FXR-Dependent Modulation of the Human Small Intestinal Microbiome by the Bile Acid Derivative Obeticholic Acid

Abstract

Background & aims: Intestinal bacteria can modify the composition of bile acids and bile acids, which are regulated by the farnesoid X receptor, affect the survival and growth of gut bacteria. We studied the effects of obeticholic acid (OCA), a bile acid analogue and farnesoid X receptor agonist, on the intestinal microbiomes of humans and mice.

Methods: We performed a phase I study in 24 healthy volunteers given OCA (5, 10, or 25 mg/d for 17 days). Fecal and plasma specimens were collected at baseline (day 0) and on days 17 (end of dosing) and 37 (end of study). The fecal specimens were analyzed by shotgun meta-genomic sequencing. A Uniref90 high-stringency genomic analysis was used to assign specific genes to the taxonomic signature of bacteria whose abundance was associated with OCA. Male C57BL/6 mice were gavage fed daily with water, vehicle, or OCA (10 mg/kg) for 2 weeks. Small intestine luminal contents were collected by flushing with saline and fecal pellets were collected at baseline and day 14. Mouse samples were analyzed by 16S-tagged sequencing. Culture experiments were performed to determine the taxonomic-specific effects of bile acids and OCA on bacterial growth.

Results: Suppression of endogenous bile acid synthesis by OCA in subjects led to a reversible induction of gram-positive bacteria that are found in the small intestine and are components of the diet and oral microbiota. We found that bile acids decreased proliferation of these bacteria in minimum inhibitory concentration assays. In these organisms, there was an increase in the representation of microbial genomic pathways involved in DNA synthesis and amino acid metabolism with OCA treatment of subjects. Consistent with these findings, mice fed OCA had lower endogenous bile acid levels and an increased proportion of Firmicutes, specifically in the small intestine, compared with mice fed water or vehicle.

Conclusions: In studying the effects of OCA in humans and mice, we found evidence for interactions between bile acids and features of the small intestinal microbiome. These findings indicate that farnesoid X receptor activation alters the intestinal microbiota and could provide opportunities for microbiome biomarker discovery or new approaches to engineering the human microbiome. ClinicalTrials.gov, NCT01933503.

Keywords: Gene Regulation; Genetics; Metabolism; Nuclear Hormone Receptor.

Conflict of interest statement

Conflict of interest statement: LA is a consultant for and has stock options in Intercept Pharmaceuticals. FB and JE are employees and shareholders of Intercept Pharmaceuticals. GDW and RMC receive research funding from Intercept Pharmaceuticals. The remaining authors declare no competing interests.

Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1.

Correlation between plasma C4 levels and the relative abundance of Streptococcus thermophilus in response to OCA treatment. Linear and box and whisker plots of: (A,C) plasma C4 levels and (B,D) S. thermophilus relative abundance in the 10 mg OCA group. The correlation between plasma C4 levels and the relative abundance of S. thermophilus were highly significant (FDR = 2.30e-05) based on a time-dependent GEE model.

Figure 2:

Genomic signature of the fecal microbiome associated with OCA administration. (A) A multidimensional scaling (MDS) plot of samples based on the Kendall rank correlation coefficient derived from 782 genes with a time-dependent effect in response to OCA administration based (repeated measure ANOVA, FDRS. thermophilus) over time. (D) Out of 394 total transposases identified in the samples, 32 transposases had significant time-dependent responses to each of the three OCA doses (shown as log10 mean abundance and 95% confidence interval across samples). (E) ROC curves for transposases and plasma C4 (Red: transposase with highest AUC (0.917); Blue: transposase with lowest AUC (0.789); Black: plasma C4 levels (AUC = 0.828)).

Figure 3:

Bacterial metabolic pathways associated with OCA administration. (A) 135 metabolic pathways were significantly associated with OCA administration (repeated measure ANOVA, FDR

Figure 4.

Minimal inhibitory concentrations (MICs) of the two Gram-position bacterial species most strongly associated with the use of OCA in response to two endogenous bile acids and OCA. (A) MICs of the species in response to treatment with the two dominant conjugated primary bile acids found in the human small intestine, glycochenodeoxycholic acid (GCDCA) and glycocholic acid (GCA), under both aerobic and anaerobic conditions. N=3 per measurement. Blue = Physiologically relevant concentrations of bile acids in the human small intestine. (B) MICs of the same bacterial taxa in response to treatment with OCA. N=3 per measurement. Blue = Estimated human small intestinal concentrations of OCA.

Figure 5.

Effect of OCA administration on luminal bile acid concentrations in the murine small intestine and feces. Total (endogenous bile acids and OCA), total endogenous, total primary, and total secondary bile acids in the (A) lumen of the proximal small intestine; (B) lumen of the distal small intestine, and; (C) feces of mice following 14 days of gavage with either water (control, N=5), 0.5% methylcellulose (MC, N=10), or 0.5% methylcellulose with 10 mg/kg obeticholic acid (OCA, N=10). Mean±SE, *p

Figure 6.

Effect of OCA on the proximal and distal small intestinal (PSI and DSI, respectively), as well as the fecal, microbiota composition in mice based on 16S tagged sequencing following 14 days of gavage with water (control), 0.5% methylcellulose (MC), or 0.5% methylcellulose with 10 mg/kg obeticholic acid (OCA).

Figure 7:

The power of the relative abundance of bacterial species to discriminate OCA treatment (day 16) vs. non-treatment (days 1 and 37) as assessed by logistic regression models. (A) The three species with the highest AUC values based on a ROC analysis of the three OCA doses. (B) AUC values based on a ROC analysis using the combination of any two of the three species with the highest AUC values. (C) AUC values based on separate ROC analyses for Day 1 vs. Day 16 and Day 37 vs. Day 16 based on logistic regression analysis. Species with high AUC values (AUC > 0.8) from at least one model are shown. The pseudo-validation AUCs were obtained by applying the logistic model derived from the Day 1 vs. Day 16 dataset (i.e., training set) to the Day 16 vs. Day 37 dataset (i.e., validation set).