Optimization of the proportions of advantageous components in the hypolipidemic “bioequivalent substance system” of Jiang-Zhi-Ning and its mechanism of action

Abstract

Context

Jiang-Zhi-Ning (JZN), a traditional Chinese medicinal formula, is used to treat hyperlipidemia in clinics.

Objective

To screen the hypolipidemic “bioequivalent substance system (BSS)” of JZN and elucidate the potential hypolipidemic mechanism.

Materials and methods

In vitro, the TG content in HepG2 cells was determined after the intervention of the combination of advantageous components (CAC) by uniform design. In vivo, hyperlipidemia models were established by Triton WR-1339 (400 mg/kg; i.p.) in male ICR mice, and corresponding treatments were administered via oral administration once. The mice were divided into 12 groups (n = 5): control, hyperlipidemic model, simvastatin (positive control, 20 mg/kg), gradient doses of JZN granules (2, 4 and 8 g/kg) and the hypolipidemic effective extraction (HEE) of JZN (120, 240 and 480 mg/kg) and CAC groups (20, 40 and 160 mg/kg). Serum TC, TG, LDL-C and HDL-C were performed after 24 h. Transcriptomics and qRT–PCR technology were used to explore the mechanism of the “BSS” of JZN.

Results

In vitro, the ratio of CAC was determined. CAC could reduce the TG content in HepG2 cells (77.21%). Compared with the model group, the high dose of CAC could markedly decrease the levels of TC (61.86%), TG (105.54%) and LDL-C (39.38%) and increase the level of HDL-C (232.67%). CAC was proved to be the “BSS”. Transcriptomics and qRT–PCR analysis revealed CAC regulated non-alcoholic fatty liver disease, bile secretion, PPAR and adipocytokine signalling pathway.

Discussion and conclusions

These findings provided new feasible ideas and methods for the elucidation of the pharmacodynamic material basis.

Keywords:

• Hyperlipidemia
• uniform design
• transcriptome sequencing

Introduction

Hyperlipidemia is a pathological condition of lipid metabolism disorder for a variety of reasons and is also a common cause of atherosclerosis and other diseases. In recent years, many researchers explored the mechanism of traditional Chinese medicine and chemical medicine in preventing and treating hyperlipidemia. Regardless if it is traditional Chinese medicine or chemical medicine, it usually plays a role in lowering blood lipids by regulating cholesterol metabolism, triglyceride metabolism, bile acid metabolism, etc. At the same time, recent research showed that some targets play an important role in treating hyperlipidemia, such as PCSK9 and ANGPTL3 (Zhu et al. 2016; Rosenson et al. 2020; Ahamad and Bhat 2022; Shakir et al. 2022). Traditional Chinese medicine, which has the advantages of significant efficacy, diverse pathways of action and few side effects in the treatment of hyperlipidemia over a long history, has become an exciting topic in recent years in this field.

The traditional Chinese medicine (TCM) Jiang-Zhi-Ning (JZN) is composed of Polygonum multiflorum Thunb. [Polygonaceae] (the root tuber), Crataegus pinnatifida Bge. [Nymphaeaceae] (the fructus), Nelumbo nucifera Gaertn. [Rosaceae] (the leaves) and Cassia obtusifolia L. [Leguminous] (the seed), and it was recorded in Volume 13 of the Chinese Medicine Prescriptions Standard issued by the PRC health ministry. JZN, which is commonly used in the clinical treatment of hyperlipidemia, promotes blood circulation of the coronary artery, softens blood vessels, resists arrhythmia and lowers blood lipids (Chinese Pharmacopoeia Commission 2020). Modern pharmacological studies showed that effective extraction of JZN could significantly reduce the contents of TC, TG, LDL-C, CRI and AI in hyperlipidemic rats (Chen et al. 2012) and regulate cholesterol metabolism by regulating LDL receptor and Cyp7a1 mRNA expression levels (Chen et al. 2011). In the JZN prescription, Crataegus pinnatifida is the monarch drug, which contains flavonoids, triterpenes, phytosterols, organic acids, etc., with the effect of promoting the β-oxidation of fatty acids and inhibiting cholesterol synthesis (Lin et al. 2011; Niu et al. 2011; Kwok et al. 2013). Nelumbo nucifera is the minister drug and mainly contains flavonoids and alkaloids, among others, and it has the effects of lipid-lowering, antihypertensive and stopping bleeding (Li et al. 2017). Cassia obtusifolia is an adjuvant drug that contains anthraquinones and naphthol with the effects of reducing exogenous lipid uptake, inhibiting endogenous lipid synthesis and promoting lipid transport (Li et al. 2008; Gao et al. 2013; Wang et al. 2014). Polygonum multiflorum is an absorbent drug that contains flavonoids, anthraquinone, stilbene glycoside, polysaccharide, lecithin, etc., with the effect of increasing the liver lipase content and promoting the breakdown of liver lipids (Zuo et al. 2017; Zhang et al. 2019).

