Characterization of gut microbial and metabolite alterations in faeces of Goto Kakizaki rats using metagenomic and untargeted metabolomic approach

Abstract

Background: In recent years, the incidence of type 2 diabetes (T2DM) has shown a rapid growth trend. Goto Kakizaki (GK) rats are a valuable model for the study of T2DM and share common glucose metabolism features with human T2DM patients. A series of studies have indicated that T2DM is associated with the gut microbiota composition and gut metabolites. We aimed to systematically characterize the faecal gut microbes and metabolites of GK rats and analyse the relationship between glucose and insulin resistance.

Aim: To evaluate the gut microbial and metabolite alterations in GK rat faeces based on metagenomics and untargeted metabolomics.

Methods: Ten GK rats (model group) and Wistar rats (control group) were observed for 10 wk, and various glucose-related indexes, mainly including weight, fasting blood glucose (FBG) and insulin levels, homeostasis model assessment of insulin resistance (HOMA-IR) and homeostasis model assessment of β cell (HOMA-β) were assessed. The faecal gut microbiota was sequenced by metagenomics, and faecal metabolites were analysed by untargeted metabolomics. Multiple metabolic pathways were evaluated based on the differential metabolites identified, and the correlations between blood glucose and the gut microbiota and metabolites were analysed.

Results: The model group displayed significant differences in weight, FBG and insulin levels, HOMA-IR and HOMA-β indexes (P < 0.05, P < 0.01) and a shift in the gut microbiota structure compared with the control group. The results demonstrated significantly decreased abundances of Prevotella sp. CAG:604 and Lactobacillus murinus (P < 0.05) and a significantly increased abundance of Allobaculum stercoricanis (P < 0.01) in the model group. A correlation analysis indicated that FBG and HOMA-IR were positively correlated with Allobaculum stercoricanis and negatively correlated with Lactobacillus murinus. An orthogonal partial least squares discriminant analysis suggested that the faecal metabolic profiles differed between the model and control groups. Fourteen potential metabolic biomarkers, including glycochenodeoxycholic acid, uric acid, 13(S)-hydroxyoctadecadienoic acid (HODE), N-acetylaspartate, β-sitostenone, sphinganine, 4-pyridoxic acid, and linoleic acid, were identified. Moreover, FBG and HOMA-IR were found to be positively correlated with glutathione, 13(S)-HODE, uric acid, 4-pyridoxic acid and allantoic acid and ne-gatively correlated with 3-α, 7-α, chenodeoxycholic acid glycine conjugate and 26-trihydroxy-5-β-cholestane (P < 0.05, P < 0.01). Allobaculum stercoricanis was positively correlated with linoleic acid and sphinganine (P < 0.01), and 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate was negatively associated with Prevotella sp. CAG:604 (P < 0.01). The metabolic pathways showing the largest differences were arginine biosynthesis; primary bile acid biosynthesis; purine metabolism; linoleic acid metabolism; alanine, aspartate and glutamate metabolism; and nitrogen metabolism.

Conclusion: Metagenomics and untargeted metabolomics indicated that disordered compositions of gut microbes and metabolites may be common defects in GK rats.

Keywords: Goto-Kakizaki rats; Gut microbial; Metabolites; Metagenomics; Type 2 diabetes mellitus; Untargeted metabolomics.

Figure 1

Comparison of body weight, fasting blood glucose and insulin resistance levels. A: Comparison of body weight; B: Comparison of fasting blood glucose levels; C: Comparison of insulin levels; D: Comparison of homeostasis model assessment of insulin resistance index; E: Comparison of homeostasis model assessment of β cell index. ^aP < 0.05, ^bP < 0.01 vs the control group. FBG: Fasting blood glucose.

Figure 2

Comparison of the gut microbiota structure by principal coordinate analysis. A: Comparison of the gut microbiota structure by principal coordinate analysis; B: Distribution of different groups on the first principal component axis.

Figure 3

Comparison of the relative abundances of phylum, genus and species. A: Comparison of the relative abundances of phylum; B: Comparison of the relative abundances of genus; C: Comparison of the relative abundances of species. ^aP < 0.05, ^bP < 0.01 vs the control group.

Figure 4

Correlation the relationship between glucose factors, gut microbiota at the species level and metabolites. A: Correlation between glucose factors and the gut microbiota at the species level; B: Correlation between glucose factors and metabolites; C: Correlation between metabolites and the gut microbiota at the species level. ^aP < 0.05, ^bP < 0.01 vs the control group. The range of r values is noted. FBG: Fasting blood glucose.

Figure 5

Comparison of the metabolite profiles by orthogonal partial least squares discriminant analysis and model validation. A: Comparison of the positive-ion metabolite profiles by orthogonal partial least squares discriminant analysis (OPLS-DA); B: Positive-ion metabolite profiles with OPLS-DA model validation; C: Comparison of the negative-ion metabolite profiles by OPLS-DA; D: Negative-ion metabolite profiles with OPLS-DA model validation.

Figure 6

Differential metabolites and metabolic pathways. A: Volcano plot of the differential positive- and negative-ion metabolites; B: Metabolic pathways showing differential regulation in Goto Kakizaki rats.

Figure 7

Potential targets of the metabolic pathways showing differential regulation in Goto Kakizaki rats. Compared with the control group, green and orange represent decreased and increased levels, respectively.