Temporal variations in the gut microbial diversity in response to high-fat diet and exercise

Abstract

High-fat diet-induced obesity is a pandemic caused by an inactive lifestyle and increased consumption of Western diets and is a major risk factor for diabetes and cardiovascular diseases. In contrast, exercise can positively influence gut microbial diversity and is linked to a decreased inflammatory state. To understand the gut microbial variations associated with exercise and high-fat diet over time, we conducted a longitudinal study to examine the effect of covariates on gut microbial diversity and composition. Young mice were divided into four groups: Chow-diet (CHD), high-fat diet (HFD), high-fat diet + exercise (HFX), and exercise only (EXE) and underwent experimental intervention for 12 weeks. Fecal samples at week 0 and 12 were collected for DNA extraction, followed by 16S library preparation and sequencing. Data were analyzed using QIIME 2, R and MicrobiomeAnalyst. The Bacteroidetes-to-Firmicutes ratio decreased fivefold in the HFD and HFX groups compared to that in the CHD and EXE groups and increased in the EXE group over time. Alpha diversity was significantly increased in the EXE group longitudinally (p < 0.02), whereas diversity (Shannon, Faith's PD, and Fisher) and richness (ACE) was significantly reduced in the HFD (p < 0.005) and HFX (p < 0.03) groups over time. Beta diversity, based on the Jaccard, Bray-Curtis, and unweighted UniFrac distance metrics, was significant among the groups. Prevotella, Paraprevotella, Candidatus arthromitus, Lactobacillus salivarius, L. reuteri, Roseburia, Bacteroides uniformis, Sutterella, and Corynebacterium were differentially abundant in the chow-diet groups (CHD and EXE). Exercise significantly reduced the proportion of taxa characteristic of a high-fat diet, including Butyricimonas, Ruminococcus gnavus, and Mucispirillum schaedleri. Diet, age, and exercise significantly contributed to explaining the bacterial community structure and diversity in the gut microbiota. Modulating the gut microbiota and maintaining its stability can lead to targeted microbiome therapies to manage chronic and recurrent diseases and infections.

Figure 1

Alpha-diversity boxplots illustrating species richness (ACE) and diversity (Shannon, Faith’s phylogenetic diversity [PD], Fisher, Pielou’s Evenness and Inverse Simpson). Two-way ANOVA followed by the 2-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli test and Šidák’s test were performed for mean comparison and multiple comparisons across groups. Boxes indicate interquartile ranges, lines denote medians, and whiskers demarcate the ranges. P-values are false discovery rate (FDR)-corrected. Longitudinal significance among intervention groups is marked.

Figure 2

PCoA of the intervention groups using the Jaccard, Bray–Curtis, and unweighted UniFrac distance metrics based on feature level. The data points represent individual samples (triangles for 0-week and circles for 12-week). Values in parentheses show the percentage of total variance explained by each axis. Statistical significance was determined using PERMANOVA and confirmed using PERMDISP; Jaccard (p = 0.001), Bray–Curtis (p = 0.002), and unweighted UniFrac (p = 0.001).

Figure 3

Taxonomic profiling of the gut microbiota of mice among experimental groups over time. (A) Phylum level composition, and (B) Bacteroidetes/Firmicutes ratio. Two-way ANOVA and Tukey’s multiple comparison tests (p: **** < 0.0001 and *** < 0.0005).

Figure 4

Relative abundance of microbiota at the genus/species level in the guts of mice among the intervention groups over time, comparing the initial (week 0) and final time points (week 12). (A) Percent relative abundance. Two-way ANOVA and Tukey’s multiple comparison tests were used to determine significance (p < 0.05). (B) Log-transformed counts of differentially abundant features estimated using the LEfSe algorithm.

Figure 5

Analysis of differentially abundant bacterial taxa based on diet and exercise interventions. (A) LEfSe analysis based on diet is shown as a histogram of the log LDA score > 3 and FDR-corrected p value < 0.05 for the significant taxa. (B) Multiple linear regression analysis associating microbial genera/species with exercise intervention after covariate adjustment for diet and intervention duration. The Y-axis shows log-transformed counts of significantly altered bacterial taxa (FDR-corrected p < 0.05).

Figure 6

Heatmap representing hierarchical clustering analysis at the genus/species level. The analysis was performed with normalized data and autoscale feature standardization. Cluster organization was performed based on intervention group, using the Minkowski distance measure and Ward’s algorithm.