Commonly, TCM compound preparation shows the characteristics of a complex composition of chemical components, diverse pathways of action and ambiguous mechanisms of action (Hao et al. 2017). TCM compounds exert their clinical therapeutic efficacy by multiple active components acting on multiple targets cooperatively, rather than the linear addition of a few chemical components (Zhang and Feng 2019). TCM compounds usually contain many different types of chemical components, including several active components. These active components are combined to exert medicinal effects, with different chemical components having different pathways or mechanisms of action. Due to many factors, such as origin, harvest time and growing environment, the contents of active components in natural medicines vary, which will also affect the efficacy. At the same time, the relationship between the efficacy and concentration of active components is not an absolute linear relationship. Meanwhile, several types of effective components are combined together; in other words, the efficacy of TCM compounds, including several types of effective components combined together is the result of the synergistic effect of various components, which is not only related to the composition of the effective components but also related to the ratio and interaction of each component. Therefore, for TCM prescriptions, the basic research of pharmacodynamic substances includes not only the discovery of the active components but also the confirmation of the proportion of each active component as well as a clear explanation of its mechanism. Therefore, our group proposed the research idea of the TCM compound “bioequivalent substance system” (BSS). In other words, in TCM compounds, a group of effective components with the same pharmacodynamic effect, clear chemical composition and proportional relationship is the TCM compound “BSS”.

According to the research idea of the TCM compound “bioequivalent substance system”, our previous work demonstrated that quercetin-3-O-β-d-glucuronide, armepavine, emodin-8-O-β-d-glucoside, 2-hydroxy-1-methoxyaporphine, hyperoside and rubrofusarin-6-O-β-d-gentiobioside (Figure 1) (Zhang et al. 2021) were the hypolipidemic advantageous components of JZN, which had good hypolipidemic activity. However, the underlying mechanisms of JZN in lowering blood lipids remains to be clarified.

Figure 1. Structure of each advantage component. A: quercetin-3-O-β-d-glucuronide B: hyperoside C: emodin-8-O-β-d-glucoside D: rubrofusarin-6-O-β-d-gentiobioside E: 2-hydroxy-1-methoxyaporphine F: armepavine.

On the basis of a previous study (Zhang et al. 2021), the CAC and the proportion of advantageous components in JZN were determined in this study, and the pharmacodynamic substance basis and hypolipidemic mechanism of JZN were clarified. Our study provides a scientific basis for the establishment of quality control methods and the clinical application of JZN.

Materials and methods

Materials and reagents

Armepavine (batch no.: P22M10F88910), 2-hydroxy-1-methoxyaporphine (batch no.: M16N10S103163), hyperoside (batch no.: P14A11F121347), emodin-8-O-β-d-glucoside (batch no.: P17A10F86138), and sodium oleate (batch no.: J24M6Y1) were purchased from the Shanghai Yuanye Biotechnology Co., Ltd. (Shanghai, China). Quercetin 3-O-β-d-glucuronide (batch no.: Must-20070805) was purchased from the Chengdu Must Biotechnology Co., Ltd. (Chengdu, China). Rubrofusarin-6-O-β-d-gentiobioside (batch no.: 16120621) was purchased from the Shanghai Tauto Biotech Co., Ltd. (Shanghai, China). The purity of all standards was demonstrated to exceed 98.0% by HPLC analysis. Chloromethane, isopropyl alcohol and anhydrous ethanol were all from the Beijing Chemical Plant Co., Ltd. (Beijing, China) and were pure for analysis.

All herbs used in this study were commercially available dry matter. Crataegus pinnatifida (Hebei, batch no.: 20160315), Cassia obtusifolia (Anhui, batch no.: 20160501), Nelumbo nucifera (Hebei, batch no.: 20160301) and Polygonum multiflorum (Hubei, batch no.: 20160401) were purchased from the Anguo Shengtai Medicinal Materials Co. Ltd. (Hebei, China). All samples were authenticated by Professor Yuan Zhang (College of Traditional Chinese Medicine, Beijing University of Chinese Medicine). JZN granules (batch no.: 150812) were purchased from the Beijing Tongrentang (Group) Co., Ltd., and simvastatin (batch no.: P03869) was purchased from the Beijing Huawei Ruike Chemical Company.

Animals and experimental protocol

Four-week-old male ICR mice weighing 18–22 g were purchased from the SiBeiFu Biotechnology Co., Ltd. (Beijing, China) (Certificate No: SCXK (Jing) 2019-0010). The mice were maintained at 25–30 °C, 50–70% relative humidity and a 12 h light/dark cycle with free access to food and water. All animal experiments were conducted in accordance with the ethical standards of the Animal Care and Welfare Committee of Beijing University of Chinese Medicine (Certificate number: BUCM-04-2018030102-1013).

Cell culture

HepG2 cells were purchased from the Cell Resource Center (Beijing, China) and cultured in RPMI-1640 (Thermo Corporation, Waltham, MA) supplemented with 10% FBS and antibiotics (100 units/mL penicillin and 100 μg/mL streptomycin) (Thermo Corporation) in an incubator at 37 °C in humidified 5% CO₂.

Preparation of the HEE of JZN

According to the formula ratio of JZN, 500 g of Crataegus pinnatifida and 25 g of Cassia obtusifolia were weighed and extracted in distilled water twice (2 h each time, the amount of the solvent was seven times the total weight of herbs), and then the extract was evaporated into paste. Meanwhile, 75 g of Nelumbo nucifera and 25 g of Polygonum multiflorum were weighed, mixed together, added to a volume of 50% ethanol (25 times) to reflux for 1.5 h, and filtered, and then the residue was refluxed in the same way twice. The ethanol extract was mixed and evaporated into a paste. The two pastes above were mixed and suspended in 5000 mL of distilled water. An AB-8 macroporous adsorption resin column (with a diameter-to-height ratio of 1:7) was used for enrichment and separation. After the adsorption was completed, the resin column was eluted with distilled water and 50% ethanol, and the 50% ethanol eluate was collected and dried under vacuum conditions. Dryness was proven to be responsible for the efficacy of JZN, and it was the HEE of JZN.

