Green Tea Liquid Consumption Alters the Human Intestinal and Oral Microbiome

Abstract

Scope: GTPs (green tea polyphenols) exert anti-CRC (colorectal cancer) activity. The intestinal microbiota and intestinal colonization by bacteria of oral origin has been implicated in colorectal carcinogenesis. GT modulates the composition of mouse gut microbiota harmonious with anticancer activity. Therefore, the effect of green tea liquid (GTL) consumption on the gut and oral microbiome is investigated in healthy volunteers (n = 12).

Methods and results: 16S sequencing and phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis of both fecal and saliva samples (collected before intervention, after 2 weeks of GTL (400 mL per day) and after a washout period of one week) in healthy volunteers show changes in microbial diversity and core microbiota and difference in clear classification (partial least squares-discriminant analysis [PLS-DA]). An irreversible, increased FIR:BAC (Firmicutes to Bacteroidetes ratio), elevated SCFA producing genera, and reduction of bacterial LPS synthesis in feces are discovered in response to GTL. GTL alters the salivary microbiota and reduces the functional pathways abundance relevance to carcinogenesis. Similar bacterial networks in fecal and salivary microbiota datasets comprising putative oral bacteria are found and GTL reduces the fecal levels of Fusobacterium. Interestingly, both Lachnospiraceae and B/E (Bifidobacterium to Enterobacteriacea ratio-markers of colonization resistance [CR]) are negatively associated with the presence of oral-like bacterial networks in the feces.

Conclusion: These results suggest that GTL consumption causes both oral and gut microbiome alterations.

Keywords: Bacteroidetes; Firmicutes; colorectal cancer; green tea; gut microbiome; oral microbiome; short-chain fatty acids.

Figure 1

Green tea liquid consumption alters the overall gut microbiota composition. A) Similarity percentage (SIMPER) analysis using Bray–Curtis dissimilarity index showing the top 23 taxa with the greatest contribution (>1%) to the differences observed between 3 time points. B) Hierarchical clustering (HCN) with a heat map shows the relative abundances of core microbiota (relative abundance >1.0% and shared taxa among three time points). C) 3D view of score plots showing the results of supervised partial least squares‐discriminant analysis (PLS‐DA) with model fitness parameters of R ²(cum) = 0.937 and Q ²(cum) = 0.704, respectively. D) The top 25 taxa with variable importance in projection (VIP) scores (between 1.6 and 2.5) possibly responsible for discrimination of the GTL from BL and WO samples. The scores are given with upper bound (95%) and lower bound (95%). E) Linear discriminant analysis (LDA) scores (log 10) derived from LEfSe analysis, showing the biomarker taxa (LDA score of >2 and a significance of p <0.05 determined by the Wilcoxon signed‐rank test) for BL and Tx (GT) and WO. F) Cladogram generated from LEfSe analysis showing the relationship between taxon (the levels represent, from the inner to outer rings, phylum, class, order, family, and genus). Taxa are shown as phylum, family, and genus in (A), (B), and (D).

Figure 2

Green tea liquid (GTL) consumption is associated with altered gut microbiota diversity measurements. (A,B) α‐diversity (within‐subject diversity) measurements (observed taxa, Simpson Evenness Index, Shannon Diversity Index and Chao1 Richness Index) at the taxonomic level of genus and species for baseline (BL), GTL intervention (GT) and washout (WO) fecal samples. C) Principal‐coordinate analysis (PCoA) and PERMANOVA significance test (overall and pair wise comparisons) with Bray–Curtis dissimilarity index based on the relative abundance (RA) of phylum level OTUs identified in the feces at BL, GT, and WO samples. D) Differential abundance analysis (DAA) (non‐parametric one‐way ANOVA with FDR correction (p < 0.05) for multiple testing) followed by principal‐component analysis (PCA) (variance‐covariance type) showing the top three OTUs at phylum (p) level and Firmicutes (FIR) to Bacteroidetes (BAC) ratio included as vectors. The magnitude and direction correspond to the weights. (E–H) RA of FIR and BAC and FIR/BAC ratio. I) DAA with PCA analysis showing the top 7 OTUs at family level. J–O) RA of those families with FDR corrected p‐value <0.05. Data on the scatter plots with bar are expressed as means ± standard errors of the means (SEM). Data with different superscript letters are significantly different (p < 0.05) by Wilcoxon matched‐pairs signed rank test. n = 12. PERMANOVA, permutational multivariate analysis of variance; FDR, false discovery rate; OTUs, operational taxonomic units.

Figure 3

Green tea liquid consumption is associated with altered genus and species level microbiota composition and functions. A,B) Differential abundance analysis (DAA) followed by PCA (variance‐covariance type) showing score plots (A) and RA (B) of top eight OTUs at genus (g) level and Bifidobacterium (B) to Enterobacteriacea (E) ratio included as vectors for BL, GT and WO samples. The magnitude and direction correspond to the weights. C,D) DAA with PCA showing score plots (C) and RA (D) of top ten OTUs at species level for BL, GT, and WO samples. Species with invisible vectors were not mentioned in the PCA biplot. E,F) Functional prediction of microbial genes associated with BL, GT, and WO samples using PICRUSt followed by 3D projection of score plots (E) showing the results of PLS‐DA with model fitness parameters and LDA scores (F) of discriminating functional pathways between BL and GT derived from LEfSe analysis. G) Boxplots of RA of markers of microbial functional pathways relevance to inflammation. Data on the scatter plots with bar are expressed as means ± standard errors of the means (SEM). Data with different superscript letters are significantly different (p < 0.05) by Wilcoxon matched‐pairs signed rank test. n = 12. ANOVA, analysis of variance; RA, relative abundance; FDR, false discovery rate; OTUs, operational taxonomic units; LDA, linear discriminant analysis; LEfSe, LDA effect size.

Figure 4

Green tea liquid consumption alters the overall salivary microbiota composition. A) Similarity percentage (SIMPER) analysis. B) Hierarchical clustering (HCN) with a heat map shows the relative abundances of core microbiota. C) 3D view of score plots showing the results of supervised PLS‐DA with model fitness parameters. D) The top 25 taxa with VIP scores (between 1.7 and 2.8) possibly responsible for discrimination of the GT from BL and WO samples. The scores are given with upper bound (95%) and lower bound (95%). E) LDA scores (log 10) derived from LEfSe analysis, showing the biomarker taxa for BL, Tx (GT) and WO. F) Cladogram generated from LEfSe analysis showing the relationship between taxon. LEfSe, LDA effect size.

Figure 5

GTL consumption is associated with altered β‐diversity and functions of salivary microbiota. (A,B) 3D view of score plots showing the results of PCOA and PERMANOVA significance test (overall as well as pair wise comparisons) based on the relative abundance (RA) of whole microbiota (A) and class level (B) OTUs identified in the feces at BL, GT and WO periods. C) Functional prediction of microbial genes associated with BL, GT and WO samples using PICRUSt followed by LEfSe analysis showing LDA scores of discriminating functional pathways between 3 time points. D) RA of biomarker microbial functional pathways relevance to environmental information processing and carcinogenesis was shown in a panel where the straight and dotted lines plots means and medians of the RA, respectively, in each subgroup. n = 12. RA, relative abundance; FDR, false discovery rate.

Figure 6

Higher fecal Lachnospiraceae and B/E ratio were negatively associated with colonization of gut with oral‐like bacterial networks. A) Hierarchical Ward‐linkage clustering based on the Pearson correlation coefficients of the RA of genus level OTUs in fecal microbiota of 12 healthy subjects. Data from all three time points were combined for this analysis. Oral biofilm co‐abundance groups (CAGs) and oral pathogen CAGs were defined on the basis of a recently published literature (please refer main text). B) Results showing comparative analysis performed between members of salivary biofilm and pathogen CAGs and oral‐like bacterial genera (members of both CAGs) present in feces using RA of genus level OTUs. C,D) Linear regression analysis showing an association between oral pathogen CAG and Lachnospiraceae CAG and between oral pathogen CAG and B/E ratio using the sqrt transformed RA of members these two CAGs identified in the whole fecal microbiota data profile. Data are expressed as means ± standard errors of the means (SEM). Data with different superscript letters are significantly different (p < 0.05) by Wilcoxon matched‐pairs signed rank test. n = 12. RA, relative abundance; OTUs, operational taxonomic units; CAGs, co‐abundance groups; B/E Bifidobacterium to Enterobacteriacea ratio; √%, square root transformed percentage values. p < 0.05 considered significant.

Figure 7

α and β diversity differences between fecal and salivary microbiota. A) Differences in the α‐diversity measurements of fecal and salivary microbiota. B) Genera shared between feces and saliva were identified and then PCOA and PERMANOVA significance test were performed based on the RA of OTUs identified at genus level. C) The effects of status of bowel movements (normal vs constipation) on the α‐diversity measurements of fecal and salivary samples. D,E) The influence of age, sex, BMI, bowel movements, periodontal disease (PD) status on the overall composition of fecal (D), and salivary microbiota (E). F,G) Results of PLS‐CCA showing the correlation between environmental variables and RA (>1%) of OTUs identified at genus level in fecal and salivary 16S profile. Variable biplot dotted lines indicate direction of environmental gradient. Inserts in the corresponding figures show the VIP scores derived from PLS‐CCA. Overall p < 0.0001 (1000 permutations) for both feces and saliva. Variables with VIP score close to 1 or >1 exert significant effects on the overall composition of microbiota.