Gut microbiome variation modulates the effects of dietary fiber on host metabolism

Abstract

Background: There is general consensus that consumption of dietary fermentable fiber improves cardiometabolic health, in part by promoting mutualistic microbes and by increasing production of beneficial metabolites in the distal gut. However, human studies have reported variations in the observed benefits among individuals consuming the same fiber. Several factors likely contribute to this variation, including host genetic and gut microbial differences. We hypothesized that gut microbial metabolism of dietary fiber represents an important and differential factor that modulates how dietary fiber impacts the host.

Results: We examined genetically identical gnotobiotic mice harboring two distinct complex gut microbial communities and exposed to four isocaloric diets, each containing different fibers: (i) cellulose, (ii) inulin, (iii) pectin, (iv) a mix of 5 fermentable fibers (assorted fiber). Gut microbiome analysis showed that each transplanted community preserved a core of common taxa across diets that differentiated it from the other community, but there were variations in richness and bacterial taxa abundance within each community among the different diet treatments. Host epigenetic, transcriptional, and metabolomic analyses revealed diet-directed differences between animals colonized with the two communities, including variation in amino acids and lipid pathways that were associated with divergent health outcomes.

Conclusion: This study demonstrates that interindividual variation in the gut microbiome is causally linked to differential effects of dietary fiber on host metabolic phenotypes and suggests that a one-fits-all fiber supplementation approach to promote health is unlikely to elicit consistent effects across individuals. Overall, the presented results underscore the importance of microbe-diet interactions on host metabolism and suggest that gut microbes modulate dietary fiber efficacy. Video abstract.

Fig. 1

Study design. Six to eight-week-old C57BL/6 GF male mice were placed on irradiated diet containing a mix of five fibers (assorted fiber diet; Af); a week later mice were colonized with one of two different human fecal samples SubA, SubB. Bedding and wires with food were exchanged between cages of mice colonized with the same community to minimize cage effects. Two weeks after colonization gnotobiotic mice received one of four isocaloric diets that vary by the type of fiber (10% w/w): cellulose (C), inulin (I), pectin (P), and assorted fiber (Af). Mice were maintained in these diets for 4 weeks

Fig. 2

Gut microbiome impact on host metabolic phenotypes in different dietary fibers. Phenotypes were measured after 6 weeks colonization and 4 weeks of specific dietary fiber exposure (~ 15 weeks old). a Epididymal fat pad weight expressed a percentage of body weight (n = 7–10/community/diet). b Liver triglycerides levels (n = 7–10/community/diet). c Serum glucose levels (arbitrary units) as measured by UPLC/MS/MS (untargeted metabolomics platform; n = 6/community/diet). Wilcoxon rank sum test was conducted to examine whether two samples are likely to derive from the same population. Box plots represent median, interquartile range, minimum and maximum value in the data, and potential outliers. *P < 0.05, ns = not significant. SubA, magenta; SubB, yellow

Fig. 3

Dietary fibers cause significant restructuring of transplanted human-derived microbial communities. 16S rRNA gene sequence analysis of cecal communities of gnotobiotic mice colonized with SubA and SubB exposed to diets containing cellulose (orange), inulin (purple), pectin (pink), or assorted fiber (green). a, b Principal coordinates analysis (PCoA) of unweighted and weighted UniFrac distances, respectively. SubA community is represented by squares and SubB by circles. c Cladograms generated using LEfSe analysis; comparison results are presented for the two communities in each diet, colors distinguish taxa differences between SubA (magenta) and SubB (yellow) communities. Diet is indicated in each of the four cladograms (C = cellulose, I = inulin, P = pectin, Af = assorted fiber). d Genus/family level relative abundances of taxa. Taxa that showed significant differences in relative abundance through the interventions are marked with an asterisk (Kruskal-Wallis test and LDA P < 0.05)

Fig. 4

Gut microbiome variation directs changes in serum levels of amino acid and lipid metabolites. Heatmap indicating fold-changes in the abundance of amino acid and lipid metabolites in serum from mice colonized with SubA and SubB communities for each diet as determined by ultrahigh-performance liquid chromatography-tandem mass spectroscopy (UPLC–MS/MS). Biochemicals exhibiting a difference of at least 20% between SubB and SubA are indicated for each diet; P < 0.05 (Two-way ANOVA). List of metabolites, P values, and fold-changes are listed in Supplemental Table S6

Fig. 5

Changes in serum levels of amino acids and lipids associated with metabolic phenotypes. a Correlation matrix between metabolite consensus modules and host phenotypes measured in mice described in Fig. 1. Modules were determined based on patterns of co-abundance of metabolites using weighted correlation network analysis (WGCNA). Each of the modules was labelled with a unique color as an identifier. Each module was tested for correlation with host metabolic phenotypes. Within each cell, upper values are correlation coefficients between module and the phenotypes; lower values are the corresponding FDR adjusted-P values. b Pathways enriched in the blue, turquoise, and red modules as determined by Fisher’s test

Fig. 6

Association network between gut microbiota and blood metabolites. Association strength is denoted by width of the lines; red lines show positive association while blue ones show negative. Phylum of taxa is indicated by colored boxes and metabolite super-pathway (listed in Supplemental Table 5) by colored circles. Anaerotruncus (Firmicutes) is the genus with the largest number of negative associations with the lipid super-pathway, while the family Chistensenellaceae (Firmicutes) has the largest number of positive associations with metabolites in the same super-pathway. Enterobacteriaceae family (Proteobacteria) is the taxa with more positive associations with the amino acid super-pathway

Fig. 7

Effects of gut microbiome variation on hepatic gene expression across different fibers. a MA plots showing differentially expressed genes in the liver of mice colonized with SubB vs. SubA microbiome consuming (i) cellulose diet; (ii) inulin diet; (iii) pectin diet; (iv) assorted fiber diet (n = 5 animals/microbiome/diet). Differentially express genes (DEGs) P < 0.05, FDR adjusted-P < 0.05. b Heatmap showing differentially expressed KEGG pathways between SubB and SubA. The ratio of significantly regulated KEGG terms and genes annotated with each KEGG term (minimum of 10) comparing SubB vs. SubA in each dietary intervention are shown in the heatmap. Upregulated pathways are shown in red and downregulated pathways are shown in blue. Inulin and pectin comparisons show the largest number of differentially expressed genes. MA stands for the relationship between values of intensity (i.e., counts) and difference between the data (M = log ratio and A = mean average)

Fig. 8

Liver gene expression is associated with host metabolic phenotypes. a Correlation matrix between gene expression consensus modules and host phenotypes measured in mice described in Fig. 1. Modules were determined based on patterns of co-abundance of transcripts using weighted correlation network analysis (WGCNA). Each of the modules was labelled with a unique color as an identifier. Each module was tested for correlation with host metabolic phenotypes. Within each cell, upper values are correlation coefficients between module and the phenotypes; lower values are the corresponding FDR adjusted-P values. b Gene Ontology (biological process term) and KEGG pathways over-represented in the blue module (gene count > 5; P and FDR adjusted-P < 0.05)

Fig. 9

Taxa associated with liver transcriptomic modules. Association strength is denoted by width of the lines, red lines show positive association while blue ones show negative. Phylum level classification of the taxa is marked by colored boxes and transcriptomic weighted correlation network analysis (WGCNA) module by colored circles. Anaerotruncus (Firmicutes) and Alistipes (Bacteroidetes) are the taxa with the most associations with the clustered genes