Ratio optimization of the hypolipidemic CAC of JZN

Selection of the mixed-level uniform design table

The minimum concentration at which the six advantageous components exerted hypolipidemic activity was taken as the initial concentration, and the IC₅₀ value of the six advantageous components in inhibiting TG contents in HepG2 cells was taken as the intermediate concentration. The concentration levels of each component were uniformly designed within the safe concentration range of each advantageous component (Huang et al. 2016; Zhang et al. 2021; Chen et al. 2023). As the intermediate concentration of armepavine was close to the maximum safe concentration, a uniform design experiment at the optimized mixing level was chosen. The uniform design table of the optimal mixing level (Sun et al. 2015) was constructed using DPS software (Version 9.01, China). The advantageous components were combined into 12 combinations according to the concentration shown in Table 1.

Table 1. Concentration level table of each combination.

Combination	Quercetin 3-O-β-D-glucuronide (μM) X1	Hyperoside (μM) X2	Rubrofusarin- 6-O-β-D-gentiobioside (μM) X3	Emodin-8-O-β-D- glucoside (μM) X4	2-Hydroxy-1- methoxyaporphine (μM) X5	Armepavine (μM) X6
N1	50	68	145	103	3	50
N2	95	44	25	68	17	80
N3	65	152	10	96	45	35
N4	170	140	115	75	10	20
N5	125	92	55	117	24	5
N6	155	104	40	54	80	50
N7	110	128	160	89	66	80
N8	185	56	100	110	52	65
N9	35	32	70	82	73	20
N10	20	116	85	47	31	65
N11	140	20	175	61	38	35
N12	80	80	130	40	59	5

Establishment of hyperlipidemia model in HepG2 cells and determination of the TG content

HepG2 cells were placed in 6-well plates (1 × 10⁵ cells/well) and incubated in 5% CO₂ and 37 °C incubators for 24 h. The cells were induced by sodium oleate (SO 70 μg/mL) and cultured for 24 h. Then, the cells were divided into the control group, administration group, model group (SO 70 μg/mL) and sim group (35 μmol/mL). Each administration group was treated with 2 mL of drug-containing medium at different concentrations and incubated for 24 h. At the end of treatment, the cells were treated with Triton X-100 lysate and then centrifuged at 1000 rpm for 10 min at 4 °C, and the TG contents were determined with an assay kit (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer’s instructions.

Data analysis

The effect of each combination on the TG content in lipid-laden HepG2 cells was transformed into the inhibition rate of each combination on the TG content according to the formula: (1) $$\text{Efficacy}=\frac{{\text{Content}}_{\text{model}}-{\text{Content}}_{\text{drug}}}{{\text{Content}}_{\text{model}}-{\text{Content}}_{\text{control}}}\times 100\%$$(1)

The raw data and inhibition rate of each experiment were input into DPS 9.01 software, and the input data were defined as data matrix blocks. Then, quadratic polynomial stepwise regression analysis was performed.

Bioequivalence analysis in vitro

The control group, administration group, model group (SO) and sim group were set. Each administration group was treated with 2 mL of JZN granules and the HEE of JZN, and incubated for 24 h. The method was described above in “Establishment of hyperlipidemia model in HepG2 cells and determination of the TG content”.

Bioequivalence analysis in vivo

Establishment of hyperlipidemia model in mice

Four-week-old male ICR mice weighing 18–22 g were randomly divided into 12 groups, with 6 mice in each group, including the control group, hyperlipidemic model group, sim group (20 mg/kg), low, middle and high dose of JZN granules (2, 4, 8 g/kg), low, middle and high dose of the HEE of JZN (120, 240, 480 mg/kg), and low, middle and high dose of the CAC groups (20, 40, 160 mg/kg). All groups were fed a standard chow diet. Triton WR-1339 model solution (400 mg/kg) was intraperitoneally injected into all mice in the treated groups after 12 h of fasting to induce a hyperlipidemic model, and mice in the normal control group were intraperitoneally injected with an equal volume of saline. Then, the TG, TC and LDL-C contents in the serum of model mice were significantly higher than those of the control mice (p < 0.01), and the HDL-C content in the serum of model mice was significantly lower than that of the control mice (p < 0.01), indicating that the hyperlipidemia mice were successfully modelled.

Determination of lipid indices

After intraperitoneal injection with Triton WR-1339, all groups were orally administered the relevant drugs, and the control group and hyperlipidemic model group were given an equal volume of 0.2% CMC-Na solution.

After 24 h of oral administration, mice were anesthetized with ether, blood samples were collected into centrifuge tubes from the fosse orbital vein and centrifuged, and the supernatant was collected for testing. The concentrations of TC, TG, HDL-C and LDL-C in the serum of mice were measured with a commercially available detection kit (Nanjing Jiancheng Institute of Biological Engineering, Nanjing, China) according to the manufacturer’s instructions.

