Short-chain fructo-oligosaccharides modulate intestinal microbiota and metabolic parameters of humanized gnotobiotic diet induced obesity mice

Abstract

Prebiotic fibres like short-chain fructo-oligosaccharides (scFOS) are known to selectively modulate the composition of the intestinal microbiota and especially to stimulate Bifidobacteria. In parallel, the involvement of intestinal microbiota in host metabolic regulation has been recently highlighted. The objective of the study was to evaluate the effect of scFOS on the composition of the faecal microbiota and on metabolic parameters in an animal model of diet-induced obesity harbouring a human-type microbiota. Forty eight axenic C57BL/6J mice were inoculated with a sample of faecal human microbiota and randomly assigned to one of 3 diets for 7 weeks: a control diet, a high fat diet (HF, 60% of energy derived from fat)) or an isocaloric HF diet containing 10% of scFOS (HF-scFOS). Mice fed with the two HF gained at least 21% more weight than mice from the control group. Addition of scFOS partially abolished the deposition of fat mass but significantly increased the weight of the caecum. The analysis of the taxonomic composition of the faecal microbiota by FISH technique revealed that the addition of scFOS induced a significant increase of faecal Bifidobacteria and the Clostridium coccoides group whereas it decreased the Clostridium leptum group. In addition to modifying the composition of the faecal microbiota, scFOS most prominently affected the faecal metabolome (e.g. bile acids derivatives, hydroxyl monoenoic fatty acids) as well as urine, plasma hydrophilic and plasma lipid metabolomes. The increase in C. coccoides and the decrease in C. leptum, were highly correlated to these metabolic changes, including insulinaemia, as well as to the weight of the caecum (empty and full) but not the increase in Bifidobacteria. In conclusion scFOS induce profound metabolic changes by modulating the composition and the activity of the intestinal microbiota, that may partly explain their effect on the reduction of insulinaemia.

Figure 1. Workflow of the data analysis.

Figure 2. Composition of the faecal microbiota of human microbiota-associated mice after 7 weeks of feeding.

The heatmap represents the relative presence of the different bacterial groups when mice received either the control (C) or the high-fat (HF) or the isocaloric high-fat containing 10% of scFOS (HF-scFOS) diets (dark box, increase; light box decrease). A different letter in each row indicates statistically significant difference (Fisher PLSD post-hoc test after ANOVA). Lab 158-C: Lactobacillus - Enterococcus group; Clep 1156-C Clostridium leptum group; Muc 1437 Akkermansia muciniphila species; Ato291-C: Atopobium cluster; Enter 1432-C: Enterobacteria; Bif 164-C: Bifidobacterium genus; Ecyl-387: Erysipelotrichi group; Erec 482-C: Clostridium coccoides group; Bac303-C: Bacteroides – Prevotella group.

Figure 3. OGTT performed in mice after feeding a control or high fat diet with or without scFOS.

A: blood glucose AUC; B: blood insulin sampled at 20 and 60 min after oral load of glucose. ^a,b values with different letters are significantly different (p<0.05). HF = high fat diet, HF-scFOS = isocaloric high fat diet +10% scFOS.

Figure 4. Partial least square discriminant analysis of metabolome of human microbiota-associated mice fed a control or high fat diet with or without scFOS.

Class assignment was 100% using the classification list algorithm of SIMCA. Group variance explained R2Y = 94% of observed variance, Q2Y = 78% of predicted variance; the parameters of model validation were: Mean root squares of standard error estimation (RMSEE) = 9.55%; P value after CV-ANOVA = 3.24664e-008 (correlation on 65% of total variance). Mean R2Y and Q2Y shifted after 20 permutations at intercept from 0.962 to 0.68 and from 0.79 to −0.32, respectively; all indicators showed therefore good model validation. HF = high fat diet, HF-scFOS = isocaloric high fat diet +10% scFOS.

Figure 5. Box and Whiskers plot comparing the composite metabolic scores.

VIP values of the n = 1710 metabolic features calculated from the partial least square discrimant analysis of figure 4) for faecal (F), hydrophilic plasma (PH), lipophylic plasma (PL) and urine (U) sample. Each box is delimited by the first quartile Q1 (lower limit of the box), the mediane (second quartile Q2) and the third quartile Q3 (upper box limit). The whiskers represent the adjacent values to the interquartile difference. a, b, c, d indicate a significant difference among the groups (Bonferroni/Dunn post-hoc test after ANOVA).

Figure 6. Bacterial group correlation network with metabolites and phenotypic measures (n+1+1) of mice fed high fat diet with or without scFOS.

Correlation plots indicating pair-wise correlations among metabolites and bacterial group or phenotypic measures that have a Pearson correlation coefficient value over or equal to 0.7. Node shapes represent the metabolic feature origin as in the legend. Red nodes represent measures that are increased with scFOS and green nodes represent measures that are lowered with scFOS. The edges with solid lines represent positive correlation coefficients and the edges with dashed lines represent negative correlation coefficients. Nodes with an arrow indicates variable of interest (Hub or physiologically relevant variable).