The Concentration of Organic Acids in Cranberry Juice Modulates the Gut Microbiota in Mice

Abstract

A daily consumption of cranberry juice (CJ) is linked to many beneficial health effects due to its richness in polyphenols but could also awake some intestinal discomforts due to its organic acid content and possibly lead to intestinal inflammation. Additionally, the impact of such a juice on the gut microbiota is still unknown. Thus, this study aimed to determine the impacts of a daily consumption of CJ and its successive deacidification on the intestinal inflammation and on the gut microbiota in mice. Four deacidified CJs (DCJs) (deacidification rates of 0, 40, 60, and 80%) were produced by electrodialysis with bipolar membrane (EDBM) and administered to C57BL/6J mice for four weeks, while the diet (CHOW) and the water were ad libitum. Different parameters were measured to determine intestinal inflammation when the gut microbiota was profiled. Treatment with a 0% DCJ did not induce intestinal inflammation but increased the gut microbiota diversity and induced a modulation of its functions in comparison with control (water). The effect of the removal of the organic acid content of CJ on the decrease of intestinal inflammation could not be observed. However, deacidification by EDBM of CJ induced an additional increase, in comparison with a 0% DCJ, in the Lachnospiraceae family which have beneficial effects and functions associated with protection of the intestine: the lower the organic acid content, the more bacteria of the Lachnospiraceae family and functions having a positive impact on the gut microbiota.

Keywords: cranberry juice; electrodialysis with bipolar membrane; gut microbiota; intestinal inflammation; mice model; organic acid removal; organic acids; polyphenols.

Figure 1

Effect of the deacidification of CJ on the (A) food intake, (B) total food intake, (C) body weight, and (D) total body weight gain in mice in comparison with a control (water). Results are presented as the mean ± SEM and n = 8 for A and B and n = 12 for C and D. ANOVAs combined with a Tukey’s test were done on A–D. For (A,C), the absence of letter means that there is no statistically significant effect at a probability level of 0.05. For (B,D), a different letter means that the results are significantly different at a probability level of 0.05.

Figure 2

Effect of the deacidification of CJ on the expression of Tnf, Il22, and Muc2. Results are presented in % of the gene expression ± SEM compared to the control (n = 12). A Student’s t test was performed to evaluate the differences between the treated groups and the control group.

Figure 3

Impact of the administration of the different DCJs on (A) the global structure of the microbiota after 4 weeks of treatment, reflected by the principal coordinate analysis (PcoA) on the unweighted UniFrac (Unique Fraction) distance matrix. (B) The alpha-diversity of the gut microbiota before (T0) and after the end (T4) of the experiment, as measured with the Shannon index. (C) Relative abundance of taxa at the genus level.

Figure 4

Administration of the different DCJs is associated with changes in the gut microbial of mice in comparison with the control (water). The linear discriminant analysis (LDA) effect size was calculated in order to explore the taxa within genus levels that more strongly discriminated between the gut microbiota of mice treated with (A) water (white) and 0% DCJ (dark), (B) water and 40% DCJ (light grey), (C) water and 60% DCJ (medium grey), and (D) water and 80% DCJ (dark grey). Families followed by the label ‘-g’ indicate unidentified genera.

Figure 5

Administration of the different DCJs alter metabolic pathways in the gut microbiota of mice in comparison with the control (water). The linear discriminant analysis (LDA) effect size was calculated in order to explore the microbial functions that more strongly discriminated between the gut microbiota of mice treated with (A) water (white) and 0% DCJ (dark), (B) water and 40% DCJ (light grey), (C) water and 60% DCJ (medium grey), and (D) water and 80% DCJ (dark grey). bioS: biosynthesis, SP: superpathway.

Figure 5

Administration of the different DCJs alter metabolic pathways in the gut microbiota of mice in comparison with the control (water). The linear discriminant analysis (LDA) effect size was calculated in order to explore the microbial functions that more strongly discriminated between the gut microbiota of mice treated with (A) water (white) and 0% DCJ (dark), (B) water and 40% DCJ (light grey), (C) water and 60% DCJ (medium grey), and (D) water and 80% DCJ (dark grey). bioS: biosynthesis, SP: superpathway.

Figure 6

Level of deacidification of CJ is associated with changes in the gut microbial of mice. The linear discriminant analysis (LDA) effect size was calculated in order to explore the taxa within genus levels that more strongly discriminated between the gut microbiota of mice treated with (A) 0% DCJ (dark) and 40% DCJ (light grey), (B) 0% DCJ and 60% DCJ (medium grey), and (C) 0% DCJ and 80% DCJ (dark grey). Families followed by the label ‘-g’ indicate unidentified genera.

Figure 7

Level of deacidification of CJ is associated with altered metabolic pathways in the gut microbiota of mice. The linear discriminant analysis (LDA) effect size was calculated in order to explore the microbial functions that more strongly discriminated between the gut microbiota of mice treated with (A) 0% DCJ (dark) and 40% DCJ (light grey), (B) 0% DCJ and 60% DCJ (medium grey), and (C) 0% DCJ and 80% DCJ (dark grey). bioS: biosynthesis, SP: superpathway.

Figure 8

Graphical representation of the links between the pathways and SP induced by (A) the 60% DCJ and (B) the 80% DCJ in the gut microbiota of the treated mice in comparison with a 0% DCJ. Filled frames represent degradation (light green), sugar biosynthesis (dark yellow), pentapeptide biosynthesis (dark green), lipid biosynthesis (brown), generation of precursor metabolites and energy (blue), and polyprenyl biosynthesis (light yellow).