Assessment of bioequivalence

According to the above Formula 1 in “Data analysis”, the content determination results of TC, TG, HDL-C and LDL-C in animal experiments were converted into the inhibition rates of TC, TG and LDL-C and the promotion rate of HDL-C of each drug (i.e., efficacy value), and the content determination results of TG in cell experiments were converted into the inhibition rate of TG of each drug (i.e., efficacy value). (1) $$\text{Efficacy}=\frac{{\text{Content}}_{\text{model}}-{\text{Content}}_{\text{drug}}}{{\text{Content}}_{\text{model}}-{\text{Content}}_{\text{control}}}\times 100\%$$(1)

Bioequivalence was evaluated by calculating the 95% confidence interval of the ratio between the inhibition rates of the two drugs. If the 95% confidence interval of relative efficacy fell within the range from 70% to 140%, the two drugs were considered to be bioequivalent (Karalis et al. 2012; Liu et al. 2014).

Transcriptome sequencing analysis

Total RNA extraction, library construction and transcriptome sequencing

Total RNA of the high-fat cell model (SO) and the “bioequivalent substance system” administration (N) groups was isolated using TRIzol reagent according to the manufacturer’s protocol, and those groups were treated with DNase I reagent. The integrity and purity of total RNA were assessed. The cDNA libraries were constructed for transcriptome sequencing on Illumina HiSeq. To obtain clean reads, the raw reads were filtered by removing reads containing adapters, reads containing more than 5% N and low-quality reads with a basic mass value of Q ≤ 5. Then, all clean reads were mapped to the reference sequence with HISAT2 software, and the FRKM (expected number of fragments per kilobase of transcript sequence per million base pairs sequenced) was calculated to obtain gene expression levels for each sample. In this experiment, the correlation coefficient was greater than 0.8 for the samples of biological replicates. On the basis of the gene expression level FPKM, the differentially expressed genes between the two samples in the experiment were defined as follows: the absolute value of log₂ (fold change) between two samples was greater than 1, and the value of false discovery rate (FDR) was less than or equal to 0.001. A volcano plot was used to visually represent the overall differential expression distribution of genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs) were implemented by R software (clusterProfiler).

Transcriptome validation of DEGs by qRT–PCR

The RNA of the SO and N groups, which was used for quantitative analysis of DEG expression, was prepared in the same way as for transcriptome sequencing. Then, the concentration, purity and integrity of the RNA were determined. Total RNA solution was used as a template for reverse transcription into cDNA. qRT–PCR was performed using a Roche Light Cycler® 480II Real-time PCR system and the following primer sequences (Table 2).

Table 2. Primer sequence table.

The name of genes	F(5′–3′)	R (5′–3′)
Actb	CGGGAAATCGTGCGTGAC	GGAAGGAAGGCTGGAAGAGTG
Acsbg1	CCTGAAGCTCGGCCTGAAG	CCATGATGACATTGGCGCAG
Traf2	CGACCGTTGGGGCTTTGT	ACTTGGCTTCCAGCTTGGTC
Ldlr	CAGCTACCCCTCGAGACAGA	CACTGTCCGAAGCCTGTTCT
Ppara	TCACCACAGTAGCTTGGAGC	GTGAAAGCGTGTCCGTGATG
Ppargc1a	TGAAGGGTACTTTTCTGCCCC	ATTGCTGATGCTGCTTGCAC

Statistical analysis

SPSS 22.0 software was used for statistical analysis of experimental data, which are expressed as the mean ± standard deviation ($\overline{\text{x}}$ ± s). Statistical analysis was performed using univariate ANNOVA. GraphPad Prism 8.0 software was used for graphic rendering. The model group data are normalized. p < 0.05 was considered to indicate a statistically significant difference.

Results

Pharmacodynamic analysis of the hypolipidemic CAC of JZN based on uniform design

Ratio optimization of the hypolipidemic CAC of JZN

The effects of different combinations on the TG content in lipid-laden HepG2 cells were used to evaluate the hypolipidemic activity of each combination. The specific results are shown in Figure 2. Quadratic polynomial regression analysis was performed to obtain the regression equation as follows: Y = 45.0809381 + 0.18444509496×1 − 0.8972380513×5 + 0.011237521113×5*X5 − 0.0026104358199×6*X6 − 0.0009251838990×1*X2 − 0.0010537014802×1*X4 + 0.0013152148192×2*X4 + 0.0005468619825×4*X6 + 0.003437354688×5*X6, R² = 0.9999, significance level p = 0.0008. The Durbin–Watson statistic of this equation is 1.7106, R > Ra, indicating that the Y regression effect is significant. The results of the data processing are shown in Table 3 and Figure 3.

Figure 2. Effect of compositions N1-N12 on TG content in lipid-laden HepG2 cells (n = 5). Compared with SO group, *p < 0.05, **p < 0.01.

Figure 3. Interaction diagram of various factors. A-X1*X4, B-X2*X4, C-X1*X2, D-X5*X6, E-X4*X6, X1: quercetin-3-O-β-d-glucuronide, X2: hyperoside, X3: rubrofusarin-6-O-β-d-gentiobioside, X4: emodin-8-O-β-d-glucoside, X5: 2-hydroxy-1-methoxyaporphine, X6: armepavine.

Table 3. Data processing results.

