Fecal metabolomics combined with 16S rRNA gene sequencing to analyze the effect of Jiaotai pill intervention in type 2 diabetes mellitus rats

Abstract

The occurrence and development of type 2 diabetes mellitus (T2DM) are closely related to gut microbiota. Jiaotai pill (JTP) is used to treat type 2 diabetes mellitus, with definite efficacy in clinical practice. However, it is not clear whether the therapeutic effect is produced by regulating the changes in gut microbiota and its metabolism. In this study, T2DM rat models were established by a high-fat diet and low-dose streptozotocin (STZ). Based on the pharmacodynamic evaluation, the mechanism of JTP in the treatment of type 2 diabetes mellitus was investigated by fecal metabolism and 16S rRNA gene sequencing. The results showed that JTP decreased blood glucose (FBG, HbA1c) and blood lipid (TC, TG, and LDL) levels and alleviated insulin resistance (FINS, IL-10) in T2DM rats. 16S rRNA gene sequencing results revealed that JTP increased microbiota diversity and reversed the disorder of gut microbiota in T2DM rats, and therefore achieved the therapeutic effect in T2DM. JTP regulated 13 differential flora, which were Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Eubacteriaceae, Prevotellaceae, Ruminococcaceae, Clostridium_IV, Clostridium_XlVa, Eubacterium, Fusicatenibacter, Romboutsia, and Roseburia. Metabolomics analysis showed that JTP interfered with 13 biomarkers to play a therapeutic role in type 2 diabetes mellitus. They were L-Valine, Choline, L-Aspartic acid, Serotonin, L-Lysine, L-Histidine, 3-Hydroxybutyric acid, Pyruvic acid, N-Acetylornithine, Arachidonic acid, L-Tryptophan, L-Alanine, and L-Methionine. KEGG metabolic pathway analysis of the above differential metabolites and gut microbiota by using the MetaboAnalyst database and Picrust software. It was found that JTP treated type 2 diabetes mellitus by affecting metabolic pathways such as amino acid metabolism, carbohydrate metabolism, and lipid metabolism. Spearman correlation analysis revealed high correlations for 7 pharmacological indicators, 12 biomarkers, and 11 gut microbiota. In this study, the therapeutic effect and potential mechanism of JTP on type 2 diabetes mellitus were preliminarily demonstrated by gut microbiota and metabolomics, which could provide a theoretical basis for the treatment of T2DM with JTP.

Keywords: 16S rRNA gene sequencing; Jiaotai pill; UPLC-Q-exactive focus MS; metabolomics; type 2 diabetes mellitus.

FIGURE 1

(A) Trends in fasting blood glucose (FBG) values in the control and model groups during the establishment of the type 2 diabetes mellitus (T2DM) model. *p < 0.05, **p < 0.01 compared to the control group (n = 8). (B–H) Changes in FBG, HbA1c, FINS, TC, TG, LDL, and IL-10 in the control, model, MET, and JTP groups (n = 8). *p < 0.05, **p < 0.01 compared to the control group, ^#p < 0.05, ^##p < 0.01 compared to the model group. (I) HE staining results of pancreatic tissue in the control, model, MET, and JTP groups (200×).

FIGURE 2

(A) The species accumulation curves. (B) Species dilution curve. (C) PCoA analysis of each group n = 6. (D) NMDS analysis of each group n = 6. (E) Sobs indexes n = 6. (F) Chao indexes n = 6. (G) Shannon indexes n = 6. (H) Ace indexes n = 6. (I) Differences in the abundance of bacteria in each group at the phylum level. Abscissa is sample grouping and ordinate is the relative abundance of annotated species. (J) Differences in the abundance of bacteria in each group at the family level. Abscissa is sample grouping and ordinate is the relative abundance of annotated species. (K) Differences in the abundance of bacteria in each group at the genus level. Abscissa is sample grouping and ordinate is the relative abundance of annotated species. (L) PICRUST analysis to predict the function of gut microbiota based on the KEGG database. PCoA, principal coordinates analysis; NMDS, non-metric multidimensional scaling.

FIGURE 3

(A) Metabolic profile during type 2 diabetes mellitus (T2DM) modeling in positive ion mode. (B) Metabolic profile after treatment positive ion mode. (C) OPLS-DA score plots for the control group and the model group in positive ion mode. (D) Metabolic profile during T2DM modeling in negative ion mode. (E) Metabolic profile after treatment negative ion mode. (F) OPLS-DA score plots for the control group and the model group in negative ion mode. (G) Fecal biomarkers in the S-plot between the control group and the model group in positive ion mode. (H) Fecal biomarkers in the VIP between the control group and the model group in positive ion mode. (I) Fecal biomarkers in the FC between the control group and the model group in positive ion mode. (J) Fecal biomarkers in the S-plot between the control group and the model group in negative ion mode. (K) Fecal biomarkers in the VIP between the control group and the model group in negative ion mode. (L) Fecal biomarkers in the FC between the control group and the model group in negative ion mode.

FIGURE 4

(A) Heatmap of changes in levels of 23 potential biomarkers in control and model groups. (B) Metabolism pathway analysis of 23 potential biomarkers with MetPA. MetPA, metabolic pathway analysis.

FIGURE 5

(A) PLS-DA analysis of the control, model, and JTP groups in positive ion mode. (B) PCA analysis of the control, model, and JTP groups in negative ion mode. (C) Metabolism pathway analysis of 13 biomarkers of JTP back regulation with MetPA. (D) Scatter plots of the changes in the 13 biomarkers of JTP back regulation in the control, model, and JTP groups, *p < 0.05, *p < 0.01 compared to the control group, ^#p < 0.05, ^##p < 0.01 compared to the model group (n = 8).

FIGURE 6

(A) Spearman correlation analysis of biomarkers and pharmacological indices. (B) Spearman correlation analysis of biomarkers and gut microbiota. (C) Sankey diagrams for the interconnection of pharmacological indicators, biomarkers, and gut microbiota.