Green Tea Polyphenols Modify the Gut Microbiome in db/db Mice as Co-Abundance Groups Correlating with the Blood Glucose Lowering Effect

Abstract

Scope: The effects of green tea polyphenols, Polyphenon E (PPE), and black tea polyphenols, theaflavins (TFs), on gut microbiota and development of diabetes in db/db mice are investigated and compared.

Methods and results: Supplementation of PPE (0.1%) in the diet of female db/db mice for 7 weeks decreases fasting blood glucose levels and mesenteric fat while increasing the serum level of insulin, possibly through protection against β-cell damage. However, TFs are less or not effective. Microbiome analysis through 16S rRNA gene sequencing shows that PPE and TFs treatments significantly alter the bacterial community structure in the cecum and colon, but not in the ileum. The key bacterial phylotypes responding to the treatments are then clustered into 11 co-abundance groups (CAGs). CAGs 6 and 7, significantly increased by PPE but not by TFs, are negatively associated with blood glucose levels. The operational taxonomic units in these CAGs are from two different phyla, Firmicutes and Bacteroidetes. CAG 10, decreased by PPE and TFs, is positively associated with blood glucose levels.

Conclusion: Gut microbiota respond to tea polyphenol treatments as CAGs instead of taxa. Some of the CAGs associated with the blood glucose lowering effect are enriched by PPE, but not TFs.

Keywords: db/db mice; diabetes; intestinal microbiota; metabolism; tea polyphenols.

Figure 1.

Effects of PPE and TFs treatment on mouse body weights, food and water consumption. Structures of green tea polyphenols (a) and theaflavins (b); effects of PPE and TFs treatment on mouse body weights (c), food consumption (d) and water consumption (e). For green tea polyphenols – Epigallocatechin 3-gallate (EGCG): R1=galloyl, R2= OH; Epigallocatechin (EGC): R1 = H, R2 = OH; Epicatechin 3-gallate: R1 = galloyl, R2 = H; and Epicatechin: R1= R2= H. For theaflavins – Theaflavin (TF): R1 = R2 = H; TF-3-gallate: R1 = galloyl, R2 = H; TF-3‘-gallate: R1 = H, R2 = galloyl and TF-3,3‘digallate: R1 = R2 = galloyl. Body weight (n=12 mice) and food or water consumption (n=3 cages) were measured 3 times per week. For clarity, only the weekly data are shown as mean ± S.E. ^a,b indicate difference among groups by ANOVA (p < 0.05).

Figure 2.

Effects of PPE and TFs treatments on blood glucose (a), insulin levels (b), insulin staining and insulin score of pancreas samples (c-g). The data for glucose and insulin levels are shown as mean ± S.E. (n=12). Panel C was from a female wild-type mouse at 7 weeks of age and fasted overnight before sacrifice; the size of the pancreas islet was smaller than the db/db mice at age 13 weeks (without fasting), shown in panels d-f. ^a,b,c indicate difference among groups by ANOVA (p < 0.05).

Figure 3.

α-Diversity and β-diversity of microbiota composition at each sampling site. The number of observed species in each sample (a), Shannon index (b), and weighted UniFrac PCoA (c) are shown. α-Diversities were compared using Wilcoxon test (* p < 0.05, ** p < 0.01, *** p < 0.001). β-Diversities were compared using PERMANOVA among three groups first. If p-value < 0.05, then β-diversities between treatment groups were analyzed by pairwise PERMANOVA, and p-value was listed in each panel.

Figure 4.

Co-abundance groups of OTUs that were significantly altered by PPE and/or TFs treatment. Microbial interaction network showed SparCC correlation among the OTUs (a). Each node represents a bacterial OTU and its size is proportional to the relative abundance. OTUs were grouped into different co-abundance groups (CAGs) by PERMANOVA on the SparCC distance tree (999 permutations, p < 0.01). Heatmap shows the relative abundance of the 61 OTUs (b). Colors from black to red indicates the increase of the relative abundance. OTUs (rows) were clustered based on SparCC correlation distance. Samples (columns) were clustered based on Euclidean distance. Both rows and columns were grouped by Ward cluster algorithm. The average abundance of each CAGs among the four sampling sites was shown in boxplot (c). * p < 0.05, ** p < 0.01, *** p < 0.001

Figure 5.

CAGs correlated with glucose and insulin levels at different sampling sites. Spearman correlation of the relative abundance of each CAG from cecal mucosa, cecal content, colonic mucosa, and colonic content with fasting blood glucose levels and serum insulin levels at week 6 (a). The relative abundance of CAGs 6, 7 and 10 in each of the four sampling sites are shown (b). * p < 0.05, ** p < 0.01, *** p < 0.001.