Impact of a Whey Protein Hydrolysate Treated by Electrodialysis with Ultrafiltration Membrane on the Development of Metabolic Syndrome and the Modulation of Gut Microbiota in Mice

Abstract

The development of Metabolic Syndrome (MetS) affects a large number of people around the world and represents a major issue in the field of health. Thus, it is important to implement new strategies to reduce its prevalence, and various approaches are currently under development. Recently, an eco-friendly technology named electrodialysis with ultrafiltration membrane (EDUF) was used successfully for the first time at a semi-industrial scale to produce three fractions concentrated in bioactive peptides (BPs) from an enzymatically hydrolyzed whey protein concentrate (WPC): the initial (F1), the final (F2) and the recovery fraction (F3), and it was demonstrated in vitro that F3 exhibited interesting DPP-IV inhibitory effects. Therefore, the present study aimed to evaluate the effect of each fraction on in vivo models of obesity. A daily dose of 312.5 mg/kg was administered to High Fat/High Sucrose diet (HFHS) induced C57BL6/J mice for eight weeks. The physiological parameters of each group and alterations of their gut microbiota by the fractions were assessed. Little effect of the different fractions was demonstrated on the physiological state of the mice, probably due to the digestion process of the BP content. However, there were changes in the gut microbiota composition and functions of mice treated with F3.

Keywords: bioactive peptides; digestion; electrodialysis with ultrafiltration membrane; electromembrane extraction; gut microbiota; whey protein hydrolysate.

Figure 1

Impact of fraction supplementation in mice fed with HFHS diet on glucose homeostasis. Glycemic variation during ITT (A). Glycemic variation during OGTT (B). Insulinemic variation during OGGT (C). * p < 0.05 chow vs. HFHS, ** p < 0.01 chow vs. HFHS, *** p < 0.001 chow vs. HFHS.

Figure 2

Effect of 8-week treatment with fraction supplementation to HFHS diet mice on alpha and beta diversity (n = 12). Alpha diversity was calculated with the Shannon index (A). Beta diversity was represented by principal component analysis (PCA) based on the Aitchison distance matrix (B). *** p < 0.001 chow vs. HFHS, & p < 0.05 HFHS vs. F3.

Figure 3

The bacterial (log10-transformed) abundance of the microbiota community after 8 weeks of treatment represented by a heatmap. The presence of ‘p,’ ‘c,’ ‘o,’ ‘f,’ and ‘g’ at the end of the taxon denotes an unclassified phylum, class, order, family, and genus, respectively.

Figure 4

Linear discriminant analysis (LDA) combined with linear effect size measurements (LEfSe) was used to explore the taxa that enable discrimination between (A) chow vs. HFHS, (B) HFHS vs. F1, (C) HFHS vs. F2 and (D) HFHS vs. F3.

Figure 5

Prediction of the functional pathways from 16S gene-based data performed using PICRUSt2. LDA scores were used for differentially abundant functional pathways between (A) chow vs. HFHS, (B) HFHS vs. F1, (C) HFHS vs. F2 and (D) HFHS vs. F3.

Figure 6

Schematic representation of the digestion process of BPs. Step 1. Arrival of the BPs in the intestinal lumen. Step 2. Degradation of the BPs by peptidases. Step 3. Absorption of the metabolites through the epithelia to blood vessels.