Metabolic profiling reveals a contributionof gut microbiota to fatty liver phenotypein insulin-resistant miceMarc-Emmanuel Dumas*†, Richard H. Barton*, Ayo Toye‡, Olivier Cloarec*, Christine Blancher‡, Alice Rothwell‡,Jane Fearnside‡, Roger Tatoud§, Veronique Blanc§, John C. Lindon*, Steve C. Mitchell*, Elaine Holmes*,Mark I. McCarthy‡, James Scott§, Dominique Gauguier‡, and Jeremy K. Nicholson*†

*Department of Biological Chemistry and §Genetics and Genomics Research Institute, Imperial College London, South Kensington, London SW7 2AZ,United Kingdom; and ‡Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom

Edited by Jeffrey I. Gordon, Washington University School of Medicine, St. Louis, MO, and approved June 26, 2006 (received for review February 27, 2006)

Here, we study the intricate relationship between gut microbiotaand host cometabolic phenotypes associated with dietary-inducedimpaired glucose homeostasis and nonalcoholic fatty liver disease(NAFLD) in a mouse strain (129S6) known to be susceptible to thesedisease traits, using plasma and urine metabotyping, achieved by1H NMR spectroscopy. Multivariate statistical modeling of thespectra shows that the genetic predisposition of the 129S6 mouseto impaired glucose homeostasis and NAFLD is associated withdisruptions of choline metabolism, i.e., low circulating levels ofplasma phosphatidylcholine and high urinary excretion of meth-ylamines (dimethylamine, trimethylamine, and trimethylamine-N-oxide), coprocessed by symbiotic gut microbiota and mammalianenzyme systems. Conversion of choline into methylamines bymicrobiota in strain 129S6 on a high-fat diet reduces the bioavail-ability of choline and mimics the effect of choline-deficient diets,causing NAFLD. These data also indicate that gut microbiota mayplay an active role in the development of insulin resistance.

metabonomics � NMR � nonalcoholic fatty liver disease �nutritional genomics � metabolic syndrome

H ighly complex animals such as mammals can be considered as‘‘superorganisms’’ with a karyome, a chondriome, and a

microbiome (1), resulting from a coevolutionary symbiotic ecosys-tem of diverse intestinal microbiota interacting metabolically withthe host (2). Recent molecular analyses of human microbiota 16sribosomal DNA sequences revealed a majority of uncultivated orunknown species with a strong degree of interindividual diversity (3,4). Also, some of the molecular foundations of beneficial symbiotichost–bacteria relationships in the gut were revealed by colonizationof germ-free mice with known microbes and by comparisons of thegenomes of members of the intestinal microbiota (5). For instance,Bacteroides thetaiotaomicron, a dominant member of normal distalintestinal microbiota, hydrolyzes otherwise indigestible dietarypolysaccharides, thus supplying the host with 10–15% of calorificrequirement (6). Gut Lactobacillus spp. are also responsible for asignificant proportion of bile acid deconjugation, a process thatefficiently reduces lipid absorption in the gut (7). Such symbioticrelationships are the result of coevolution and operate at thegenome, proteome, and metabolome levels (6, 8).

Insulin resistance (IR) is central to a cluster of frequent andincreasingly prevalent pathologies, including type 2 diabetes mel-litus, central obesity, hypertension hepatic steatosis, and dyslipide-mia (9). IR contributes to major causes of morbidity and mortalityworldwide (10). Epidemiological and genetic studies in human andanimal models have demonstrated the importance of both geneticand environmental factors in the etiology of IR (9): Dietaryvariation and intervention, in particular, have a strong influence onthe development of IR. Nonalcoholic fatty liver disease (NAFLD),is the most frequent liver condition associated with IR (11). It isassociated with hepatic IR and characterized by hepatic accumu-lation of triglycerides, or steatosis. Although the causes of human

NAFLD are not understood, it has been shown in animal modelsthat choline-deficient diets are associated with NAFLD (12).

The critical involvement of the gut microbiota in biologicalprocesses controlling host metabolic regulations (13), includingthose involved in insulin sensitivity and caloric recovery from thediet, is emerging from recent studies (14): Conventionalized ani-mals have 40% more body fat than germ-free animals. Moreover,diet is known to modulate gut-microbial composition (15), andobesity correlates with variation in the distribution of Bacteroidetesand Firmicutes in mice (16). Hence, symbiotic bacterial contribu-tions to IR and NAFLD should not be overlooked.

