of metabolites was performed on Orbitrap
Exploris 120 (Thermo Fisher Scientific, Waltham, MA, USA) with an ESI
ion source. Simultaneous MS1 and MS/MS acquisition were used. The
parameters were as follows: sheath gas pressure, 40 arb; aux gas flow,
10 arb; spray voltage, 3.50 kV and −2.50 kV for ESI (+) and ESI (−),
respectively; capillary temperature, 325 °C; MS1 range, m/z 100–1000.
A quantitative list of substances was obtained using the R XCMS
software (V3.12.0) package for peak detection, peak filtering, and peak
alignment processing. Normalizing the total peak area was used to
achieve data correction and eliminate systematic errors. The substance
identification process uses the Human Metabolome Database (HMDB),
LipidMaps, McCloud, and KEGG databases for retrieval and comparison.
The molecular weight of metabolites was determined. The molecular
formula was predicted based on the mass-to-charge ratio of parent ions
in primary mass spectrometry and information on added ions, and then
matched with a database. Meanwhile, fragment ions from secondary
spectra were utilized for secondary qualitative identification of
metabolites [[47]14,[48]15].
2.3. AHT Chemical Composition Screening and Target Prediction
From the Traditional Chinese Medicine System Pharmacology Analysis
Platform (TCMSP), ([49]http://lsp.nwu.edu.cn/tcmsp.php, accessed on 20
July 2024). The database used Huangqi, Shaji, and Pugongying as
keywords, and the filtering criteria were set to OB ≥ 30% and DL ≥
0.18. Through the UniProt database ([50]https://sparql.uniprot.org/,
accessed on 20 July 2024), all targets are standardized
[[51]16,[52]17].
2.4. Prediction of Targets for Hyperlipidemia
Potential targets related to hyperlipidemia have been identified from
Genecards ([53]https://www.Genecards.org/, accessed on 22 July 2024),
the DisGeNet database ([54]https://www.disgenet.org/, accessed on 18
July 2024), and Online Mendelian Inheritance in Man (OMIM,
[55]https://www.genecards.org/, accessed on 18 July 2024). After
eliminating duplicate targets, potential targets related to HLP were
obtained [[56]18,[57]19].
2.5. Construction of Drug Target Network and Pathways
Firstly, in order to obtain common targets for compounds and diseases,
Venny 2.1.0 was used ([58]https://bioinfogp.cnb.csic.es/tools/venny/,
accessed on 23 July 2024) to perform filtering. Common targets imported
into Cytoscape 3.9.1 software to construct a drug–target disease
network. The interacting targets were mapped to the STRING database
([59]https://string-db.org/, accessed on 23 July 2024). After
visualization using Cytoscape 3.9.1 software, the protein–protein
interaction (PPI) network was obtained and filtered based on
intermediate centrality (BC), compact centrality (CC), degree
centrality (DC), eigenvector centrality (EC), local average
connectivity (LAC), and network centrality (NC), with values greater
than or equal to twice the median. After deleting duplicate targets,
the core targets were merged to obtain 140 common targets, which were
imported into the DAVID database ([60]https://david.ncifcrf.gov/,
accessed on 24 July 2024). They were used for gene ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGGs) analysis to explore the
biological processes and signaling pathways involved in treating
hyperlipidemia in AHT [[61]20,[62]21,[63]22].
2.6. Pancreatic Lipase Inhibition Test and Combined Index Analysis
Ten mg of pancreatic lipase (PL) was accurately weighed and dissolved
in 10 mL Tris HCl buffer, resulting in a 1.0 mg/mL PL solution. A
volume of 8.44 μL of NPB liquid was accurately transferred, and 4 mL of
acetonitrile was added to obtain a 12 mmol/L NPB solution, which was
used as a substrate. The sample, PL solution, and 4-nitrophenylbutyrate
were added to a 96-well plate and reacted at 37 °C for 2 h. The
absorbance was measured at 405 nm [[64]23,[65]24]. The formula for
calculating the pancreatic lipase inhibition rate is:
[MATH:
Inhibition rate <
mo>(%)=(A1−A2)−
(A3−A4)A1−A2×100 :MATH]
A[1] is the absorbance value of the blank group; A[2] is the absorbance
value of the blank control group; A[3] is the absorbance value of the
sample group; and A[4] is the absorbance value of the sample control
group.
The Chou Talalay method, also known as the combination index method
(CI), is widely used for quantitative evaluation of the interactions
between multiple drug formulations [[66]25]. Using the Chou and Talalay
formulas, calculate the effects of each extract alone and in
combination.
[MATH: fafu=(DDm)m :MATH]
D: Dose, D[m]: the dose that produces moderate effects, f[a]: the
practical portion of the dose, m: the coefficient of the dose–response
curve.
The data analysis and calculation formula for the combination drug
index (CI) is as follows:
[MATH: CI=D1<
msub>DX1+D2<
msub>DX2 :MATH]
D[1] and D[2] are the effective concentrations when the drug
combination inhibition rate is 50%, while D[X1] and D[X2] are effective
when the drug is used alone with an inhibition rate of 50%. The CI
values of the complex in each group were calculated using the
combination index calculation formula and CompuSyn program to analyze
the effects of the three extracts combined [[67]26,[68]27].
2.7. Cell Culture and MTT Assay
HepG2 cells were derived from the cell bank of the Chinese Academy of
Sciences (Shanghai, China); DMEM was used (adding 10% fetal bovine
serum and 1% penicillin and streptomycin); and the cells were cultured
in a humidified incubator at 37 ℃ and 5% CO[2], and the medium was
changed every two to three days. When the cell density reached over
80%, trypsin was used for subculture [[69]28]. HepG2 cells were
detected for cell proliferation using an MTT assay. The cells were
seeded at a density of 5 × 10^3 cells/well into a 96-well plate for 24
h and then replaced with standard culture medium containing different
concentrations (0, 10, 50, 100, 200, and 500 μM) of HRE, AME, TME, and
AHT for 24 h. Then, 10 μL of MTT solution were added and the cells were
incubated for 4 h. Afterward, 150 μL of dimethyl sulfoxide was added to
each well and the absorbance value was measured at 490 nm using a
microplate reader (Tecan Infinite 200 Pro, Shanghai, China)
[[70]17,[71]29].
2.8. Establishment of High-Fat Model and Administration Regimen
Cells were seeded at a density of 5 × 10^5 cells/well in a 12-well
culture plate and cultured for 24 h. They were then exposed to oleic
acid (0.5 mM) for 24 h to induce a high-fat model. The treatment group
was cultured with 100 μM HRE, 200 μM AME, 100 μM TME, and AHT (AME:
HRE: TME = 3:1:2) for 24 h.
