The Metabolic Response to a Low Amino Acid Diet is Independent of Diet-Induced Shifts in the Composition of the Gut Microbiome

Abstract

Obesity and type 2 diabetes are increasing in prevalence around the world, and there is a clear need for new and effective strategies to promote metabolic health. A low protein (LP) diet improves metabolic health in both rodents and humans, but the mechanisms that underlie this effect remain unknown. The gut microbiome has recently emerged as a potent regulator of host metabolism and the response to diet. Here, we demonstrate that a LP diet significantly alters the taxonomic composition of the gut microbiome at the phylum level, altering the relative abundance of Actinobacteria, Bacteroidetes, and Firmicutes. Transcriptional profiling suggested that any impact of the microbiome on liver metabolism was likely independent of the microbiome-farnesoid X receptor (FXR) axis. We therefore tested the ability of a LP diet to improve metabolic health following antibiotic ablation of the gut microbiota. We found that a LP diet promotes leanness, increases energy expenditure, and improves glycemic control equally well in mice treated with antibiotics as in untreated control animals. Our results demonstrate that the beneficial effects of a LP diet on glucose homeostasis, energy balance, and body composition are unlikely to be mediated by diet-induced changes in the taxonomic composition of the gut microbiome.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1

A low protein diet promotes metabolic health and alters the taxonomic composition of the cecal microbiome. (A) Glucose tolerance test on male C57BL/6J mice fed a Control (22% of calories from amino acids) or Low AA (7% of calories from amino acids) diet for 4 months (n = 8–10/group; *p < 0.05, t-test). (B) Weight and body composition were measured immediately prior to diet start and after 10 weeks on the indicated diets (n = 8–10/group; *p < 0.05, = t-test). (C) Bar plot of average relative abundance at the phylum taxonomic level. Top 6 phyla are shown. (D) Principle component analysis of demonstrating the effect of diet on taxonomic composition. (E,F) Bacterial phyla differentially represented in cecal contents from mice fed the specified diets for 4 months (n = 7–12/group; Sidak’s test following ANOVA, *p < 0.05). Error bars represent SEM.

Figure 2

A low protein diet alters the hepatic transcriptome and shows distinct changes in biological pathways. (A–C) RNA-Seq was performed on the livers for mice fed a Control diet or a Low AA for four months. (A) A heatmap indicating the relative expression of genes involved in the most significantly enriched biological KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways based on genes differentially expressed in the livers of Control and Low AA fed mice (q < 0.05, FDR). Genes in more than one significantly enriched KEGG pathway are listed only once, and assigned to the most significantly affected pathway. (B) Pathway enrichment analysis was performed using g:Profiler (g:GOSt), and the p-values of KEGG pathways significantly up- and downregulated by Low AA diet feeding were determined. Colors are matched to that of pathways in (A). (C) Heatmap representing the relative expression of liver genes known to be altered by FXR-FGF15 bile acid signaling.

Figure 3

A low protein diet alters body composition similarly in vehicle and antibiotic-treated mice. (A) Schematic representation of the experimental plan; mice were pretreated with antibiotics or vehicle for three weeks, and then randomized to either a Control or Low AA diet. (B) Fecal DNA content was determined following 3 weeks of antibiotic treatment (n = 8/group; *p < 0.05, t-test). (C) Weight of the mice in each group was tracked following randomization to each diet. (D–F) Weight and body composition were determined immediately prior to diet start and after 6 weeks on the indicated diets, and the change in (D) weight, (E) fat mass, and (F) lean mass was determined (n = 12/group; statistics for the overall effects of diet, antibiotic (ABX) treatment, and the interaction represent the p-value from a two-way ANOVA; *p < 0.05 from a Sidak’s post-test examining the effect of parameters identified as significant in the two-way ANOVA). Error bars represent SEM.

Figure 4

A low protein diet improves glucose homeostasis similarly in vehicle and antibiotic-treated mice. (A) Glucose and (B) alanine tolerance tests were conducted in mice fed the indicated diets for 8 weeks and 4 weeks, respectively (n = 12/group; statistics for the overall effects of diet, antibiotic (ABX) treatment, and the interaction represent the p-value from a two-way ANOVA; *p < 0.05 from a Sidak’s post-test examining the effect of parameters identified as significant in the two-way ANOVA). Error bars represent SEM.

Figure 5

A low protein diet increases food consumption and energy expenditure similarly in vehicle and antibiotic-treated mice. (A–F) Metabolic chambers were used to assess (A,B) food consumption, (C) spontaneous activity, (D) respiratory exchange ratio (RER), and (E,F) energy expenditure in mice fed the indicated diets for approximately two months. (n = 5–7/group; statistics for the overall effects of diet, antibiotic (ABX) treatment, and the interaction represent the p-value from a two-way ANOVA; *p < 0.05 from a Sidak’s post-test examining the effect of parameters identified as significant in the two-way ANOVA). Error bars represent SEM.