Novel approaches are emerging to measure and model metab-olism in diverse compartments in interacting multicellular systemsthat also involve symbiotic microorganisms (2). Alongside func-tional genomic profiling methods such as transcriptomics andproteomics, metabonomics is a metabolic systems-biology ap-proach that can be encapsulated as ‘‘understanding the metabolicresponses of living systems to pathophysiological stimuli by usingmultivariate statistical analysis of biological NMR spectroscopicdata’’ (17, 18). 1H NMR spectroscopy of biofluids has long beenestablished as a method for profiling abnormal biochemistry and,indeed, was applied to describe diabetic and hyperglyceridaemicphenotypes �20 years ago (19). We have recently applied meta-bonomics to characterize the intergenome interactions in mice withsymbiotic gut microflora and parasitic Schistosoma mansoni infec-tion in mice (20). We have also monitored the gut-microbialmetabolite variation in urine from acclimatizing formerly germ-freerats (21).

In this study, we have tested the effects of dietary changes, i.e.,switching from a 5% control low-fat diet (LFD) to a 40% high-fatdiet (HFD), on plasma and urine metabolic 1H NMR profiles ininbred mouse strain 129S6, documented for its susceptibility to IRor NAFLD (22), and in BALBc strain, which exhibits evidence ofresistance to these phenotypes. We characterize here the metabolicprofiles related to the cometabolome homeostatic variation (23)and show that microbial metabolism strongly contributes to aNAFLD metabotype, i.e., a quantitative combination of severalmetabolites, related to IR.

ResultsOverview of the Pathophysiological Effects of Fat-Feeding. Wepresent here background data primarily focused on glucose toler-

Conflict of interest statement: No conflicts declared.

This paper was submitted directly (Track II) to the PNAS office.

Abbreviations: GTT, glucose tolerance test; HFD, high-fat diet; IR, insulin resistance; LFD,low-fat diet; NAFLD, nonalcoholic fatty liver disease; OPLSDA, orthogonal partial least-squares-discriminant analysis; TG, triglyceride; PC, phosphatidylcholine; TMA, trimethyl-amine; TMAO, trimethylamine-N-oxide.

†To whom correspondence may be addressed. E-mail: m.dumas@imperial.ac.uk orj.nicholson@imperial.ac.uk.

© 2006 by The National Academy of Sciences of the USA

www.pnas.org�cgi�doi�10.1073�pnas.0601056103 PNAS � August 15, 2006 � vol. 103 � no. 33 � 12511–12516

MED

ICA

LSC

IEN

CES

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020

ance and glucose-induced insulin secretion in vivo as well asstructural and biochemical markers of hepatic dysfunction.

Glucose Homeostasis and Insulin Secretion. The effects of HFD inBALB�c and 129S6 mice on glucose tolerance and glucose-stimulated insulin secretion in vivo were assessed by glucose toler-ance tests (GTTs) (Fig. 1 A and B). On the LFD, the general patternof glycemic response to the glucose challenge was similar in the twostrains (Fig. 1A). On the HFD, both strains develop fasting hyper-glycemia. Fat-feeding induces marked glucose intolerance in 129S6,as reflected by sustained hyperglycemia throughout the duration ofthe GTT in HFD-fed mice when compared with LFD-fed mice(Fig. 1A). In contrast, in BALB�c mice, glucose tolerance ismarkedly improved by fat-feeding, as reflected by significantlylower plasma glucose values in HFD-fed mice than LFD-fed micethroughout the GTT.

Fasting insulinemia was not significantly different between thestrains on LFDs, but, in response to glucose, 129S6 mice secretemore insulin during the GTT than BALB�c mice (Fig. 1B). Thisapparent increased insulin-secretion capacity in 129S6 when com-pared with BALB�c does not result in improved glucose tolerance(Fig. 1A), suggesting a relative reduction of the biological action ofinsulin in 129S6 mice compared with BALB�c mice. In BALB�cmice, fat-feeding induces a strongly significant enhancement ofglucose-stimulated insulin secretion when compared with the LFD-fed group (Fig. 1B), which may account for concomitant improvedglucose tolerance by HFD in this strain (Fig. 1A). In contrast, in129S6, insulin-secretion pattern and capacity are not significantlyaltered by HFD, indicating that glucose intolerance in HFD-fed129S6 mice develops as the result of insulin resistance rather thaninsulin-secretion deficiency (Fig. 1 A and B).

Plasma Lipid Profiles. To characterize dyslipidemia, we appliedstandard clinical chemistry protocols to compare the concentration

of plasma lipids, including triglycerides (TG) and total, high-densitylipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol,in the four mouse groups (A.T., unpublished work). On LFD, thetwo strains show similar levels of plasma lipids, and prolonged HFDfeeding induced similar hypercholesterolemia in both strains (Fig.4 A–D, which is published as supporting information on the PNASweb site).