Factor	Partial correlation coefficient	t Value	p Value
r(y, X1)=	0.9987	27.6309	0.0001
r(y, X5)=	−0.9995	45.8648	0.0001
r(y, X5*X5)=	0.9996	48.9570	0.0001
r(y, X6*X6)=	−0.9978	21.0655	0.0002
r(y, X1*X2)=	−0.9975	20.1601	0.0003
r(y, X1*X4)=	−0.9982	23.3861	0.0002
r(y, X2*X4)=	0.9979	21.5873	0.0002
r(y, X4*X6)=	0.9636	5.0956	0.0146
r(y, X5*X6)=	0.9965	16.9323	0.0004

According to the data shown in Table 4, the hypolipidemic activity of the combination N6 was the most significant, and its inhibition rate of the TG content in lipid-laden HepG2 cells was the highest (66.15%). Then, the optimal proportion of each advantageous component in the combination was obtained when the Y value reached the maximum. The results showed that the maximum inhibition rate of the advantageous combination was 77.21%, and the best proportion was as follows: quercetin-3-O-β-d-glucuronide: hyperoside: rubrofusarin-6-O-β-d-gentiobioside: emodin-8-O-β-d-glucoside: 2-hydroxy-1-methoxyaporphine: armepavine = 9.32:1:4.45:1.85:2.41:1.92.

Table 4. Inhibitory rate of each combination on TG content in lipid-laden HepG2 cells.

Group	N1	N2	N3	N4	N5	N6	N7	N8	N9	N10	N11	N12
Inhibition rate (%)	49.03	34.92	47.09	47.50	41.79	66.15	52.00	44.49	50.37	33.33	45.63	41.77

Efficacy verification of the CAC

The efficacy of the CAC, which was composed of six advantageous components with the proportion stated above in “Ratio optimization of the hypolipidemic CAC of JZN” (named Nz), was verified on the TG content in lipid-laden HepG2 cells. The TG content was significantly inhibited with Nz treatment in HepG2 cells, and the inhibition rate was 74.03% (Figure 4), which was close to the maximum Y (77.21%).

Figure 4. Effect of Nz on TG content in lipid-laden HepG2 cells (n = 5). Influence of each administration group on the TG content in lipid-laden HepG2 cells (n = 5). SO: hyperlipidemic model group Nz: CAC administration group. G: JZN granules group HEE: HEE of JZN group. Compared with the model group, *p < 0.05, **p < 0.01.

Bioequivalence analysis in vivo

Effect of each administration group on the TC content in the serum of hyperlipidemia mice

As shown in Figure 5(A), the TC content in the serum of model mice was significantly higher than that of control mice. JZN granules, the HEE of JZN and the CAC had inhibitory effects on the TC content in the serum of high-fat mice. Compared with the model group (SO), the inhibition rates of the low-, middle- and high-dose JZN granule groups, the low-, middle- and high-dose HEE JZN groups, and the low-, middle- and high-dose CAC groups on the TC content in the serum of high-fat mice were 45.32, 24.82, 49.08, 30.46, 74.05, 59.95, 11.40, 45.29 and 61.86%, respectively, wherein the low-, and high-dose JZN granule groups (p < 0.01, p < 0.01), the middle-, and high-dose HEE of JZN groups (p < 0.01, p < 0.01), and the low-, middle- and high-dose CAC groups (p < 0.05, p < 0.01, p < 0.01) significantly reduced the TC content in the serum of hyperlipidemia mice.

Figure 5. Influence of each administration group on TC, TG, LDL-C and HDL-C content in serum of hyperlipidemia mice (n = 5). Compared with the model group, *p < 0.05, **p < 0.01.

Effect of each administration group on the TG content in the serum of hyperlipidemia mice

As shown in Figure 5(B), the TG content in the serum of model mice was significantly higher than that of control mice (p < 0.01). Compared with the model group, the inhibition rates of the low-, middle- and high-dose JZN granule groups, the low-, middle- and high-dose HEE of JZN groups, and the low-, middle- and high-dose CAC groups on the TG content in the serum of high-fat mice were 34.48, 56.70, 65.61, 53.11, 63.56, 85.26, 55.20, 83.21 and 105.54%, respectively, wherein the low-, middle- and high-dose JZN granule groups (p < 0.05, p < 0.01, p < 0.01), the low-, middle- and high-dose HEE of JZN groups (p < 0.01, p < 0.01, p < 0.01), and the low-, middle- and high-dose CAC groups (p < 0.01, p < 0.01, p < 0.01) significantly reduced the TG content in the serum of hyperlipidemic mice.

Effect of each administration group on the LDL-C content in the serum of hyperlipidemia mice

As shown in Figure 5(C), the LDL-C content in the serum of model mice was significantly higher than that of control mice. Compared with the model group, the inhibition rates of the low-, middle- and high-dose JZN granule groups, the low-, middle- and high-dose HEE of JZN groups, and the low-, middle- and high-dose CAC groups on the LDL-C content in the serum of high-fat mice were 10.71, 28.32, 38.09, 53.50, 30.91, 29.01, 29.39, 36.57, 39.38%, respectively, wherein the middle-, and high-dose JZN granule groups (p < 0.01, p < 0.01), the low-, middle- and high-dose HEE of JZN groups (p < 0.01, p < 0.01, p < 0.01), and the low-, middle- and high-dose CAC groups (p < 0.01, p < 0.01, p < 0.01) significantly reduced the LDL-C content in the serum of hyperlipidemia mice.

