Effect of methanol extract of Salviae miltiorrhizae Radix in high-fat diet-induced hyperlipidemic mice

Abstract

Background: The dried root of Salvia miltiorrhiza, Salviae miltiorrhizae Radix (SR), is one of the most popular medicinal herbs in Asian countries such as China and Korea. In Asian traditional medicine, SR is considered to have a bitter flavor, be slightly cold in nature, and exert therapeutic actions in the heart and liver meridians. Thus, SR has been used to control symptoms related to cardiovascular diseases. Hyperlipidemia is recognized as the main cause of cerebrovascular and heart diseases; consequently, therapeutic strategies for hyperlipidemia have been widely studied. In this study, the effects and molecular targets of methanol extract of SR (SRme) in hyperlipidemic mice were investigated.

Methods: High-fat diet was fed to mice to induce hyperlipidemia, and measurement of blood cholesterol and triglycerides were conducted to evaluate the effect of SRme on hyperlipidemic mice, and gene expression in mice liver was analyzed to identify key molecules which could be potential targets for developing anti-hyperlipidemic herbal medicines.

Results: There was no significant effect on the body weight gain of hyperlipidemic mice, but the triglyceride content in blood was significantly reduced by the administration of SRme to hyperlipidemic mice. Proteins such as minichromosome maintenance (Mcm) family which play a key role in DNA replication were identified as molecular targets in the amelioration of hyperlipidemia.

Conclusions: SRme ameliorated hyperlipidemia in high-fat diet fed mice by inhibiting increase of blood serum level of triglycerides. And several proteins such as Mcm proteins were deduced to be molecular targets in treating hyperlipidemia.

Keywords: Cardiovascular diseases; Hyperlipidemia; Salviae miltiorrhizae Radix.

Fig. 1

Design of hyperlipidemia induction. The mice were fed a normal diet or high-fat diet, based on their allocated group, for 6 weeks. SRme mixed with high-fat diet was fed to the mice in the SRme administration group (SRG) for the final 2 weeks. NOR: naive mice (n = 8), HFD: hyperlipidemic mice (n = 8), SRG: SRme-treated hyperlipidemic mice (n = 8)

Fig. 2

Effects of SRme on body weight in hyperlipidemic mice. Body weight was measured every 2 weeks. NOR: naive mice (n = 8), HFD: hyperlipidemic mice (n = 8), SRG: SRme-treated hyperlipidemic mice (n = 8). The values are presented as the mean ± SD

Fig. 3

Effects of SRme on the levels of total cholesterol, HDL-cholesterol, and triglycerides in hyperlipidemic mice. The levels of total cholesterol (a), HDL-cholesterol (b), and triglycerides (c) in serum were measured spectrophotometrically. NOR: naive mice (n = 8), HFD: hyperlipidemic mice (n = 8), SRG: SRme-treated hyperlipidemic mice (n = 8). The values are presented as the mean ± SD. ^# P < 0.05, ^### P < 0.001 vs NOR; *P < 0.05 in comparison with CON

Fig. 4

Effects of SRme on lipid peroxidation levels in hyperlipidemic mice. Lipid peroxidation in liver tissues was measured spectrophotometrically. NOR: naive mice (n = 8), HFD: hyperlipidemic mice (n = 8), SRG: SRme-treated hyperlipidemic mice (n = 8). The values are presented as the mean ± SD

Fig. 5

Effects of SRme on gene expression patterns in liver tissue of hyperlipidemic mice. To identify the genes using the quantitative analysis and expression clustering, MeV ver. 4.0 software was used. Genes colored red were upregulated compared with NOR mice (N); genes colored green were downregulated compared with NOR mice. N: naive mice, H: hyperlipidemic mice, S: SRme-treated hyperlipidemic mice

Fig. 6

Line plot of alteration of gene expression liver tissues in hyperlipidemic mice. The resultant SRme-responsive genes are plotted as log values for each differentially expressed gene. NOR: naive mice, HFD: hyperlipidemic mice, SRG: SRme-treated hyperlipidemic mice

Fig. 7

Protein network analysis by STRING software. The information about the restoration of gene expression by SRme administration in hyperlipidemic mice was uploaded into STRING software (version 9.1) for the analysis of the interactions of related proteins and protein–protein interactions. Network nodes represent proteins and edges represent protein–protein associations. Light blue colored edges mean known interactions imported from curated databases; purple, experimentally determined. Green colored edges mean predicted interactions between genes of neighborhood; red, gene fusions; dark blue, gene co-occurrence