Liver Histopathology and Dysfunction. After prolonged high-fatfeeding, 129S6 mice develop micro- and macrovesicular steatosis, asevidenced by the accumulation of fat droplets in the liver (Fig. 1D),whereas liver histology remained unchanged in BALB�c mice (Fig.1C). To further characterize steatosis, we assayed hepatic TG (oneof the major storage forms of lipids in liver) and circulating levelsof aspartate aminotransferase (AST) and alanine aminotransferase(ALT) (markers of hepatic dysfunction). Liver TG are significantlyincreased (3.3-fold) in fat-fed 129S6 mice (Fig. 4E). The levels ofboth AST and ALT are higher in 129S6 compared with BALB�cmice on LFD. Fat-feeding induces a significant increase in ALT andAST in both strains that is more prominent in 129S6 than BALB�cmice (Fig. 1 F and G).

Body Weight Follow-Up. To characterize obesity, we monitored bodyweight at different ages. On LFDs, body weight is similar in 129S6and BALB�c mice at 2, 3, and 5 months of age. In 129S6 mice, bodyweight is significantly increased after 3, 7, and 15 weeks of HFDwhen compared with age- and strain-matched LFD-fed mice,whereas it remains similar in LFD- and HFD-fed BALB�c mice(data not shown).

Overall, our observations provide confirmatory evidence of thestrong susceptibility of 129S6 mice to NAFLD, impaired glucosetolerance, dyslipidemia, and obesity in response to fat-feeding asobserved by Biddinger et al. (22) and provide evidence of resistanceto these pathologies in BALB�c mice.

Fig. 1. Pathophysiology of the response of BALB�c and 129S6 mice to prolonged fat-feeding. Effect of glucose injection on blood glucose (A) and plasma insulin(B) concentrations in BALB�c and 129S6 mice fed an LFD or HFD. H&E-stained liver sections from 5-month-old BALB�c (C) and 129S6 (D) mice on HFD, showingmicro- and macrovesicular steatosis in 129S6 mice (magnification, �20). Plasma glucose and insulin values were obtained from �53 (BALB�c-LFD), 34(BALB�c-HFD), and 40 (129S6-LFD, 129S6-HFD) mice. Significant differences (P � 0.05) between HFD-fed and LFD-fed 129S6 mice (†), between HFD-fed andLFD-fed BALB�c mice (‡), between LFD-fed 129S6 and BALB�c mice (�), and between HFD-fed 129S6 and BALB�c mice (§) are shown.

12512 � www.pnas.org�cgi�doi�10.1073�pnas.0601056103 Dumas et al.

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020

Metabolic Profiling by 1H NMR Spectroscopy at 4 Months After HFDInduction. The chronic effects of the HFD induction on urinary andplasma metabolic profiles at 4 months after induction for 129S6 andBALB�c mouse strains are illustrated in Fig. 2 and Table 1. Asshown in Fig. 2A, a typical 600-MHz 1H NMR spectrum of oneHFD-fed 129S6 mouse plasma characterizes common markers ofinsulin resistance: (CH2)n and CH3 resonances from components oflipoproteins, e.g., cholesterol esters, TG, and phospholipids. A peakfrom the N-methyl group from phosphatidylcholine (PC) can beobserved. Low-molecular-mass metabolites, e.g., lactate, alanine,and glucose, are also present (Fig. 2A).

A typical 600-MHz 1H NMR urinary spectrum from aNAFLD-sensitive 129S6 mouse on the HFD is illustrated in Fig.2B, showing contributions of a wide range of low-molecular-mass metabolites (�1 kDa) from both mammalian metabolismand associated gut-microbial systems (24, 25). The urinary 1HNMR spectrum of this 129S6 mouse on HFD is dominated bymicrobiota-derived methylamines: dimethylamine, trimethyl-amine (TMA), and trimethylamine-N-oxide (TMAO). We haveshown that these metabolites are derived only from symbioticbacterial metabolism and not from mammalian metabolism inmice (26). Other gut microbiota-derived metabolites includeformate and hippurate. Mammalian metabolites such as creat-inine, creatine, and taurine are also excreted as well as inter-mediary metabolites, such as pyruvate, citrate, oxaloacetate,succinate, lactate, glycerate, 2-oxoisocaproate, 2-oxobutyrate,isovalerate, acetate, and acetotacetate (27) (Fig. 2B).

Data Analysis. Plasma metabolic profiling provides an insight onlipid and energy metabolism, whereas urine metabolic profiling

provides a complementary description of intermediary metabolism(Fig. 2 A and B). These complementary data sets are then used tobuild orthogonal partial least-squares-discriminant analysis

Fig. 2. Plasma and urine metabolic profiling by 1H NMR spectroscopy of the response of BALB�c and 129S6 mice to dietary intervention at 5 months of age.Plasma (A) and urine (B) 600-MHz 1H NMR spectra from typical 5-month-old 129S6 mice on HFD. OPLSDA score plots for plasma (C) and urine (D) metabolic profiles.HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very low-density lipoprotein.