Effect of each administration group on the HDL-C content in the serum of hyperlipidemia mice

As shown in Figure 5(D), the HDL-C content in the serum of model mice was significantly lower than that of control mice (p < 0.01). Compared with the model group, the increased rates of the low-, middle- and high-dose JZN granule groups, the low-, middle- and high-dose HEE of JZN groups, and the low-, middle- and high-dose CAC groups on the HDL-C content in the serum of high-fat mice were 0, 85.34, 88.54, 87.66, 112.95, 166.72, 95.12, 94.65 and 232.67%, respectively, wherein the middle-, and high-dose JZN granule groups (p < 0.05, p < 0.05), the low-, middle- and high-dose HEE of JZN groups (p < 0.05, p < 0.01, p < 0.01), and the low-, middle- and high-dose CAC groups (p < 0.05, p < 0.05, p < 0.01) significantly increased the HDL-C content in the serum of hyperlipidemia mice.

Bioequivalence analysis

The bioequivalence analysis was conducted with the high-dose group of each drug as the main indicator, and the results are shown in Table 5.

Table 5. Bioequivalence analysis of CAC, JZN granules and HEE of JZN in vivo.

	CAC/JZN granules relative efficacy	CAC/HEE of JZN relative efficacy
TC	73.56–190.43%	79.43–129.58%
TG	119.91–214.33%	97.98–182.62%
LDL-C	70.02–159.78%	106.77–216.54%
HDL-C	105.20–468.15%	89.50–220.15%

CAC and JZN granules

As shown in Table 5, within the 90% confidence interval, the CAC was as effective as JZN granules in reducing the TC and LDL-C contents in the serum of hyperlipidemia mice, and the two were bioequivalent. At the same time, combined with the analysis of the data in Figure 5(B) and (D), it can be seen that the efficacy of CAC in decreasing the TG content and increasing the HDL-C content in the serum of hyperlipidemic mice is superior to that of JZN granules, and the bioequivalence relationship between the two is not significant.

CAC and HEE of JZN

In the 90% confidence interval, the efficacy of the CAC in reducing the TC and TG contents in the serum of hyperlipidemia mice was comparable to that of the HEE of JZN, and the two were bioequivalent. Then, the efficacy of the CAC in reducing the LDL-C content and increasing the HDL-C content in the serum of hyperlipidemia mice was significantly better than that of the HEE of JZN, and the bioequivalence relationship between them was not significant.

Bioequivalence analysis in vitro

Effects of each administration group on the TG content in lipid-laden HepG2 cells

The results shown in Figure 4 illustrate that JZN granules and the HEE of JZN can inhibit the TG content in lipid-laden HepG2 cells, and the differences were significant when compared to the model group.

Bioequivalence analysis

According to the hypolipidemic activity results of CAC above in “Efficacy verification of the CAC”, the 90% confidence interval of the relative efficacy value of JZN granules and the HEE of JZN was calculated. The results in Table 6 show that the 90% confidence intervals of the relative efficacy of the CAC and JZN granules fell outside the range from 70% to 143%, which, combined with the analysis of the data in Figure 4, indicated that the inhibition rate of the CAC on the TG content in the lipid-laden HepG2 cells was significantly higher than that of JZN granules, and the hypolipidemic activity of the CAC was better. The 90% confidence interval of the relative efficacy of the CAC and the HEE of JZN only partially fell within the range from 70% to 143%, indicating that the bioequivalence relationship between the CAC and the HEE of JZN was not significant, and the hypolipidemic activity of the CAC was significantly better than that of the HEE of JZN.

Table 6. Bioequivalence analysis of CAC, JZN granules and HEE of JZN in vitro.

Group	Inhibition rate	Relative efficacy
JZN granules	36.21	139.40–302.36%
HEE of JZN	44.63	92.80–294.01%
CAC	74.03	–

Transcriptome sequencing analysis

Transcriptome sequencing and gene expression analysis

To confirm the hypolipidemic mechanisms of the “BSS”, we performed six cDNA libraries: SO (SO-1, SO-2 and SO-3) and N (N-1, N-2 and N-3) groups and sequenced them on the Illumina HiSeq platform.

The FPKM value of gene expression was used to calculate the correlation between three biological replicates in the SO and N groups. The results showed that the squares of Pearson’s coefficients were all higher than 0.95, indicating that the repeatability of the three groups of samples was good and that the transcriptome data obtained in this experiment can accurately and reliably display the situation of differentially expressed genes.

Identification of DEGs

The Possion Dis algorithm was used to identify DEGs with a false discovery rate (FDR) ≤ 0.001 and |log₂ (fold change)| > 1 in the high-fat model group of HepG2 cells treated with the “BSS” of JZN. Then, 164 DEGs were defined in the “BSS” administration vs. SO groups, containing 43 up-regulated genes and 121 down-regulated genes. The volcano plot and MA plot analysis of DEGs are shown in Figure 6(A).

Figure 6. Volcano plot of the differentially expressed genes. Genes upregulated by “bioequivalent substance system” of JZN are shown in red, those downregulated by “bioequivalent substance system” of JZN are in green. (A) was Volcano plot of the differentially expressed genes. Genes upregulated by “bioequivalent substance system” of JZN are shown in red, those downregulated by “bioequivalent substance system” of JZN are in green. (B) was GO analysis of DEGs. A: Biology process B: Molecular function C: Cellular component. (C) was KEGG analysis of DEGs. .

