In vitro gut microbiome response to carbohydrate supplementation is acutely affected by a sudden change in diet

Abstract

Background: Interactions between diet, stress and the gut microbiome are of interest as a means to modulate health and performance. Here, in vitro fermentation was used to explore the effects of a sudden change in diet, 21 days sole sustenance on the Meal, Ready-to-Eat (MRE) U.S. military combat ration, on inter-species competition and functional potential of the human gut microbiota. Human fecal samples collected before and after MRE intervention or consuming a habitual diet (HAB) were introduced to nutrient-rich media supplemented with starch for in vitro fermentation under ascending colon conditions. 16S rRNA amplicon and Whole-metagenome sequencing (WMS) were used to measure community composition and functional potential. Specific statistical analyses were implemented to detect changes in relative abundance from taxa, genes and pathways.

Results: Differential changes in relative abundance of 11 taxa, Dorea, Lachnospira, Bacteroides fragilis, Akkermansia muciniphila, Bifidobacterium adolescentis, Betaproteobacteria, Enterobacteriaceae, Bacteroides egerthii, Ruminococcus bromii, Prevotella, and Slackia, and nine Carbohydrate-Active Enzymes, specifically GH13_14, over the 24 h fermentation were observed as a function of the diet intervention and correlated to specific taxa of interest.

Conclusions: These findings suggest that consuming MRE for 21 days acutely effects changes in gut microbiota structure in response to carbohydrate but may induce alterations in metabolic capacity. Additionally, these findings demonstrate the potential of starch as a candidate supplemental strategy to functionally modulate specific gut commensals during stress-induced states.

Trial registration: ClinicalTrials.gov NCT02423551.

Keywords: Carbohydrate metabolism; Carbohydrate-active enzymes; Gut microbiome; Gut microbiota; In vitro fermentation; Meal Ready-to-Eat (MRE); Metabolic competition; Microbial ecology; Microbial functional potential; Next generation sequencing.

Fig. 1

16S rRNA Weighted-Unifrac PCoA shows a divergence and clustering by Fermentation Time Points (0, 5, 10, 24, and 48 h) but not by Diet (MRE vs HAB) and Study Day (0d vs 21d)

Fig. 2

Weighted-Unifrac Distances Metric Boxplots with PERMANOVA Analysis (A) and Beta-Diversity Volatility Analysis (B). Panel A corresponds to the Weighted_Unifrac Distance Metric and Adonis PERMANOVA analysis by Diet (a), Study Day (b), Fermentation Time Points (c) and Diet_Study Day (d) groups, respectively. P-values are for comparisons to the Pool time point. Panel B corresponds to the Volatility Analysis for the Beta-Diversity Weighted_Unifrac Distance Metric for (a) Diet, (b) Study Day and (c) Diet_Study Day

Fig. 3

16S rRNA Linear Mixed Model Analysis for 11 organisms. Linear mixed model analysis (multivariate ANOVA with repeated measures) for Diet*StudyDay*Fermentation Time Points interaction. Linear graphs representing the 11 organisms out of 127 that have significant 3-way interaction for the different fermentation time points and Diet/Date groups in function of their relative abundance: Dorea (A), Lachnospira (B), Bacteroides fragilis (C), Akkermansia muciniphila (D), Bifidobacterium adolescentis (E), Betaproteobacteria (F), Enterobacteriaceae (G), Bacteroides egerthii (H), Ruminococcus bromii (I), Prevotella (J), and Slackia (K). Exact p-values from the test are also reported in Table S1. Pairwise multiple comparison (Tukeys HSD Analysis) for the 11 organisms obtained after the Linear mixed model analysis within each time-point, is represented by symbols: a (*) symbol indicates a difference between study days 0 and 21 for the same diet, and a (^) symbol indicates a difference between MRE and habitual (HAB) diets for the same study day. One symbol indicates p ≤ 0.05, two symbols indicates p ≤ 0.01, and three symbols indicates p ≤ 0.001. Exact p-values from the test are also reported in Table S2

Fig. 4

Linear Mixed Model Analysis for 9 CAZymes. Linear graphs representing the 9 CAZymes out of 20 that have significant 3-way interaction for the different fermentation time points and Diet/Date groups in function of their relative abundance: GH13_14 (A), GT79 (B), CBM40 (C), PL22 (D), GH13_4 (E), PL1 (F), GT76 (G), GH36 (H), and GH13_18 (I). (*) symbol indicates a difference between study days 0 and 21 for the same diet, and a (^) symbol indicates a difference between MRE and habitual (HAB) diets for the same study day. One symbol indicates p ≤ 0.05, two symbols indicates p ≤ 0.01, and three symbols indicates p ≤ 0.001

Fig. 5

CAZyme’s Bar Plot by Species. Bar graphs linking CAZymes relative abundances with bacterial species bins. A corresponds to GH13_14, and B GT76. MAG and taxonomic breakdown of GH13_14 by Diet*Date indicated that the increased abundance in MRE Day 21 samples was due to a Coproccocus comes MAG (A) and CAZyme GT76 and its prevalence in the MRE day 21 group associated with Lachnospira eligens (B)

Fig. 6

Schematic representation of the in vitro fermentation protocol. Human fecal samples from volunteers belonging to the two different diets were obtained and processed to meet the requirements for the in vitro fermentation system protocol. The top section represents the specific conditions of the system mimicking the human gut. The lower section represents the post-fermentation high-throughput sequencing analysis employed to samples collected at different time points during fermentation. *HAB: Habitual Diet; MRE: Meal, Ready-to-Eat