Table 1. 1H NMR-derived metabotypes significantly associatedwith the response of BALB/c and 129S6 mice to dietaryintervention at 5 months of age

BiomarkersOPLSDA coef �, ppm

BALB/cLFD

BALB/cHFD

129S6LFD

129S6HFD

PC (p) 3.219 �0.24 0.71 �0.43 �0.07Choline (u) 3.203 �0.28 0.56 �0.26 0.09TMA (u) 2.892 �0.28 0.20 �0.41 0.70TMAO (u) 3.2705 �0.35 0.14 �0.39 0.83DMA (u) 2.728 �0.27 0.30 �0.49 0.69MMA (u) 2.77 �0.26 0.23 �0.47 0.72DMG (u) 2.93 0.81 �0.14 �0.40 �0.33Creatine (u) 3.0405 0.69 �0.42 0.06 �0.51Glycerate (u) 4.0975 �0.52 �0.50 0.81 0.09Gluratate (u) 1.792 �0.18 �0.48 0.84 �0.39Isovalerate (u) 2.19 �0.41 �0.47 0.88 �0.17Pyruvate (u) 2.347 �0.15 0.81 �0.50 0.00

The OPLSDA model coefficients are listed for each of the models reportedin Fig. 2. For each metabolite, we report the chemical shift � (in parts permillion) and the OPLSDA-derived correlation (coef) for each class in the model:BALB/c LFD, BALB/c HFD, 129/S6 LFD, and 129/S6 HFD. A positive (negative)correlation means a higher (lower) intensity of the NMR resonance of themetabolite significantly associated to the corresponding class. (p), plasma; (u),urine.

Dumas et al. PNAS � August 15, 2006 � vol. 103 � no. 33 � 12513

MED

ICA

LSC

IEN

CES

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020

(OPLSDA) (28) models, focusing on the differences among thefour experimental groups, i.e., BALB�c LFD, BALB�c HFD,129S6 LFD, and 129S6 HFD, by using only three latent variables ordiscriminant scores (Fig. 2 C and D).

Plasma Metabonomic Model. In the plasma OPLSDA model (Fig. 2Cand Table 1), it is possible to differentiate the effects of both geneticbackground (strain) and environmental manipulation (diet) on thescore plot: The main effect of component T1 is to discriminate thediet induction, whereas T2 discriminates the genetic backgroundand T3 the specific response of 129S6 to HFD induction (Fig. 2C).The coefficients characterizing each experimental group used tobuild the OPLSDA model are represented as pseudospectra (Fig.5, which is published as supporting information on the PNAS website). On the LFD, both BALB�c (Fig. 5A) and 129S6 (Fig. 5C)plasma metabolic profiles are quite similar, with negative coeffi-cients for TG and PC resonances. This finding indicates that the twostrains on the LFD have low TG and low PC levels when comparedwith the HFD-matched animals. By contrast, the plasma metabolicresponse is different in HFD-fed strains. The plasma of BALB�cmice on HFD is characterized by high TG and PC levels (Table 1).This hyperlipidemia is linked to the handling of the HFD challenge(Fig. 5B). However, 129S6 mice on the HFD show dyslipidemia. Wealso observed increased glucose signals in the NMR profilessignificantly associated with fat-fed 129S6 mice (such correlation isdirectly relevant to impaired glucose homeostasis assessed in thisgroup by glucose tolerance testing) as well as pyruvate and TMAOand decreased PC (Fig. 5D). The metabolic fate of dietary cholineis monitored by urine metabolic profiling.

Urine Metabonomic Model. The urinary OPLSDA model discrimi-nates the four groups (Fig. 2D and Table 1): The major effect onT1 discriminates the HFD induction, whereas T2 and T3 describe theresponse of each strain to the HFD induction. Urine samples fromthe reference strain BALB�c on the LFD show higher excretion ofcreatine and N,N-dimethylglycine compared with the other threegroups (Fig. 6A, which is published as supporting information onthe PNAS web site). On the HFD, BALB�c mice predominantlydemonstrate higher excretion of pyruvate (Fig. 6B). However,129S6 behaves differently on the LFD, as an increased excretion ofglycerate, isovalerate, and glutarate is observed (Table 1 and Fig.6C). In fact, glycerate, isovalerate, and glutarate are excreted bystrain 129S6 only. Glyceric aciduria in 129S6 mice on LFD refers toexcretion of a glycolysis intermediate and suggests a genetic pre-disposition toward an impaired glycolysis of 129S6 mice (29). Instriking contrast to BALB�c, 129S6 mice on the HFD excretemainly methylamines (dimethylamine, TMA, and TMAO), whichare choline derivatives produced only by microbiota (26) (Table 1and Fig. 6D).

Short-Term Metabolic Signatures Linked to HFD Induction. We havealso monitored the short-term urinary metabolic response of 129S6and BALB�c mice to the HFD challenge by daily urine samplingduring 1 week before and after the diet induction (Fig. 7, which ispublished as supporting information on the PNAS web site). 1HNMR urinary metabolic profiling shows that both strains reactimmediately to the HFD switch with a substantial excretion ofmethylamines, but this finding is more dramatic in 129S6 mice.