2.9. Oil Red O Staining
HepG2 cells in each well were washed gently with PBS 3 times and then
rinsed quickly with 60% isopropanol for 20 s. The cells were stained
with Oil Red O in the dark at room temperature (25 °C) for 20 min.
Then, the staining solution was discarded and the cells were rinsed 5
times with distilled water, each for 1 min per well. In addition, 100%
isopropanol (1 mL) was added to each well and stirred at room
temperature (25 °C) for 10 min [[72]30].
2.10. Determination of Lipid-Lowering Levels in HepG2 Cells by AHT
Cells were seeded at a density of 5 × 10^5 cells/well in a 12-well
culture plate and cultured for 24 h. Except for the NC group, which was
added to the standard culture medium, all other groups were added to
the culture medium containing OA. The treatment group was added with
AME at a concentration of 200 μM, HRE, and TME at a concentration of
100 μM, and AHT for 24 h to evaluate their lipid-lowering effects on
cells. The cell culture medium was removed, and the cells were washed
three times with PBS before being lysed. The levels of TC, TG, HDL-C,
and LDL-C were measured according to the instructions of the reagent
kit. The protein concentrations of each group were measured using the
BCA protein quantification kit.
2.11. Western Blot Analysis
Cells were seeded at a density of 1 × 10^6 cells/well in a 6-well
culture plate and cultured for 24 h. All other groups were added to the
standard culture medium except for the NC group, which was added to the
culture medium containing OA. The treatment group was added with 100 μM
HRE, 200 μM AME, and 100 μM TME and AHT for 24 h, respectively, and
Western blotting was performed for testing. The Western blotting method
is described below.
Cells were lysed in RIPA buffer (Beyotime, Shanghai, China) containing
1% PMSF for 10 min, then centrifuged at 4 °C and 12,000× g for 10 min.
Protein quantification was performed using the BCA protein assay kit
(Beyotime, Shanghai, China). The protein sample was boiled at 100 °C
for 10 min and then separated by 10% SDS-PAGE. It was transferred to a
PVDF Western blotting membrane (Biotopped, Beijing, China) and then
blocked with 5% skim milk powder (Biosharp, Hefei, China) at room
temperature (RT) for 2 h. The membrane was washed three times with TBST
buffer (Biotopped, Beijing, China). The primary antibody was incubated
overnight at 4 °C, and the membrane was washed three times with TBST
and then incubated with the secondary antibody goat anti-rabbit
(ABclonal, AS014, WB: 1:2000). An ECL chemiluminescence detection kit
(ABclonal, Wuhan, China) was used for protein detection, a gel imager
(Tanon 5200, Shanghai, China) was used for image capture, and image J
software (Version 1.46r) was used to analyze the gray value. β—actin
rabbit (ABclonal, AC038, WB: 1:20,000) was used as an endogenous
control. The selected primary antibodies were: PI3 kinase p85 alpha
rabbit pAb (ABclonal, A11526, WB: 1:500), AKT1 rabbit mAb (ABclonal,
A17909, WB: 1:1000), phospho-AKT1-S129 rabbit pAb (ABclonal, AP1272,
WB: 1:200), phospho-PI3K (ABclonal, AP0854, WB: 1:500), AMPKα1 rabbit
pAb (ABclonal, AP1229, WB: 1:2000), phospho-AMPKα1 rabbit pAb
(ABclonal, AP0871, WB: 1:2000), SIRT1 rabbit pAb (ABclonal, A11267, WB:
1:2000).
2.12. Animal Experiments
Eight-week-old male KM mice weighing 32 ± 2 g were purchased from
Qingdao Petford White Mouse Breeding Professional Cooperative (Qingdao,
China). The certificate number is SCXK (Lu) 2019-00003. All mice were
free to eat and drink, with a 12 h/12 h light–dark cycle. All animal
procedures were conducted by the National Research Council’s
“Guidelines for the Care and Use of Experimental Animals” and were
approved by the Ethics Committee of Northeast Forestry University
(NEFU2024-011) [[73]31].
After adaptive feeding for 1 w, KM mice were randomly divided into 6
groups based on body weight, namely normal control (NC), model control
(MC), positive control (PC), low-dose administration (LD), medium-dose
administration (MD), and high-dose administration (HD), with 8 mice in
each group. NC was given standard feed, while the other groups were
given high-fat feed for 8 w. NC and MC were given saline by gavage, the
PC group was given 10 mg/kg simvastatin by gavage, and the LD group was
given 41.25 mg/kg AME, 13.75 mg/kg HRE, and 27.5 mg/kg TME by gavage.
MD was orally administered 82.5 mg/kg AME, 27.5 mg/kg HRE, and 55 mg/kg
TME, while HD was orally administered 165 mg/kg AME, 110 mg/kg HRE, and
55 mg/kg TME for 8 w. Food intake was measured daily, and the weight of
each mouse was measured weekly. After the experiment, blood was
collected from the eyeballs of anesthetized mice.
2.13. Determination of Serum Biochemical Indicators
Mouse blood was centrifuged at 3000× g for 15 min to obtain mouse
serum. The levels of TC, TG, LDL-C, HDL-C, AST, ALT, SOD, and T-AOC
were measured in serum according to the instructions of the kit
[[74]32].
2.14. Statistical Analysis
All experiments were conducted in at least three parallel experiments,
and the results were reported as mean ± standard deviation. Statistical
analysis was conducted using GraphPad Prism 9.5, and ****, ***, **, *
and ns represent p < 0.0001, p < 0.001, p < 0.01, p < 0.05, and p >
0.05, respectively.
3. Results
3.1. Analysis of AHT Active Ingredients
Firstly, the active ingredients of AHT were identified, and a total of
1539 metabolites were detected by LC-MS. These metabolites include a
series of compounds such as flavonoids, polyphenols, terpenes, and
alkaloids. Then, 1539 compounds were identified and analyzed through
the TCMSP database. Setting oral bioavailability (OB) ≥ 30% and
drug-like (DL) ≥ 0.18, 41 compounds were obtained, as shown in
[75]Table 1, including bioactive ingredients such as luteolin,
quercetin, isorhamnetin, and kaempferol. Next, a qualitative analysis
of AHT was conducted, summarizing the retention time, m/z, molecular
formula, and error (ppm) of 41 compounds ([76]Table 2). In addition,
the total ion chromatogram (TIC) of AHT in positive and negative ion
modes was labeled based on the retention time of the compounds
([77]Figure 1).
Table 1.
