| Literature DB >> 35668952 |
Jiacheng Ji1,2, Shuo Zhang3, Minyan Yuan1,2, Min Zhang1, Li Tang1,2, Pengjiao Wang1, Yujie Liu1,2, Changqian Xu1,2, Peng Luo4, Xiuli Gao1,2,4.
Abstract
Hyperlipidemia endangers human health and has become a significant public health problem. This study aimed to investigate the mechanism of the hypolipidemic effects of Fermented Rosa roxburghii Tratt juice (FRRT) on hyperlipidemic rats and a new hypolipidemic intervention strategy was disclosed. The study revealed 12 weeks FRRT treatment significantly decreased the body weight, total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-c), while high-density lipoprotein cholesterol (HDL-c) increased. We integrated the 16S rDNA sequencing and metabolomic profiling to evaluate the changes in the gut microbiota and metabolites. Significant changes in microbial composition accompanied marked changes in 56 feces metabolites. The results showed that FRRT could decrease the ratio of Firmicutes to Bacteroidetes, while increase the abundance of some bacterial genera (Prevotella, Paraprevotellaceae_Prevotella, Ruminococcus, Oscillospira). Metabolomics analysis displayed that the metabolisms of bile acid, amino acid and lipid were significantly affected by FRRT. Correlation analysis suggest that the reductions in serum lipids by FRRT are associated with the gut microbial community and their associated metabolites (amino acid metabolites, bile acid metabolites, and lipid metabolites). This study confirmed FRRT could be used as a new dietary and therapeutic strategy to dyslipidemia by improving the gut microbiota dysbiosis, metabolomic disorders and regulating the dyslipidemia. Our study also extended the understanding of the relationship between gut microbiota, metabolites, and lipid-lowering functions.Entities:
Keywords: fermented rosa roxburghii tratt juice; gut microbiota; hyperlipidemia; lipid metabolism; metabolomic
Year: 2022 PMID: 35668952 PMCID: PMC9164371 DOI: 10.3389/fphar.2022.883629
Source DB: PubMed Journal: Front Pharmacol ISSN: 1663-9812 Impact factor: 5.988
FIGURE 1FRRT improves dyslipidemia in HFD-induced hyperlipidemic Rats. (A) Schematic diagram of FRRT treatment. (B) Rats’ body weight at weeks 0, 8, and 20. Serum TC (C), TG (D), LDL-c (E), and HDL-c (F) levels were measured using commercial kits. Data are presented as mean ± SEM (n = 6). *p < 0.05, **p < 0.01 compared with HFD group.
FIGURE 2FRRT improves liver function damage in hyperlipidemic rats. (A) H&E staining images and morphology of Liver. (B) Changes in the liver index. Serum AST (C) and ALT (D) levels were measured using commercial kits. Data are presented as mean ± SEM (n = 6). *p < 0.05, **p < 0.01 compared with HFD group.
FIGURE 3The alteration of the gut microbiome in HFD rats. (A) The a-diversity indexes of Chao1, Shannon, Simpson, and observed-species of gut microbiota between HFD and HFD + FRRT rats. (B) Unweighted based PCoA and weighted UniFrac based PCoA between gut bacterial communities of HFD and HFD + FRRT rats. p values were determined by the ADONIS test. Differential abundances of genera were determined by the Wilcoxon rank-sum test and Mann-Whitney U test. (C) The average percent of community abundance on phylum level in HFD and HFD + FRRT groups. (D) Relative abundance of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and the Bacteroidetes/Firmicutes abundance ratio (E) in fecal microbiota between HFD and HFD + FRRT. (F) The LEfSe analysis of the gut microbiota differed between HFD and HFD + FRRT groups. (G) The effect of the gut microbiota modifications on predicted functional metabolic pathways obtained from PICRUSt analysis of 16S rDNA sequencing data.
FIGURE 4Metabolic profile as well as multivariate/Univariate analysis and metabolic pathway of metabolites between HFD and HFD + FRRT group. (A) PCA plots with the scores of the first two principal components based on 160 metabolites. (B) OPLS-DA plots with the scores of the first two principal components. (C) Venn Plot of differential metabolites, by getting union/intersection of the differential metabolites from univariate statistics and multi-dimensional statistics. (D) Heatmap analysis of potential biomarkers, the concentration value is converted to Z-score by standardized Z-score transformation.
FIGURE 5The Spearman’s correlation analysis of gut microbiota, metabolic parameters, and metabolites. (A) Correlations of 56 altered metabolites and five metabolic parameters in HFD and HFD + FRRT using Spearman correlation analysis. (B) Correlations of 56 altered metabolites and 20 altered gut microbiota in HFD and HFD + FRRT using Spearman correlation analysis.