Characterization of the Link Between Choline Bioavailability andNAFLD. We further characterized the link between NAFLD andcholine bioavailability by: (i) quantifying the levels of circulatingtotal plasma choline and plasma TG to show an imbalance betweentotal choline and plasma TG in 129S6 (Fig. 8, which is published assupporting information on the PNAS web site) and (ii) showing thatquantitative variation in dietary choline induces an inverse quan-titative variation in liver fat content in 129S6 mice (Fig. 9, which ispublished as supporting information on the PNAS web site).

Overall, the results derived from metabolic profiling monitormainly present classical markers of insulin resistance, i.e., lipidemia,glycemia, and phospholipids (PC), but also gut microbiota biomar-kers. These results reveal a marked decrease of PC in the plasmaof fat-fed 129S6 strain compared with fat-fed BALB�c mice. Thisdecrease is associated with a substantial increase of exclusivelybacterial-origin methylamines in the urine of 129S6 mice.

DiscussionWe show in this study a significant association between a specificmetabotype, e.g., low plasma phosphatidylcholine (PC) and highurinary methylamines and genetic predisposition to HFD-inducedNAFLD in mice. Thus, we confirm and complement the initialdescription of NAFLD in fat-fed 129S6 mice by Biddinger et al. (22).In fact, methylamines are coprocessed by gut microbiota: We havereported that germ-free mice do not excrete TMA and have shownthe fundamental role of microbiota in TMA production from itsprecursor choline (26). Hence, urinary excretion of methylamines(TMA, TMAO, and dimethylamine) is directly related to gut-microbiota metabolism. Thus, these metabolites can be used as aprobe of microbial metabolism of choline in the metabolic cross-talk between host and symbionts. This microbial bypass leads to areduction of choline bioavailability for the host (compared with thedietary fatty acid uptake in liver induced by HFD), as denoted bylower PC levels (see Fig. 8), and seem to trigger NAFLD in return(12). Such significant association indicates that the altered gut-microbial metabolism of choline plays a role in the development ofNAFLD, as detailed below.

The Metabolic Fate of Choline and the NAFLD Metabotype: LowPlasma PC and High Urinary Methylamines. The pathways of cholinemetabolism involve a complex superorganism host�symbiont mo-lecular cross-talk (Fig. 3A): there are three major pathways usingdietary choline, two pure mammalian pathways and a sym-xenobiotic pathway (30). Each one of these pathways has beenassociated to at least one experimental group: (i) urinary metabolicprofiling shows that dietary choline is excreted as NN-dimethylglycine, ultimately leading to the production of creatineand creatinine in BALB�c on the LFD, (ii) choline is also convertedto methylamines (TMA, TMAO, and dimethylamine) by gut mi-crobiota in HFD animals in general and 129S6 in particular, (iii)HFD induction leads to a discordant phenotype regarding thecirculating PC levels, deriving from choline: Plasma PC levels aresignificantly lower in 129S6 than in BALB�c mice, even though thediet is supplemented with choline (see Table 2, which is publishedas supporting information on the PNAS web site). Mice from the129S6 strain on the HFD are characterized by high methylaminesand low plasma PC, and they also develop NAFLD (22). Suchdistinct metabotypes can be explained as described below.

Methylamines and Gut Microbiota. The first reaction of the meth-ylamine pathway involves conversion of dietary choline into TMAby gut microbiota (31). The microbiota-processed metabolites, orsym-xenobiotic metabolites (30), are absorbed by microvillae andpenetrate the systemic circulation through the portal vein betweenthe intestinal tract and liver (32), and TMA is cleared substantiallyby hepatic first-pass metabolism. This input of xenobiotic or sym-xenobiotic compounds from either symbiotic microbiota or vegetalorigin triggers a hepatic detoxification process, involving flavin-containing monooxygenase 3 (FMO3) (32).

Since we showed the gut-microbial origin of TMA and methyl-amines (26), our more recent results provide indirect evidence thatdifferent metabolic activities of microbiota populations exist andthat quantitative changes in these microbial metabolic activities aresignificantly associated with genetically distinct inbred strains re-sulting in distinct metabolic phenotypes (33, 34). In fact, resultsfrom our study suggest the existence of strain-specific and geneti-cally determined selection of gut-microbial metabolism under HFD

12514 � www.pnas.org�cgi�doi�10.1073�pnas.0601056103 Dumas et al.

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020

challenge, based on strain-dependent variation in excretion ofmethylamines. A possible involvement of gut microbiota in thedevelopment of IR-related processes has been suggested by Back-hed et al. (14) who reported that the gut microbiota affects energyharvest from the diet and energy storage in the host. It has also beenobserved that Helicobacter pylori interacts with the appetite-regulating peptides, i.e., leptin and ghrelin (35, 36).