Active ingredients of AME, HRE, and TME.
No. Name Molecule ID Molecule Name OB (%) DL
1 Luteolin MOL000006 Luteolin 36.16 0.25
2 Quercetin MOL000098 Quercetin 46.43 0.28
3 Isorhamnetin MOL000354 Isorhamnetin 49.6 0.31
4 Kaempferol MOL000422 Kaempferol 41.88 0.24
5 Phaseollidin MOL000457 Phaseollidin 52.04 0.53
6 Cholesterol MOL000953 CLR 37.87 0.68
7 Ellagic acid MOL001002 Ellagic acid 43.06 0.43
8 Pelargonidin MOL001004 Pelargonidin 37.99 0.21
9 Dihydrochelerythrine MOL001461 Dihydrochelerythrine 32.73 0.81
10 Dihydrosanguinarine MOL001463 Dihydrosanguinarine 59.31 0.86
11 Sanguinarine MOL001474 Sanguinarine 37.81 0.86
12 Chelerythrine MOL001478 Toddaline 25.99 0.81
13 Scopolamine MOL001554 Scopolamine 67.97 0.27
14 (+)-Sesamin MOL001558 Sesamin 56.55 0.83
15 Acacetin MOL001689 Acacetin 34.97 0.24
16 Diphyllin MOL001699 Diphyllin 36.23 0.75
17 Podofilox MOL001714 Podophyllotoxin 59.94 0.86
18 Linarin MOL001790 Linarin 39.84 0.71
19 Scopolin MOL002218 Scopolin 56.45 0.39
20 Hesperetin MOL002341 Hesperetin 70.31 0.27
21 Medicarpin MOL002565 Medicarpin 49.22 0.34
22 Baicalein MOL002714 Baicalein 33.52 0.21
23 Melilotoside MOL004101 Melilotoside 36.85 0.26
24 Corydaline MOL004195 CORYDALINE 65.84 0.68
25 Kaempferide MOL004564 Kaempferid 73.41 0.27
26 Glabranin MOL004910 Glabranin 52.9 0.31
27 Artemetin MOL005229 Artemetin 49.55 0.48
28 Arachidonic acid MOL005320 Arachidonate 45.57 0.2
29 Citromitin MOL005815 Citromitin 86.9 0.51
30 Papaverine MOL006980 Papaverine 64.04 0.38
31 Codeine MOL006982 Codeine 45.48 0.56
32 Cirsimaritin MOL007274 Skrofulein 30.35 0.3
33 Artemisinin MOL007424 Artemisinin 49.88 0.31
34 Corynoline MOL008636 Corynoline 30.53 0.85
35 Camptothecin MOL009830 EHD 61.04 0.81
36 Leucocyanidin MOL010489 Resivit 30.84 0.27
37 Estrone MOL010921 Estrone 53.56 0.32
38 Lobelanine MOL012208 Lobelanine 54.13 0.32
39 Uridine 5’-monophosphate MOL012820 5’-Uridylic acid 40.25 0.2
40 Fustin MOL013296 Fustin 50.91 0.24
41 Garbanzol MOL013305 Garbanzol 83.67 0.21
[78]Open in a new tab
Table 2.
The compound composition in AHT determined by LC-MS.
Peak No. Proposed Compound RT/s Precursor m/z Error (ppm) Formula
1 Luteolin 290 285.0761 2.606 C[15]H[10]O[6]
2 Quercetin 287 303.0498 0.420 C[15]H[10]O[7]
3 Isorhamnetin 303.7 315.0515 1.512 C[16]H[12]O[7]
4 Kaempferol 282 285.0391 4.770 C[15]H[10]O[6]
5 Phaseollidin 224 325.1379 17.003 C[20]H[20]O[4]
6 CLR 412.7 369.3588 19.607 C[27]H[46]O
7 Ellagic acid 116.6 324.9999 13.574 C[14]H[6]O[8]
8 Pelargonidin 57.5 543.1263 4.175 C[15]H[11]O[5]
9 Dihydrochelerythrine 63.8 332.1285 1.169 C[21]H[19]NO[4]
10 Dihydrosanguinarine 48.1 665.1907 3.352 C[20]H[15]NO[4]
11 Sanguinarine 116.9 289.1103 1.990 C[20]H[14]NO[4]
12 Toddaline 83.9 332.1315 10.197 C[21]H[18]NO[4]
13 Scopolamine 301.9 285.1163 10.784 C[17]H[21]NO[4]
14 Sesamin 48.5 337.1115 13.218 C[20]H[18]O[6]
15 Acacetin 336.1 283.0606 2.098 C[16]H[12]O[5]
16 Diphyllin 46.8 381.0936 8.594 C[21]H[16]O[7]
17 Podophyllotoxin 58.8 829.2688 1.687 C[22]H[22]O[8]
18 Linarin 240.2 653.184 17.888 C[28]H[32]O[14]
19 Scopolin 81.8 335.0758 4.287 C[16]H[18]O[9]
20 Hesperetin 241.6 301.071 2.518 C[16]H[14]O[6]
21 Medicarpin 240.9 254.0728 11.289 C[16]H[14]O[4]
22 Baicalein 297.6 269.0448 2.767 C[15]H[10]O[5]
23 Melilotoside 81.8 371.0968 4.221 C[15]H[18]O[8]
24 CORYDALINE 226.1 387.2221 14.781 C[22]H[27]NO[4]
25 Kaempferid 302.1 299.0567 1.977 C[16]H[12]O[6]
26 Glabranin 385.4 369.1471 4.907 C[20]H[20]O[4]
27 Artemetin 61.3 345.1367 9.972 C[20]H[20]O[8]
28 Arachidonate 447.9 303.2351 7.121 C[20]H[32]O[2]
29 Citromitin 206.6 405.1339 18.738 C[21]H[24]O[8]
30 Papaverine 66.5 322.127 4.678 C[20]H[21]NO[4]
31 Codeine 219.3 300.1587 2.372 C[18]H[21]NO[3]
32 Skrofulein 252 359.073 11.803 C[17]H[14]O[6]
33 Artemisinin 260.9 343.1382 4.764 C[15]H[22]O[5]
34 Corynoline 254.3 350.1458 3.569 C[21]H[21]NO[5]
35 EHD 260.3 330.2363 11.189 C[20]H[1]6N[2]O[4]
36 Resivit 267.9 288.0443 14.498 C[15]H[14]O[7]
37 Estrone 292.4 269.079 10.881 C[18]H[22]O[2]
38 Lobelanine 276 336.1978 5.963 C[22]H[25]NO[2]
39 5’-Uridylic acid 41.1 323.0274 3.682 C[9]H[13]N[2]O[9]P
40 Fustin 326.2 333.0473 15.279 C[15]H[12]O[6]
41 Garbanzol 252.4 273.0805 17.407 C[15]H[12]O[5]
[79]Open in a new tab
Figure 1.