GO and KEGG enrichment analysis

To investigate the biological functions of DEGs after intervention with the hypolipidemic “BSS” of JZN in lipid-laden HepG2 cells, the DEGs were analyzed by GO and KEGG. In the GO enrichment analysis, 137 DEGs, including 129 biological processes, 7 molecular functions and 1 cellular component, were obtained. Ordered by the p value, the top 20 items in each module were selected. The results of GO enrichment are shown in Figure 6(B). In addition, KEGG enrichment analysis showed that 164 differentially expressed genes were involved in 121 pathways. The results were sorted according to the p value from small to large, and the top 20 pathways were selected for mapping, as shown in Figure 6(C). The enrichment pathways related to the lipid-lowering effect included the PPAR signalling pathway (Ppara, Olr1 and Acsbg1), bile secretion pathway (Ldlr and Abcc3), nonalcoholic fatty liver disease pathway (Bid, Traf2 and Ppara) and adipocytokine signalling pathway (Ppara, Acsbg1 and Ppargc1a). These four pathways are shown in Figure 7.

Figure 7. Four pathways related to hypolipidemia (Figure was created with BioRender. com).

The pathways related to hypolipidemia

PPAR signalling pathway

The DEGs involved in this signalling pathway were Ppara, Olr1 and Acsbg1. After intervention with the “BSS” in lipid-laden HepG2 cells, the expression of the Olr1 gene in the PPAR signalling pathway was down-regulated, and the expression of the Ppara and Acsbg1 genes was up-regulated. The hypolipidemic mechanism of the “BSS” may be concluded as follows: (1) the expression of the Olr1 gene in the PPAR signalling pathway was down-regulated, then the uptake of free fatty acids and the content of TG was reduced (Chui et al. 2005; Guo et al. 2019; Qiao et al. 2020), thereby exerting a hypolipidemic effect and delaying the progression of atherosclerosis; (2) up-regulation of Acsbg1 gene expression promoted the β-oxidation of fatty acids and provided energy to the body, while reducing the accumulation of fatty acids and lipid production to regulate lipid metabolism (Steinberg et al. 2000; Pei et al. 2003; Qing et al. 2018) and exert a hypolipidemic effect and (3) up-regulation of the expression of the Ppara gene promoted β-oxidation of fatty acids and cholesterol metabolism, exerting a hypolipidemic effect by reducing the content of cholesterol and fatty acids.

Nonalcoholic fatty liver disease pathway

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of diseases caused by the accumulation of large amounts of fat in the liver, ranging from simple steatosis to more severe steatohepatitis with liver inflammation as well as fibrosis (called nonalcoholic steatohepatitis (NASH)). The hypolipidemic “BSS” of JZN could down-regulate the expression of Bid and Traf2 genes in the NAFLD pathway and up-regulate the expression of Ppara genes after intervention in lipid-laden HepG2 cells. The hypolipidemic mechanism may be as follows: (1) gene Bid was reduced, which inhibits the expression of downstream Casp3 and Casp7 genes and inhibits apoptosis, thereby reducing hepatocyte damage (Molpeceres et al. 2007); (2) down-regulation of the gene Traf2 could reduce the production of multiple downstream inflammatory factors, thereby alleviating the development of steatohepatitis and protecting the liver and (3) up-regulation of the expression of the Ppara gene reduced the content of fatty acids in the liver and alleviated the symptoms of NAFLD.

Bile secretion pathway

Bile is an important secretion for the digestion and absorption of fats and fat-soluble vitamins in the small intestine. In addition, bile is an important means of eliminating superfluous cholesterol and many waste products, bilirubin, drugs and toxic compounds. After intervention with the “BSS” in lipid-laden HepG2 cells, the expression of the Ldlr and Abcc3 genes in the bile secretion pathway was up-regulated. The hypolipidemic mechanism may be concluded as follows: (1) the expression of the Ldlr gene was increased, which simultaneously enhanced the binding to LDL-C and then promoted bile acid metabolism and accelerated the metabolism of cholesterol and (2) the gene Abcc3 was up-regulated, which alleviated the symptoms of cholestasis by removing excess bile acid from hepatocytes.

Adipocytokine signalling pathway

Adipose tissue has a broad secretion profile and can secrete a large number of cytokines called adipocytokines. Common cytokines include adiponectin (ADPN), leptin (LEP), TNFα, IL6 and angiogenin-like protein 4 (ANGPT4) (Gong et al. 2007). These factors play an important role in insulin signal transduction, lipid metabolism disorders and cardiovascular diseases. The hypolipidemic “BSS” of JZN can have a hypolipidemic effect by interfering with the adipocytokine signalling pathway after intervention in HepG2 cells. The mechanism may be concluded as follows: (1) the expression of the Traf2 gene was down-regulated, and the expression of downstream proteins, including mTOR, JNK and IKK, was reduced, thereby down-regulating the inhibition of IRS1 tyrosine phosphorylation by serine phosphorylation and reducing insulin resistance and (2) the Ppara and Ppargc1a genes were up-regulated, which up-regulated the expression of CPT1 protein, promoted β-oxidation of fatty acids and reduced lipid accumulation.

Confirmation of DEGs by qRT–PCR

To validate the expression of DEGs sourced from the transcriptome, five special DEGs that repeatedly recurred in several pathways were selected. After intervention with the hypolipidemic “BSS” of JZN, the expression of these genes was determined by qRT–PCR. The results (Figure 8) show that the expression of DEGs from qRT–PCR was consistent with the results of transcriptome sequencing, indicating that the transcriptome sequencing results were reliable.

Figure 8. Effect of “bioequivalent substance system” of JZN on mRNA expression in lipid-laden HepG2 cells (n = 3). Compared with SO group, *p < 0.05, **p < 0.01.