Gut Microbiota Mimic Choline-Deficient Diets. Choline-deficientdiets have been consistently associated with hepatic steatosis,which is reversible by choline i.v. infusion (12). We have alsoshown that quantitative variation in dietary choline induces aninverse quantitative variation in liver fat content (see Fig. 9). Weshow here that lower plasma PC levels in strain 129S6 on HFDcompared with BALB�c mice can be explained by reducedbioavailability of choline (see Fig. 8) because of conversion ofcholine into methylamines by gut microbiota, with subsequenturinary excretion. This mechanism thus mimics a choline-deficient diet. This microbiota-related reduced choline bioavail-ability may result in the inability to synthesize PC necessary forthe assembly and secretion of very-low-density lipoprotein(VLDL) (37) and subsequent accumulation of TG in liver.Methylamines also induce hepatotoxicity and hepatocarcinoge-nicity in rats (38). Indeed, microsomal FMO-detoxificationenzymatic systems have been evolutionarily coselected towardthe assimilation of biologically active natural compounds in-volved in biological defense signaling (39). This enzymaticsystem detoxifies soft nucleophilic functional groups of naturalorigin, such as alkaloids, with basic side chains, and organicsulfur xenobiotics. Microbiota-derived methylamines, predomi-nantly excreted in urine, share the same metabolic detoxificationprocess and may also share the same toxicity as other softnucleophiles. Recent metagenomic studies have also shown astrong interaction between gut flora and detoxification ofxenobiotics (40).

Putative Mechanisms of Hepatotoxicity. We propose, in Fig. 3B, anindirect mechanism of hepatotoxicity involving: (i) microbialconversion choline into TMA, thus reducing bioavailability ofcholine, (ii) influx of fatty acids in the liver, (iii) generation ofradical oxidative species via reprocessing of fatty acids andoxidative stress, and (iv) lack of VLDL secretion generatingsteatosis and NAFLD. In that regard, small variations in circu-lating levels of choline and PC linked to microbial bypassbetween the two strains, associated with dietary fatty acidchallenge, might trigger irreversible liver damage.

ConclusionsOur observations indicate that gut-microbial metabolism altersthe metabolism of the mammalian host. This work stronglysupports the idea that complex metabolic disease traits are aproduct of extended genome (superorganism) perturbationsunder the influence of an external stressor, in this case, a dietarychange. We show that a specific metabotype with low plasma PCand high gut microbiota-mediated urinary excretion of methyl-amines is associated with the predisposition to impaired glucosehomeostasis and NAFLD. Along with mechanisms of suppres-sion of inflammatory response (41, 42) or regulation of fatstorage by microbiota (14), we are now able to describe adiet-induced mechanism of steatosis triggered by symbioticmicrobiota, another example of ‘‘the thin line between gutcommensal and pathogen’’ noted by Gilmore and Ferretti (43).It is also likely that changes in the Western lifestyle inducechanges in the gut-microbial ecology. Such changes are likely toaffect the nutrigenomic or pharmacometabonomic predisposi-tion (44) toward different energy storage capabilities and pa-thologies and, in the end, personalized healthcare (23).

Materials and MethodsAnimals and Treatment. Male mice from two inbred strains,BALB�cOxjr (BALB�c) and 129S6�SvEvOxjr (129S6), werebred locally by using a stock originating from The JacksonLaboratory, Bar Harbor, ME. Mice were weaned at 3 weeks,housed in groups of 10–12 animals, maintained under standardbreeding conditions, and fed ad libitum a standard carbohydrateand 5% LFD (B & K Universal, Hull, U.K.) from weaning to 5weeks of age. At 5 weeks of age, one group of mice from eachstrain was transferred to a 40% HFD (Special Diets Services,Witham, Essex, U.K.), containing 32% lard and 8% corn oil,whereas strain- and age-matched control groups remained onthe LFD for the duration of the diet trial. The detailed dietformulation is available in Table 2. Mice were maintained undera 12-h–12-h light–dark regime, with lights off at 7 p.m. Allprocedures were carried out in accordance with U.K. HomeOffice guidelines on animal welfare and license conditions andUniversity of Oxford guidelines on animal welfare.

Glucose Tolerance and Insulin-Secretion Tests. GTTs were per-formed in mice fed the HFD for 4 months and age-matchedcontrols. After an overnight fast, mice were anesthetized by i.p.injection of sodium pentobarbital (Sagatal; Rhone Merieux, Ath-ens, GA). Mice were injected i.p. with a single dose of 2 g of glucoseper kg of body weight. Blood samples were collected through the tail

Fig. 3. The symbiotic methylamines’ metabolic pathway (A) and their putative mechanism of liver toxicity (B). The OPLS-derived correlation is represented bystandard color-coding for each metabolite: red square, positive correlation; green square, negative correlation, meaning a higher (lower) metaboliteconcentration in the corresponding group. FFA, free fatty acids.