[80]Figure 1
[81]Open in a new tab
Total ion chromatogram in positive and negative ion modes. Ionic
chromatograms of AME, HRE, and TME in positive-ion mode (A–C). Ionic
chromatograms of AME, HRE, and TME in negative-ion mode (D–F). The
numbers in [82]Figure 1 represent the substances in [83]Table 2.
3.2. Potential Target Prediction of AHT and HLP
Firstly, the TCMSP database filtering criteria (OB ≥ 30%, DL ≥ 0.18)
was set, a total of 256 potential targets of compounds in AHT were
searched for, and all potential targets with the “Homo sapiens” species
were standardized using the UniProt database to obtain [84]Table S1.
The potential targets related to hyperlipidemia were obtained from the
Genecards database and Drugbank database. After eliminating duplicate
targets, 1571 potential targets related to HLP were obtained ([85]Table
S2).
3.3. Target Network Construction for AHT and HLP
Firstly, the common targets of AHT and HLP were screened using Venny
2.1.0, resulting in 140 common targets ([86]Figure 2A, [87]Table S3).
These targets are potential targets for AHT treatment of HLP. The above
targets were imported into Cytoscape 3.9.1 software to construct a
drug–target disease network. There are 181 nodes and 546 edges in
[88]Figure 2B, and the size of the nodes in the figure is related to
the degree centralities (DCs) value. The 40 compounds of AHT are
represented by red squares; green circles represent common targets; and
blue triangles represent AHT. The results show that quercetin,
kaempferol, luteolin, baicalein, isorhamnetin, acacetin, medicarpin,
phaseollidin, papaverine, and dihydrochelerythrine ranked in the top
ten in terms of DC values, suggesting that they are key compounds.
Figure 2.
[89]Figure 2
[90]Open in a new tab
Network pharmacology analysis of AHT treatment for hyperlipidemia. (A)
Common targets of AHT and HLP. (B) AHT compound target network diagram.
(C) Analysis of protein–protein interaction networks with 140 common
targets. (D) PPI network diagram of common targets. (E) Core target PPI
network diagram (node size and color depth indicate the high or low DC
value).
3.4. Construction of PPI Network
Common targets were imported into the STRING 11.0 database for PPI
network analysis ([91]Figure 2C). There were a total of 139 nodes and
395 edges, with an average node degree of 5.68. Import the cross
targets obtained through STRING database analysis into Cytoscape 3.9.1
software to obtain the PPI network diagram for AHT treatment of HLP. As
shown in [92]Figure 2D, the larger the degree value, the darker the
color and the larger the area of the gene target, indicating that the
target has a more significant influence in the PPI network. In
addition, the target’s BC, CC, DC, EC, LAC, and NC values were obtained
through network topology analysis in Cytoscape 3.9.1 software. By
screening and removing duplicate targets from the first six targets in
each item, 15 key targets were obtained ([93]Figure 2E and [94]Table
3), namely TP53, PPARG, ESR1, TNF, CCL2, AKT1, RELA, MAPK1, IL6, CXCL8,
IL1A, IL4, IL10, IL1B, and IFNG. We speculate that they may be key
targets for treating HPL.
Table 3.
Prediction of key targets for AHT treatment of HPL.
NO. Symbol ID Protein Name Pathways
1 TP53 Tumor protein p53 hsa05417, hsa04010, hsa04151
2 PPARG Peroxisome proliferator-activated receptor gamma hsa05417
3 ESR1 Estrogen receptor hsa05207
4 TNF Tumor necrosis factor hsa05417, hsa04933, hsa04010
5 CCL2 C-C motif chemokine 2 hsa05417, hsa04933, hsa05418
6 AKT1 Potassium channel AKT1 hsa05417, hsa04933, hsa04010, hsa04151
7 RELA Transcription factor p65 hsa05417, hsa04933, hsa04010, hsa04151
8 MAPK1 Mitogen-activated protein kinase 1 hsa05417, hsa04933,
hsa04010, hsa04151
9 IL6 Interleukin-6 hsa05417, hsa04933, hsa04151
10 CXCL8 Interleukin-8 hsa05417, hsa04933
11 IL1A Interleukin-1 alpha hsa04933, hsa04010, hsa05418
12 IL4 Interleukin-4 hsa04151
13 IL10 Interleukin-10 -
14 IL1B Interleukin-1 beta hsa05417, hsa04933, hsa04010
15 IFNG Interferon gamma hsa05418
[95]Open in a new tab
3.5. Enrichment Analysis of GO and KEGG Pathways
To explore the potential therapeutic mechanism of AHT for
hyperlipidemia we imported the overlapping genes of AHT and HLP into
the DAVID database for GO and KEGG pathway enrichment analysis.
Firstly, visual analysis was conducted on biological progress (BP),
cellular components (CC), and molecular functions (MF). the top 10
enriched GO terms were identified separately (p-Value < 0.05) and the
key targets were analyzed ([96]Figure 3A). The results show that in BP,
the targets of AHT were associated with response to lipolysis (GO:
0032496) and cellular response to lipolysis (GO: 0071222). In MF, the
target of AHT was associated with steroid binding (GO: 0005496). In CC,
the targets of AHT were associated with membrane raft (GO: 0045121),
protein-containing complex (GO: 0032991), and transcription regulator
complex (GO: 0005667). The GO enrichment analysis results indicated
that compounds in AHT can exert therapeutic effects on hyperlipidemia
from lipopolysaccharides, inflammatory factors, and protein regulation.
The false discovery rate (FDR) was used as the X-axis; the size of the
dots represented the number of targets in different pathways, and dots
of different colors represented the p-values of different pathways. The
darker the red color, the higher the significance. As shown in
[97]Figure 3B, enrichment analysis was performed on the top 20
signaling pathways for AHT treatment of hyperlipidemia. It was found
that AHT mainly occurs through lipids and atherosclerosis, the AGE–RAGE
signaling pathway in diabetic complications, and the inflammatory
pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway play a
role. [98]Table 4 presents detailed information on the top 10 signaling
pathways and their corresponding targets. Moreover, the target and
signaling pathway were visualized in [99]Figure 3C. The above 10
pathways connected with relevant key targets and compounds to form a
compound–target–signaling pathway network. It included 140 nodes and
657 edges ([100]Figure 3D). A significant correlation was indicated
between key signaling pathways and the main compounds of AHT. The core
compounds included quercetin (MOL000098, edge count = 67), luteolin
(MOL000006, edge count = 31), kaempferol (MOL000422, edge count = 27),
and baicalein (MOL002714, edge count = 18).
Figure 3.
[101]Figure 3
[102]Open in a new tab
Network pharmacology pathway analysis of AHT treatment for HLP. (A) GO
enrichment analysis. (B) The top 20 KEGG enrichment pathways. (C) KEGG
chord diagram, it indicated the top 10 pathways and their corresponding
targets; the different colors of the graphics represent different
signal pathways. (D) Compound–target–pathway–disease network diagram.
Table 4.
Annotation of the top 10 KEGG pathways.
ID Description p-Value Gene ID Count
hsa05200 Pathways in cancer 29.15 GSK3B, CXCL8, PTEN, CASP9, CASP8,
CCND1, MYC, CASP3, AKT1, NCOA1, CHUK, PRKCB, MMP1, MMP2, FOS, MMP9, AR,
IFNG, BIRC5, PPARG, RAF1, TP53, PPARD, PTGS2, HIF1A, EGFR, RELA, RXRB,
MAPK8, RXRA, ERBB2, E2F1, HMOX1, MAPK1, RXRG, TGFB1, NOS2, CDKN2A, EGF,
STAT1, IGF2, ESR1, ESR2, IL2, VEGFA, MAPK10, IL4, IL6, CDK4, BCL2,
MDM2, BAX, NFE2L2 53
hsa05417 Lipid and atherosclerosis 30.27 GSK3B, CXCL8, TNF, CXCL2,
RELA, ICAM1, CASP9, RXRB, PPP3CA, MAPK8, CASP8, CYP2B6, RXRA, CASP3,
CCL2, AKT1, MAPK1, OLR1, RXRG, VCAM1, CHUK, MMP1, NOS3, MMP3, NFATC1,
FOS, MAPK14, SELE, MMP9, MAPK10, IL6, CD40LG, IL1B, CYP1A1, BCL2, BAX,
PPARG, TP53, NFE2L2 39
hsa04933 AGE-RAGE signaling pathway in diabetic complications 31.05
CXCL8, SERPINE1, TNF, RELA, ICAM1, THBD, MAPK8, CCND1, CASP3, CCL2,
AKT1, MAPK1, TGFB1, VCAM1, PRKCB, NOS3, STAT1, MMP2, NFATC1, MAPK14,
SELE, F3, VEGFA, MAPK10, IL1A, COL3A1, IL6, CDK4, IL1B, BCL2, BAX 31
hsa05167 Kaposi sarcoma-associated herpesvirus infection 20.65 GSK3B,
CXCL8, PTGS2, HIF1A, CXCL2, RELA, PIK3CG, ICAM1, CASP9, PPP3CA, MAPK8,
CASP8, CCND1, MYC, CASP3, E2F1, AKT1, MAPK1, CHUK, STAT1, NFATC1, FOS,
MAPK14, VEGFA, MAPK10, IL6, CDK4, BAX, RAF1, TP53 30
hsa05207 Chemical carcinogenesis—receptor activation 19.50 NR1I3,
ADRB1, AHR, ADRB2, CYP3A4, RELA, EGFR, RXRB, CYP2B6, RXRA, CCND1, MYC,
E2F1, AKT1, MAPK1, RXRG, UGT1A1, PRKCB, EGF, FOS, ESR1, ESR2, VEGFA,
AR, CYP1A1, BCL2, BIRC5, PGR, RAF1, PPARA 30
hsa05163 Human cytomegalovirus infection 17.81 GSK3B, CXCL8, PTGS2,
TNF, RELA, EGFR, CASP9, PPP3CA, CASP8, CCND1, MYC, CASP3, E2F1, CCL2,
AKT1, MAPK1, CHUK, CDKN2A, PRKCB, NFATC1, MAPK14, VEGFA, IL6, CDK4,
IL1B, MDM2, BAX, RAF1, TP53 29
hsa04010 MAPK signaling pathway 12.69 HSPB1, TNF, RELA, EGFR, PPP3CA,
MAPK8, MYC, CASP3, ERBB2, AKT1, MAPK1, TGFB1, CHUK, PRKCB, EGF, INSR,
IGF2, NFATC1, FOS, MAPK14, VEGFA, MAPK10, IL1A, RASA1, IL1B, RAF1, TP53
27
hsa04151 PI3K-Akt signaling pathway 10.81 GSK3B, PTEN, RELA, EGFR,
PIK3CG, CASP9, RXRA, CCND1, MYC, ERBB2, SPP1, AKT1, MAPK1, CHUK, EGF,
NOS3, INSR, IGF2, IL2, VEGFA, IL4, IL6, CDK4, BCL2, MDM2, RAF1, TP53 27
hsa05418 Fluid shear stress and atherosclerosis 19.75 PLAT, TNF, RELA,
ICAM1, THBD, MAPK8, CCL2, AKT1, HMOX1, VCAM1, CHUK, NOS3, CAV1, MMP2,
FOS, MAPK14, SELE, MMP9, VEGFA, MAPK10, IL1A, IFNG, IL1B, BCL2, TP53,
NFE2L2 26
hsa05161 Hepatitis B 18.15 CXCL8, TNF, RELA, CASP9, MAPK8, CASP8, MYC,
CASP3, E2F1, AKT1, MAPK1, TGFB1, CHUK, PRKCB, STAT1, NFATC1, FOS,
MAPK14, MMP9, MAPK10, IL6, BCL2, BAX, BIRC5, RAF1, TP53 26
[103]Open in a new tab
3.6. Determination of AHT Compounding Ratio
An orthogonal experiment was conducted to determine the optimal
blending ratio of AME, HRE, and TME. According to the theoretical
analysis of the k value, the optimal combination is A[3]H[1]T[2], which
meant the ratio of AME, HRE, and TME was 3:1:2. To verify this ratio,
the pancreatic lipase inhibition rate was 97.74 ± 0.62% ([104]Table 5).
Table 5.
Pancreatic lipase inhibition rates of AME, HRE, and TME with different
compounding ratios.
