Polyphenols and Polysaccharides from Morus alba L. Fruit Attenuate High-Fat Diet-Induced Metabolic Syndrome Modifying the Gut Microbiota and Metabolite Profile

Meixia Wan, Qing Li, Qianya Lei, Dan Zhou, Shu Wang, Meixia Wan, Qing Li, Qianya Lei, Dan Zhou, Shu Wang

Abstract

Morus alba L. fruit, a medicinal and edible fruit in East Asia, showed potential health-promoting effects against metabolic syndrome (MetS). However, both the protective effects and mechanisms of different fractions extracted from Morus alba L. fruit against MetS remain unclear. Additionally, the gut microbiota and its metabolites are regarded as key factors in the development of MetS. This study aimed to investigate the potential role of polyphenols and polysaccharides derived from Morus alba L. fruit against MetS in high-fat diet (HFD)-fed mice, individually and in combination, focusing on remodeling effects on gut microbiota and metabolite profiles. In the study, polyphenols and polysaccharides derived from Morus alba L. fruit improved the traditional pharmacodynamic parameters of MetS, including reductions in body weight (BW) and fat accumulation, improvement in insulin resistance, regulation of dyslipidemia, prevention of pathological changes in liver, kidney and proximal colon tissue, and suppressive actions against oxidative stress. In particular, the group treated with polyphenols and polysaccharides in combination showed better efficacy. The relative abundance of beneficial bacterial genera Muribaculum and Lachnospiraceae_NK4A136_group were increased to various degrees, while opportunistic pathogens such as Prevotella_2, Bacteroides, Faecalibacterium and Fusobacterium were markedly decreased after treatments. Moreover, fecal metabolite profiles revealed 23 differential metabolites related to treatments with polyphenols and polysaccharides derived from Morus alba L. fruit, individually and in combination. Altogether, these results demonstrated that polyphenols and polysaccharides derived from Morus alba L. fruit attenuated MetS in HFD-fed mice, and improved the gut microbiota composition and fecal metabolite profiles.

Keywords: Morus alba L. fruit; gut microbiota; metabolic syndrome; polyphenols; polysaccharides; untargeted fecal metabolomics.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Identification analysis of MFP. (A) HPLC analysis of cyanidin-3-O-glucoside and rutin. (B) HPLC analysis of MFP.
Figure 2
Figure 2
The improvement of obesity and fat accumulation in HFD-fed mice after MFP, MFS and MFPS treatments. (A) Changes in body weight. (B) Body weight gain. (C) Epididymal fat index. (D) Epididymal adipose tissue and H&E-stained photomicrograph of epididymal adipose tissue (original magnification × 200). Data are expressed as the mean ± standard deviation (n = 12). # p < 0.05, ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 3
Figure 3
Prevention of HFD-induced hepatic fat deposition and oxidative stress after MFP, MFS and MFPS treatments (A) Liver index. (B,C) Liver function-related transaminase (AST and ALT in serum). (D) Macroscopic appearance of liver, H&E-stained photomicrograph of liver tissue and oil red O-stained photomicrograph of liver tissue (original magnification × 200). (E) Malondialdehyde (MDA). (F) Superoxide dismutase (SOD). (G) Glutathione peroxidase (GSH-Px). Black arrows indicate lipid droplets, blue arrows indicate steatosis with edema, and yellow arrows indicate inflammatory cell infiltration. Data are expressed as the mean ± standard deviation (n = 12). # p < 0.05, ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 4
Figure 4
The improvement of glucose tolerance and insulin resistance (IR) after MFP, MFS and MFPS treatments. (A) Oral glucose tolerance test (OGTT). (B) Area under the curve (AUC) of OGTT. (C) Fasting blood glucose. (D) Fasting serum insulin. (E) Homeostatic model assessment-insulin resistance (HOMA-IR) index. Data are expressed as the mean ± standard deviation (n = 12). ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 5
Figure 5
The improvement of hyperlipemia in HFD-fed mice after MFP, MFS and MFPS treatments. (A) Levels of total triglyceride (TG). (B) Total cholesterol (TC). (C) Low-density lipoprotein cholesterol (LDL-C). (D) Very low-density lipoprotein cholesterol (VLDL-C). (E) High-density lipoprotein cholesterol (HDL-C). Data are expressed as the mean ± standard deviation (n = 12). ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 6
Figure 6
The improvement of renal injury phenotypes in HFD-fed mice after MFP, MFS and MFPS treatments. (A) Serum creatinine (Scr). (B) Serum uric acid (SUA). (C) Blood urea nitrogen (BUN). (D) The renal index (%). (E) H&E-stained photomicrograph of kidney tissue (original magnification × 200). Black arrows indicate diffuse swelling. Data are expressed as the mean ± standard deviation (n = 12). # p < 0.05, ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 7
Figure 7
Effects of MFP, MFS and MFPS on inflammatory mediators and colonic lesion phenotypes in HFD-fed mice. Serum levels of pro-inflammatory cytokines by ELISA, including interleukin-1β (IL-1β) (A), interleukin-6 (IL-6) (B), tumor necrosis factor-α (TNF-α) (C). (D) The renal index (%). (E)The colon tissues and H&E-stained photomicrograph of colon tissue (Original magnification × 200). Black arrows indicate inflammatory cell infiltration, green arrows indicate colonic muscle layer. Data were expressed as the mean ± standard deviation (n = 12). ## p < 0.01 vs. NCD group; * p < 0.05, ** p < 0.01 vs. HFD group.
Figure 8
Figure 8
The improvement of gut microbiota dysbiosis in HFD-fed mice after MFP, MFS and MFPS treatments (n = 4). (A) Boxplots for Chao 1 index and Simpson index. (B) OPLS-DA plot. (C) PCoA plot. (D) Relative abundance of the main phyla and genera of the intestinal microbiota in different groups. (E) Spearman’s correlation analysis between gut microbiota genera and pharmacodynamic parameters (The color scale represents the Spearman r value, with red and blue indicating positive and negative correlations, respectively. # p < 0.05, * p < 0.05 and ** p < 0.01). (F) LEfSe (linear discriminant analysis effect size) (LDA score >3.5).
Figure 9
Figure 9
The changes in fecal metabolites in HFD-fed mice after MFP, MFS and MFPS treatments (n = 4). (A) PCA score plot. (B) Cluster heatmap (color bars showing green to red indicate the relative content of metabolites, with red representing high expression and green representing low expression). (C) Pathway enrichment analysis (HFD vs. NCD). (D) Spearman’s correlation analysis between gut microbiota and different metabolites (color scale represents the Spearman r value, with red and blue indicating positive and negative correlations, respectively. * p < 0.05, ** p < 0.01). (E) Spearman’s correlation analysis between pharmacodynamic parameters and different metabolites (color scale represents the Spearman r value, with pink and green indicating positive and negative correlations, respectively. * p < 0.05, ** p < 0.01).

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