Dumas et al. PNAS � August 15, 2006 � vol. 103 � no. 33 � 12515

MED

ICA

LSC

IEN

CES

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020

vein before glucose injection and at 15, 30, and 75 min afterward.Blood glucose concentrations were immediately determined byusing a glucose meter (Accucheck; Roche Diagnostics, Lewes,U.K.). Blood samples were centrifuged, and plasma was separatedfor insulin assays. Plasma insulin concentration was determined byELISA (Mercodia, Uppsala, Sweden).

Biofluid Sample Collection. Four days after the glucose tolerancetest, 24-h urinary samples (9 a.m. to 9 a.m.) were collected frommice maintained in individual metabolic cages. Urinary samplescollected in a solution of 1% (wt�vol) sodium azide were centri-fuged to remove solid particles and kept at �80°C until assayed.After an overnight fast, mice were killed by exsanguination. Plasmawas separated by centrifugation and stored at �80°C until NMRanalysis.

Liver Histology. Liver biopsies from these fasted mice were fixed inneutral buffered formalin solution (Surgipath Europe, Peterbor-ough, U.K.) for �24 h, dehydrated, embedded in paraffin, andsectioned at 4 �m. Staining of liver sections was carried out withH&E. Results are based on assessment of five or more mice perstrain and diet group examined.

1H NMR Spectroscopy. Mouse urine samples were prepared byusing 200 �l of urine mixed with 200 �l of water and 200 �l of0.1 M phosphate buffer solution (10% 2H2O�H2O vol�vol, with0.05% sodium 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate forchemical shift reference at �-0.0) in 96-well plates for high-throughput flow-injection NMR acquisition. Plasma samples

were prepared by using a 100-�l aliquot diluted in 400 �l of a 9g�liter saline solution (20% 2H2O�H2O vol�vol). Standard 1HNMR spectra were measured on a spectrometer (Bruker, Rhein-stetten, Germany) operating at 600.22 MHz 1H frequency, asdescribed (20). The 1H NMR spectra were phase and baselinecorrected by using in-house software (T. Ebbels and H. Keun,personal communication) and were imported into Matlab at highresolution as described (45). The regions �-6.0–5.5 and �-5.0–4.5were removed to eliminate baseline effects of imperfect watersignal presaturation. Each spectrum was normalized to a con-stant intensity sum, and each variable was mean centered.

OPLSDA. The method allows enhanced focus on strain and dietintervention while minimizing other biological�analytical variation.Sample classes were modeled by using the OPLS algorithm. Thisalgorithm derives from the partial least-squares regression method(28). In discriminant analysis version, the method explains themaximum separation between class samples Y (n dummy variablesfor n classes) by using the NMR data X, by decomposing thecovariation matrix (YTX) into n � 1 O-PLS components and severalorthogonal signal correction components (46). Further detailson standard OPLS implementation in metabonomics have beengiven in ref. 45. The model coefficients locate the NMR variablesassociated with a specific class in Y.

We thank Terry M. Hacker (Medical Research Council, Harwell, U.K.)for helping with liver histology. This study was supported by WellcomeTrust Functional Genomics Initiative Grant 066786 [Biological Atlas ofInsulin Resistance (www.bair.org.uk)] and Wellcome Trust Senior Fel-lowship in Basic Biomedical Science 057733 (to D.G.).

1. Lederberg, J. (2000) Science 288, 287–293.2. Nicholson, J. K., Holmes, E., Lindon, J. C. & Wilson, I. D. (2004) Nat.

Biotechnol. 22, 1268–1274.3. Bik, E. M., Eckburg, P. B., Gill, S. R., Nelson, K. E., Purdom, E. A., Francois,

F., Perez-Perez, G., Blaser, M. J. & Relman, D. A. (2006) Proc. Natl. Acad. Sci.USA 103, 732–737.

4. Eckburg, P. B., Bik, E. M., Bernstein, C. N., Purdom, E., Dethlefsen, L.,Sargent, M., Gill, S. R., Nelson, K. E. & Relman, D. A. (2005) Science 308,1635–1638.

5. Xu, J. & Gordon, J. I. (2003) Proc. Natl. Acad. Sci. USA 100, 10452–10459.6. Xu, J., Bjursell, M. K., Himrod, J., Deng, S., Carmichael, L. K., Chiang, H. C.,

Hooper, L. V. & Gordon, J. I. (2003) Science 299, 2074–2076.7. Narushima, S., Ito, K., Kuruma, K. & Uchida, K. (2000) Lipids 35, 639–644.8. Mutch, D. M., Simmering, R., Donnicola, D., Fotopoulos, G., Holzwarth, J. A.,

Williamson, G. & Corthesy-Theulaz, I. (2004) Physiol. Genom. 19, 22–31.9. Saltiel, A. R. & Kahn, C. R. (2001) Nature 414, 799–806.