Group AME HRE TME Inhibition Rate%
1 1 1 1 80.71 ± 0.83
2 1 2 2 79.84 ± 0.80
3 1 3 3 72.11 ± 1.94
4 2 1 2 94.25 ± 1.10
5 2 2 3 86.08 ± 0.86
6 2 3 1 89.64 ± 1.23
7 3 1 3 96.72 ± 1.30
8 3 2 1 95.87 ± 0.25
9 3 3 2 96.47 ± 0.32
k[1] 77.55 90.56 88.74
k[2] 89.99 87.26 90.19
k[3] 96.35 86.07 84.97
R 18.80 4.49 5.13
Best combination A[3]H[1]T[2]
[105]Open in a new tab
3.7. Combination Index Analysis of AHT, AME, HRE, and TME
To further determine whether the combination of various extraction
solutions has a synergistic effect, the dose–response relationship
between each individual extraction solution and the combination group
was studied, and the combination index (CI) value of each experimental
group was calculated based on the principle of medium effect. The CI
value of less than 1 indicated a synergistic effect between the
components. As shown in [106]Figure 4A, the CI values of all groups
were less than 1, indicating that the combination of the three extracts
showed sound synergistic effects. The CI value of AHT (A[3]H[1]T[2])
was significantly lower than that of the other groups (p < 0.05),
indicating that AHT had the best synergistic effect. [107]Figure 4B
showed that the pancreatic lipase inhibition effect of AHT was superior
to that of AME, HRE, and TME at various addition ratios. [108]Figure 4C
illustrates that when the synergistic effect reached 50%, the drug
activity size was AHT > HRE > TME > AME. The above results indicated
that the in vitro lipid-lowering effect of AHT was due to the
combination of a single extract and other ratios.
Figure 4.
[109]Figure 4
[110]Open in a new tab
Combination Index analysis of AHT, AME, HRE, and TME. (A) CI values are
combined in different proportions. (B) Dose effect curves of AHT, AME,
HRE, and TME on pancreatic lipase inhibition. (C) Intermediate effect
diagrams of AHT, AME, HRE, and TME. (n = 3, **** and * represent p <
0.01 and p < 0.05, respectively).
3.8. The Effects of AHT, AME, HRE, and TME on HepG2 Cell Proliferation and
Oleic Acid Staining
To verify the lipid-lowering effect of AHT, we chose to establish a
high-fat blood model using HepG2 cells. Before establishing the model,
the cytotoxicity of AME, HRE, and TME were first detected by MTT assay.
As shown in [111]Figure 5A, AME and HRE had a promoting effect on cell
proliferation at concentrations of 200 μM and 100 μM, while TME had a
significant promoting effect at concentrations of 50 μM and 100 μM (p >
0.05). TME at a concentration of 100 μM was selected for the next
experiment. When the dosage continued to increase, the activity of
cells was inhibited. Next, the optimal concentrations of AME, HRE, and
TME were compounded in a ratio of 3:1:2 to obtain the compound extract
AHT. The lipid-lowering effect of AHT was verified. HepG2 cells were
induced to produce lipid droplets using 0.5 mM OA, and the lipid
droplets inside the cells were observed by staining with Oil Red O.
[112]Figure 5B showed that no obvious lipid droplet staining was
observed in the NC group, and the cell morphology was good. Red lipid
droplets were clearly observed within the OA group cells, covering the
entire cell periphery. After adding AHT, AME, HRE, and TME, a decrease
in intercellular lipid droplets was observed. The results showed that
AHT, AME, HRE, and TME could alleviate lipid deposition in HepG2 cells
to varying degrees.
Figure 5.
[113]Figure 5
[114]Open in a new tab
Effects of AHT, AME, HRE, and TME on HepG2 cell proliferation and oleic
acid staining. (A) Cytotoxicity assay. (B) Oleic acid staining image.
(n = 3, ****, ***, **, *, and ns represent p < 0.0001, p < 0.001, p <
0.01, p < 0.05, and p > 0.05, respectively).
3.9. The Lipid-Lowering Effect of AHT on Oleic Acid-Induced HepG2 Cells
The HepG2 cell model induced by oleic acid (OA) had been widely used
for in vitro studies of hyperlipidemia ([115]Figure 6). Compared with
the NC group, HepG2 cells induced by OA showed significantly increased
levels of TC, TG, and LDL-C (p < 0.01) and significantly decreased
levels of HDL-C (p < 0.01). After administration, compared with the OA
group, the levels of AHT, AME, HRE, and TME decreased by 31.53%,
21.04%, 22.95%, and 24.88% (p < 0.01) in TC, 30.37%, 14.84%, 14.50%,
and 18.84% (p < 0.01) in TG, 25.93%, 20.74%, 19.26%, and 23.70% (p <
0.01) in LDL-C, and 90.10%, 63.37%, 67.33%, and 72.28% (p < 0.01) in
HDL-C, respectively. It indicated that AHT, AME, HRE, and TME all have
good lipid-lowering effects, and the lipid-lowering performance of AHT
after compounding has been further improved.
Figure 6.
[116]Figure 6
[117]Open in a new tab
Determination of AHT lipid-lowering levels. (A) TC. (B) TG. (C) LDL-C.
(D) HDL-C. (n = 3, ****, ***, **, *, and ns represent p < 0.0001, p <
0.001, p < 0.01, p < 0.05, and p > 0.05, respectively).
3.10. The Effect of AHT on the Lipid-Lowering Mechanism Induced by Oleic Acid
in HepG2 Cells
Based on the prediction of lipid-lowering-related proteins by network
pharmacology, we treated HepG2 cells induced by oleic acid with AHT,
AME, HRE, and TME and verified the effects of the MAPK signaling
pathway and the PI3K-Akt signaling pathway on the lipid-lowering
mechanism of HepG2 cells ([118]Figure 7A). As shown in [119]Figure
7B,C, the protein expression of AKT, phosphorylated AKT (p-AKT), PI3K,
and phosphorylated PI3K (p-PI3K) were validated and the relevant
protein ratios were calculated. It was found that the protein
expression levels of p-AKT/AKT and p-PI3K/PI3K in HepG2 cells induced
by oleic acid were significantly reduced after treatment with AHT, AME,
HRE, and TME (p < 0.05). AHT significantly reduced the phosphorylation
of AKT and PI3K in HepG2 cells (p < 0.01). As shown in [120]Figure
7D,E, AHT significantly increased the protein expression levels of
AMPKα1, phosphorylated AMPKα1, and SIRT1 (p < 0.05). The above results
indicated that AHT could regulate lipid function and improve lipid
metabolism balance through the MAPK signaling pathway and the PI3K-Akt
signaling pathway.
Figure 7.
[121]Figure 7
[122]Open in a new tab
The effect of AHT on critical proteins in the PI3K Akt signaling
pathway and MAPK signaling pathway. (A) Immunoblot analysis of related
proteins. (B) The ratio of p-AKT/AKT protein quantification results.