Discussion

Hyperlipidemia is a pathological condition that when blood lipids increase, a large amount of lipid is deposited on the blood vessel wall, which can directly cause a series of diseases, especially atherosclerosis, that endanger the well-being of patients. TCM compound has unique advantages in the treatment of hyperlipidemia: abundant resources, definite curative effects, flexible formula, less adverse effects and a variety of lipid-lowering pathways, so it has a good application prospect (Zhang et al. 2015). The selection of research ideas and the application of experimental models are very diverse when studying the mechanism of hypolipidemic action of drugs.

In the present study, the research idea of the TCM compound “bioequivalent substance system” was proposed. Bioequivalence assessment was initially applied to determine whether two drugs are bioequivalent by calculating the 90% confidence interval range of the ratio of their pharmacodynamic parameters (Karalis et al. 2012). Subsequently, it was applied to the evaluation of the efficacy of TCM (compound) and combination of advantageous components which screened from TCM (compound). If the 90% confidence interval of relative efficacy fell within the range from 70% to 140%, the combination of advantageous components and the TCM compound was considered to be bioequivalent (Liu et al. 2014).

According to the research idea of TCM compound “bioequivalent substance system”, the ratio of the hypolipidemic CAC of JZN was determined. Then, a pharmacodynamic study of the CAC of JZN was carried out using animal experiments and cell experiments. In this study, Triton WR-1339 was used as an acute model for preliminary and rapid screening of hypolipidemic agents. The hyperlipidemia model induced by Triton WR-1339 could cause endogenous lipid disorders and liver inflammation in mice with a short modelling time (Schurr et al. 1972; Li et al. 2019; Eimad Dine et al. 2020). At the same time, compared with the control group, TC, TG and LDL-C levels in plasma of hyperlipidemia rats were significantly increased, HDL-C levels in plasma of hyperlipidemia rats were significantly decreased, which were consistent with the results reported previously (Eimad Dine et al. 2020). Our pharmacological data showed that CAC can significantly reduce the content of TG, TC and LDL-C in hyperlipidemia rats’ plasma, and the bioequivalence relationship between the CAC, JZN granules and the HEE of JZN was carried out using the contents of TC, TG and LDL-C in the serum of mice as indicators. The results confirmed that the three have a certain bioequivalent relationship. Second, in cell experiments, SO was used to induce a hyperlipidemia model of HepG2 cells, and the bioequivalence relationship between the CAC, JZN granules and the HEE of JZN was studied with the TG content as an index. The results showed that the hypolipidemic activity of the CAC was better than those of JZN granules and the HEE of JZN, and the three did not have an absolute bioequivalence relationship. There is no absolute bioequivalence according to the evaluation standard, so it is speculated that “BSS” has some advantages in lowering TG than JZN granules, perhaps because of the result after purification.

Meanwhile, the composition of chemical components of TCM compound was complex, the clinical therapeutic efficacy of TCM compounds was the result of the whole and synergistic effect of several active components, rather than the linear addition of a few chemical components (Zhang and Feng 2019). Nowadays, the concept of phytochemical synergy plays an important role in promoting human health and preventing hyperlipidemia (Liu et al. 2022). On the basis of the complexity of the regulatory network of lipid metabolism, synergistic therapy has shown to be superior to monotherapy (Efferth and Koch 2011). For example, a combination of quercetin, hyperoside, rutin and chlorogenic acid (6:9:2:1) could increase the inhibitory activity of HMG-CoA reductase by 58.9% (Gao et al. 2021). The mixture of quercetin and kaempferol (1:1) could improve LDL-C uptake more effectively in HepG2 cells (Yusof et al. 2018).

Therefore, in order to clarify the mechanism of TCM compounds, it is necessary to further analyze and study the synergistic effect among active components. In this study, in order to clarify how the six advantageous components, exert the hypolipidemic effects synergistically, further research and explanation from the aspects of the affinity between components and the synergism of pharmacodynamic characterization in vivo is needed. In addition, based on the previous research of our group, the six advantageous components could all enter the blood as prototypes, and there are also aglycone metabolites and aglycone secondary metabolites of glycoside components such as quercetin-3-O-β-d-glucuronide, hyperoside, etc. (Zhang et al. 2021).

Despite the advances, our study had several limitations, therefore, it is necessary to further investigate the effects of glycogen metabolites of advantageous components on the hypolipidemic in combination with other methods such as metabolomics and proteomics approaches.

Conclusions

A systematic pharmacodynamic study and evaluation of the hypolipidemic “bioequivalent substance system” of JZN was carried out, and finally the substance basis of JZN lowering blood lipid was initially clarified. Together, these findings provided new feasible research ideas and methods for the elucidation of the pharmacodynamic material basis and the study of the mechanism of natural medicine with complex components.

Author contributions

Yu-miao Li: Conceptualization, Methodology, Investigation, Software. Yan Zhang: Data curation, Investigation. Yu Zhang: Investigation, Writing-Original draft preparation. Tian-feng Lin: Formal analysis, Supervision. Yan-yan Gao: Data curation, Formal analysis. Yuan Cai, Chang Zhou and Le-Yi Yang: Writing- Reviewing and Editing. Bin Liu: Project administration. Shi-fen Dong and Yan-yan Jiang: Project administration, designing the study.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Beijing Municipal Natural Science Foundation [grant number 7232287].