10. Zimmet, P., Alberti, K. G. & Shaw, J. (2001) Nature 414, 782–787.11. Day, C. P. & James, O. F. (1998) Hepatology 27, 1463–1466.12. Buchman, A. L., Dubin, M. D., Moukarzel, A. A., Jenden, D. J., Roch, M., Rice,

K. M., Gornbein, J. & Ament, M. E. (1995) Hepatology 22, 1399–1403.13. Backhed, F., Ley, R. E., Sonnenburg, J. L., Peterson, D. A. & Gordon, J. I.

(2005) Science 307, 1915–1920.14. Backhed, F., Ding, H., Wang, T., Hooper, L. V., Koh, G. Y., Nagy, A.,

Semenkovich, C. F. & Gordon, J. I. (2004) Proc. Natl. Acad. Sci. USA 101,15718–15723.

15. Mai, V. (2004) Nutr. Rev. 62, 235–242.16. Ley, R. E., Backhed, F., Turnbaugh, P., Lozupone, C. A., Knight, R. D. &

Gordon, J. I. (2005) Proc. Natl. Acad. Sci. USA 102, 11070–11075.17. Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. (2002) Nat. Rev. Drug

Discov. 1, 153–161.18. Nicholson, J. K., Lindon, J. C. & Holmes, E. (1999) Xenobiotica 29, 1181–1189.19. Nicholson, J. K., O’Flynn, M. P., Sadler, P. J., Macleod, A. F., Juul, S. M. &

Sonksen, P. H. (1984) Biochem. J. 217, 365–375.20. Wang, Y., Holmes, E., Nicholson, J. K., Cloarec, O., Chollet, J., Tanner, M.,

Singer, B. H. & Utzinger, J. (2004) Proc. Natl. Acad. Sci. USA 101, 12676–12681.

21. Nicholls, A. W., Mortishire-Smith, R. J. & Nicholson, J. K. (2003) Chem. Res.Toxicol. 16, 1395–1404.

22. Biddinger, S. B., Almind, K., Miyazaki, M., Kokkotou, E., Ntambi, J. M. &Kahn, C. R. (2005) Diabetes 54, 1314–1323.

23. Nicholson, J. K., Holmes, E. & Wilson, I. D. (2005) Nat. Rev. Microbiol. 3,431–438.

24. Nicholson, J. K., Sadler, P. J., Bales, J. R., Juul, S. M., MacLeod, A. F. &Sonksen, P. H. (1984) Lancet 2, 751–752.

25. Bales, J. R., Higham, D. P., Howe, I., Nicholson, J. K. & Sadler, P. J. (1984)Clin. Chem. 30, 426–432.

26. al-Waiz, M., Mikov, M., Mitchell, S. C. & Smith, R. L. (1992) Metabolism 41,135–136.

27. Lindon, J. C., Nicholson, J. K., Holmes, E. & Everett, J. R. (2000) ConceptMagnetic Res. 12, 289–320.

28. Trygg, J. & Wold, S. (2003) J. Chemometrics 17, 53–64.29. Manuel Keenoy, B., Maggetto, C. & Malaisse, W. J. (1993) Biochem. Med.

Metab. Biol. 50, 35–53.30. Nicholson, J. K. & Wilson, I. D. (2003) Nat. Rev. Drug Discov. 2, 668–676.31. Zeisel, S. H., Wishnok, J. S. & Blusztajn, J. K. (1983) J. Pharmacol. Exp. Ther.

225, 320–324.32. Lang, D. H., Yeung, C. K., Peter, R. M., Ibarra, C., Gasser, R., Itagaki, K.,

Philpot, R. M. & Rettie, A. E. (1998) Biochem. Pharmacol. 56, 1005–1012.33. Robosky, L. C., Wells, D. F., Egnash, L. A., Manning, M. L., Reily, M. D. &

Robertson, D. G. (2005) Toxicol. Sci. 87, 277–284.34. Holmes, E. & Nicholson, J. (2005) Toxicol. Sci. 87, 1–2.35. Nwokolo, C. U., Freshwater, D. A., O’Hare, P. & Randeva, H. S. (2003) Gut

52, 637–640.36. Azuma, T., Suto, H., Ito, Y., Ohtani, M., Dojo, M., Kuriyama, M. & Kato, T.

(2001) Gut 49, 324–329.37. Jiang, X.-C., Li, Z., Liu, R., Yang, X. P., Pan, M., Lagrost, L., Fisher, E. A. &

Williams, K. J. (2005) J. Biol. Chem. 280, 18336–18340.38. Lin, J. K. & Ho, Y. S. (1992) Food Chem. Toxicol. 30, 695–702.39. Ziegler, D. M. (1990) Trends Pharmacol. Sci. 11, 321–324.40. Gill, S. R., Pop, M., