Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency

Abstract

Background & aims: Nonalcoholic fatty liver disease affects up to 30% of the US population, but the mechanisms underlying this condition are incompletely understood. We investigated how diet standardization and choline deficiency influence the composition of the microbial community in the human gastrointestinal tract and the development of fatty liver under conditions of choline deficiency.

Methods: We performed a 2-month inpatient study of 15 female subjects who were placed on well-controlled diets in which choline levels were manipulated. We used 454-FLX pyrosequencing of 16S ribosomal RNA bacterial genes to characterize microbiota in stool samples collected over the course of the study.

Results: The compositions of the gastrointestinal microbial communities changed with choline levels of diets; each individual's microbiome remained distinct for the duration of the experiment, even though all subjects were fed identical diets. Variations between subjects in levels of Gammaproteobacteria and Erysipelotrichi were directly associated with changes in liver fat in each subject during choline depletion. Levels of these bacteria, change in amount of liver fat, and a single nucleotide polymorphism that affects choline were combined into a model that accurately predicted the degree to which subjects developed fatty liver on a choline-deficient diet.

Conclusions: Host factors and gastrointestinal bacteria each respond to dietary choline deficiency, although the gut microbiota remains distinct in each individual. We identified bacterial biomarkers of fatty liver that result from choline deficiency, adding to the accumulating evidence that gastrointestinal microbes have a role in metabolic disorders.

Copyright © 2011 AGA Institute. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1. Experimental Design

Participants were fed a controlled research diet that included adequate daily choline intake during the baseline period (red). During depletion, subjects were fed a diet very low in choline until they demonstrated signs of deficiency or for a maximum of 42 days (grey). The 10-day repletion diet was very rich in choline (green). Arrows indicate timing of stool samples.

Figure 2. Hierarchical clustering of gut microbiome samples

A. Hierarchical clustering based on OTUs at 97% sequence similarity. Samples are colored by subject. B. Hierarchical clustering based on ARISA profiles, a DNA fingerprinting technique.

Figure 2. Hierarchical clustering of gut microbiome samples

A. Hierarchical clustering based on OTUs at 97% sequence similarity. Samples are colored by subject. B. Hierarchical clustering based on ARISA profiles, a DNA fingerprinting technique.

Figure 3. Diagrams of bacterial sequence proportions by time point at the Class level

The key shows ordering of time points in the ring: B1-baseline, B2-standard diet, D1-choline-deficient diet, D2-end of choline-deficient diet, R1-high dietary choline and R2-second high dietary choline sample (subjects 34, 32, 37, 39). Blue subject labels designate those who developed fatty liver (liver fat change ≥ 28%) on a choline deficient diet; those with red labels did not. Subjects 4 and 29 do not have all samples, and time points are labeled.

Figure 3. Diagrams of bacterial sequence proportions by time point at the Class level

The key shows ordering of time points in the ring: B1-baseline, B2-standard diet, D1-choline-deficient diet, D2-end of choline-deficient diet, R1-high dietary choline and R2-second high dietary choline sample (subjects 34, 32, 37, 39). Blue subject labels designate those who developed fatty liver (liver fat change ≥ 28%) on a choline deficient diet; those with red labels did not. Subjects 4 and 29 do not have all samples, and time points are labeled.

Figure 4. Distribution of Gammaproteobacteria abundance by time point by subject

Plot of logged standardized sequence frequencies (log10) for Gammaproteobacteria colored by subject.

Figure 5. Gammaproteobacteria and Erysipelotrichi abundance, particularly when combined with subject geneotype, predicts choline deficiency induced fatty liver

Regressions of A. Gammaproteobacteria B1 abundance and B. Erysipelotrichi B1 abundance against the Liver fat/Spleen fat (LF:SF) % change from baseline (B1) to choline deficient (D2) diet. C. Regression of PCA1 from PCA of Gammaproteobacteria and Erysipelotrichi B1 abundance against the LF:SF % change from B1 to D2. D. Regression of PCA1 from PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance (6B) against the LF:SF % change from B1 to D2.

Figure 5. Gammaproteobacteria and Erysipelotrichi abundance, particularly when combined with subject geneotype, predicts choline deficiency induced fatty liver

Regressions of A. Gammaproteobacteria B1 abundance and B. Erysipelotrichi B1 abundance against the Liver fat/Spleen fat (LF:SF) % change from baseline (B1) to choline deficient (D2) diet. C. Regression of PCA1 from PCA of Gammaproteobacteria and Erysipelotrichi B1 abundance against the LF:SF % change from B1 to D2. D. Regression of PCA1 from PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance (6B) against the LF:SF % change from B1 to D2.

Figure 5. Gammaproteobacteria and Erysipelotrichi abundance, particularly when combined with subject geneotype, predicts choline deficiency induced fatty liver

Regressions of A. Gammaproteobacteria B1 abundance and B. Erysipelotrichi B1 abundance against the Liver fat/Spleen fat (LF:SF) % change from baseline (B1) to choline deficient (D2) diet. C. Regression of PCA1 from PCA of Gammaproteobacteria and Erysipelotrichi B1 abundance against the LF:SF % change from B1 to D2. D. Regression of PCA1 from PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance (6B) against the LF:SF % change from B1 to D2.

Figure 5. Gammaproteobacteria and Erysipelotrichi abundance, particularly when combined with subject geneotype, predicts choline deficiency induced fatty liver

Regressions of A. Gammaproteobacteria B1 abundance and B. Erysipelotrichi B1 abundance against the Liver fat/Spleen fat (LF:SF) % change from baseline (B1) to choline deficient (D2) diet. C. Regression of PCA1 from PCA of Gammaproteobacteria and Erysipelotrichi B1 abundance against the LF:SF % change from B1 to D2. D. Regression of PCA1 from PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance (6B) against the LF:SF % change from B1 to D2.

Figure 6. The PEMT SNP affects risk of developing choline deficiency induced fatty liver

A. Variability plot of PEMT genotype for SNP rs12325817 and the % change in Liver fat/Spleen fat ratio (LF:SF). WT, wildtype; HET, heterozygous; HO, homozygous. B. PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance. Open circles indicate subjects who did not develop fatty liver; closed squares are subjects who did.

Figure 6. The PEMT SNP affects risk of developing choline deficiency induced fatty liver

A. Variability plot of PEMT genotype for SNP rs12325817 and the % change in Liver fat/Spleen fat ratio (LF:SF). WT, wildtype; HET, heterozygous; HO, homozygous. B. PCA of PEMT genotype for rs12325817, Gammaproteobacteria and Erysipelotrichi B1 abundance. Open circles indicate subjects who did not develop fatty liver; closed squares are subjects who did.