(C) The ratio of p-PI3K/PI3K protein quantification results. (D) The
ratio of p-AMPK/AMPK protein quantification results. (E) SIRT1 protein
quantification results. (n = 3, ****, ***, **, *, and ns represent p <
0.0001, p < 0.001, p < 0.01, p < 0.05, and p > 0.05, respectively).
3.11. The Effect of AHT on Body Weight, Food Intake, and Organ Index in Mice
As shown in [123]Figure 8A, there was no significant difference in the
initial body weight of mice at 0 w (p > 0.05). With the prolongation of
time, at 2 w, the weight of the MC group fed with a high-fat diet was
significantly higher than that of the NC group fed with a normal diet
(p < 0.05). At 4 w, the weight of the MC group was significantly higher
than that of the HD group (p < 0.01). However, as shown in [124]Figure
8B, there was no significant difference in food intake among the groups
fed a high-fat diet (p > 0.05). It indicated that a high-fat diet could
lead to weight gain in mice, and when AHT was supplemented, it
prevented weight gain induced by a high-fat diet. But there was no
significant difference in organ index, indicating that AHT had no
adverse effects on mice (p > 0.05) ([125]Table 6).
Figure 8.
[126]Figure 8
[127]Open in a new tab
Body weight and food intake of mice. (A) Changes in body weight of mice
in each group during 8 w. (n = 8, *, ** represent significant and
extremely significant differences between the MC group and the NC group
(p < 0.05, p < 0.01); ## represents extremely significant differences
between the MC group and the HD group (p < 0.05). (B) During the 8 w
period, the food intake of mice in each group. (n = 3, ****, **, and ns
represent p < 0.0001, p < 0.01, and p > 0.05, respectively).
Table 6.
The effect of AHT on organ indices in different groups of mice.
Group NC MC PC LD MD HD
Heart (%) 0.693 ± 0.11 0.672 ± 0.11 0.602 ± 0.10 0.707 ± 0.19 0.660 ±
0.09 0.613 ± 0.10
Liver (%) 4.554 ± 0.47 4.527 ± 0.35 3.847 ± 1.40 4.750 ± 0.67 4.483 ±
0.66 4.548 ± 0.74
Spleen (%) 0.327 ± 0.03 0.309 ± 0.05 0.253 ± 0.06 0.306 ± 0.08 0.282 ±
0.10 0.321 ± 0.09
Kidney (%) 1.462 ± 0.18 1.367 ± 0.13 1.357 ± 0.17 1.463 ± 0.19 1.334 ±
0.23 1.271 ± 0.14
[128]Open in a new tab
(n = 8, p > 0.05; there was no significant difference between the
groups).
3.12. The Effect of AHT on Serum Biochemical Indicators in High-Fat Mice
Blood lipids, liver damage, and antioxidant levels in mouse serum were
measured. As shown in [129]Figure 9A–D, compared with the NC group, the
levels of TC, TG, and LDL-C were significantly increased in the MC
group, while the level of HDL-C was significantly decreased. The LD
group, MD group, and HD group all showed varying degrees of
improvement, with TC, TG, and LDL-C levels in the HD group decreasing
by 29.01%, 42.98%, and 55.01%, respectively, compared to the MC group
(p < 0.01), while HDL-C levels increased significantly by 165.85% (p <
0.0001). AST and ALT are considered indicators for detecting liver
injury and can verify the damage of hyperlipidemia to the liver of
mice. As shown in [130]Figure 9E,F, the AST and ALT levels in the HD
group decreased by 49.19% and 45.23%, respectively, compared to the MC
group and returned to normal levels. Dysregulation of lipid metabolism
could lead to the production of large amounts of reactive oxygen
species in the body, causing oxidative stress. Therefore, we validated
the antioxidant level and found that the antioxidant capacity of the MC
group significantly decreased, while the SOD and T-AOC levels of the HD
group significantly increased by 18.99% and 44.20%, respectively,
compared to the MC group (p < 0.01) ([131]Figure 9G,H).
Figure 9.
[132]Figure 9
[133]Open in a new tab
Serum indicators. (A) TC. (B) TG. (C) LDL-C. (D) HDL-C. (E) AST. (F)
ALT. (G) SOD. (H) T-AOC. (n = 3, ****, ***, **, *, and ns represent p <
0.0001, p < 0.001, p < 0.01, p < 0.05, and p > 0.05, respectively).
4. Discussion
Long-term high-fat diets will lead to a disorder of lipid metabolism
and increase the risk of chronic diseases such as obesity,
hyperlipidemia, and diabetes [[134]33]. Hyperlipidemia is a complex
disease; the traditional intervention method is taking statins.
However, these drugs usually have a single site of action, and
long-term use can cause some toxic side effects. Previous studies have
found that disturbances in liver lipid metabolism, oxidative stress,
and chronic inflammation in the body often accompany the occurrence of
hyperlipidemia [[135]34]. Therefore, using natural plant ingredients
with multi-target, multi-pathway, natural, safe, and efficient
characteristics to prevent or intervene in lipid metabolism disorders
has attracted widespread attention from researchers [[136]35].
Astragalus membranaceus, Hippophae rhamnoides L., and Taraxacum
mongolicum Hand. Mazz are traditional edible and medicinal plants with
a long history of consumption. They have been widely reported to have
various bioactive functions, such as lowering blood lipids,
antioxidation, and immune regulation [[137]36,[138]37,[139]38].
Therefore, this study aimed to explore the lipid-lowering effects,
targets, and mechanisms of the combined application of Astragalus
membranaceus extract, Hippophae rhamnoides extract, and dandelion
extract.
We first identified the main compounds in Astragalus membranaceus
extract, Hippophae rhamnoides L. extract, and Taraxacum mongolicum
Hand. Mazz extract using LC-MS and identified 1539 compounds. Research
has confirmed that compound plants have better regulatory effects on
lipid metabolism/diseases than single plants. Previous studies have
shown that AME, HRE, and TME all have lipid-lowering activity, but
their potential mechanisms for preventing hyperlipidemia have yet to be
fully explored. Further research is needed to verify their mechanisms
of action, such as key compounds, targets, and mechanisms. Therefore,
network pharmacology was used to predict the targets and signaling
pathways of the combined intervention of AME, HRE, and TME in
hyperlipidemia. A total of 41 compounds and 140 potential targets for
treating hyperlipidemia were detected. Fifteen core targets, including
TP53, PPARG, ESR1, AKT1, RELA, and MAPK1, were identified through PPI
analysis. Key compounds such as quercetin, luteolin, kaempferol, and
baicalein were identified through compound target pathway analysis.
These key compounds and core targets may be key factors in treating
hyperlipidemia. Research has shown that the main component of the
polyphenol-rich extract from Allium cepa and Gynostemma pentaphyllum is
quercetin, demonstrating that quercetin can achieve therapeutic effects
on hyperlipidemia through LOX1-PI3K AKT eNOS [[140]39,[141]40]. In
addition, Astragalus membranaceus can regulate lipid metabolism by
downregulating AKT1 and upregulating ESR1 [[142]41]. These results are
similar to our research findings [[143]42]. The results of KEGG
enrichment analysis indicate that AHT may regulate hyperlipidemia
through lipids and atherosclerosis, MAPK signaling pathway, and
PI3K-Akt signaling pathway.
To verify the predictive results of network pharmacology, AME, HRE, and
TME were combined to obtain AHT. The lipid-lowering ability,
antioxidant capacity, and mechanism of action of AHT were validated by
establishing oleic acid-induced HepG2 cell models and high-fat
diet-induced hyperlipidemia mouse models. The results showed that AHT
can regulate lipid function, alleviate oxidative stress, and have an
excellent lipid-lowering effect through the MAPK and PI3K-Akt signaling
pathways. Research shows that the PI3K-Akt signaling pathway can
participate in atherosclerosis activity and improve glucose and lipid
metabolism in mice [[144]43,[145]44]. In this study, AHT can regulate
the PI3K-Akt signaling pathway and activate the expression of SIRT1
protein. SIRT1, as a dependent acylase, is involved in lipid metabolism
disorders such as hyperlipidemia, which reduces the protein expression
level of SITR1 [[146]45]. The SIRT1/AMPK pathway can regulate lipid
content, increase mitochondrial fatty acid oxidation, and promote lipid
breakdown and energy metabolism. In Zheng et al.’s study [[147]46],
SDF-PPs effectively reversed the decrease in p-AMPK/AMPK and inhibited
the activity of acetyl CoA carboxylase 1 (ACC1). Our research also
found that AMPK phosphorylation was enhanced in HepG2 cells treated
with AHT. Previous reports have shown that α-ketoglutarate (AKG)
activates AMPK protein in liver cells in dyslipidemia, enhancing its
phosphorylation. After AMPK phosphorylation enhancement, it can inhibit
the increase in TC and TG and oxidative stress induced by palmitic acid
in HepG2 cells [[148]47]. Our results also found a significant decrease
in TC, TG, and LDL-C levels in cells after AHT intervention, as well as
a significant increase in HDL-C levels. It indicates that AHT regulates
the lipid breakdown ability in liver cells by activating the MAPK and
PI3K-Akt signaling pathways, inhibiting TC and TG accumulation in HepG2
cells and mice. The above results indicate that AHT can enhance the
regulatory ability of liver cells toward lipid metabolism and their
resistance to oxidative stress. To further evaluate the lipid-lowering
effect of AHT, we established a high-fat mouse model using a high-fat
diet. After 8 weeks of AHT intervention, we found that the levels of
TC, TG, and LDL-C in the HD group were significantly reduced, HDL-C
levels were significantly increased, AST and ALT activities were
decreased, and SOD and T-AOC levels were increased
[[149]7,[150]48,[151]49]. These results indicate that AHT can improve
lipid metabolism disorders in high-fat mice by increasing the body’s
antioxidant levels and reducing liver damage. In summary, we validated
the targets and pathways predicted by network pharmacology through cell
and animal experiments. We found that AHT can improve hyperlipidemia
and regulate lipid metabolism by regulating the MAPK and PI3K-Akt
signaling pathways.
5. Conclusions
In this work, network pharmacology was used to predict and analyze the
potential mechanisms of bioactive compounds in AME, HRE, and TME for
treating hyperlipidemia, and the lipid-lowering mechanism of AHT was
explored through in vitro and in vivo experiments. The research results
indicate that compounds in AHT, such as quercetin, luteolin,
kaempferol, and baicalein, might affect hyperlipidemia by targeting
core targets such as TP53, PPARG, ESR1, AKT1, RELA, MAPK1, as well as
by acting on the PI3K-Akt and MAPK signaling pathways. In vitro and in
vivo experiments showed that AHT could reduce TC, TG, and LDL-C levels,
increase HDL-C levels, reduce liver injury, and enhance the body’s
antioxidant capacity. Most importantly, AHT could improve
hyperlipidemia by affecting the expression of proteins related to the
PI3K-Akt and MAPK signaling pathways. In summary, these findings
provide a new strategy for AHT to serve as a safe and efficient
resource for improving lipid metabolism disorders such as
hyperlipidemia.
List of Abbreviations
ALT Alanine aminotransferase
AST Aspartate aminotransferase
AME Astragalus membranaceus extract
AHT Astragalus membranaceus extract: Hippophae rhamnoides L. extract:
Taraxacum mongolicum Hand. Mazz extract = 3:1:2
HDL-C High-density lipoprotein cholesterol
HRE Hippophae rhamnoides L. extract
LC-MS Liquid chromatography–mass spectrometry
LDL-C Low-density lipoprotein cholesterol
OMIM Online Mendelian Inheritance in Man
PPI Protein–protein interaction
SOD Superoxide dismutase
TME Taraxacum mongolicum Hand. Mazz extract
TCMSP Traditional Chinese Medicine System Pharmacology Analysis
Platform
T-AOC Total antioxidant capacity
TC Total cholesterol
TG Triglycerides
[152]Open in a new tab
Supplementary Materials
The following supporting information can be downloaded at:
[153]https://www.mdpi.com/article/10.3390/foods13172795/s1, Table S1:
Drug targets, Table S2: Target of hyperlipidemia, Table S3: Targets of
drugs and diseases.
[154]foods-13-02795-s001.zip^ (63.8KB, zip)
Author Contributions
Investigation, J.L.; formal analysis, Y.A. and Z.C.; conceptualization,
Y.B.; methodology, X.Y. and M.J.; writing—original draft, X.Y. and
M.J.; writing—review and editing, X.Y. and M.J.; resources, Y.B.;
funding acquisition, Y.B. All authors have read and agreed to the
published version of the manuscript.
Institutional Review Board Statement
The animal research protocol has been approved by the Science and
Technology Ethics Committee of Northeast Forestry University
(NEFU2024-011) for research involving animals.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the
article and [155]Supplementary Materials, further inquiries can be
directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research was funded by Heilongjiang Province Key Research and
Development Project (JD2023SJ08), National Key Research and Development
Program Project (2022YFD1600500), Heilongjiang Province Discipline
Collaborative Innovation Project (Zxkxt220100002).
Footnotes
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References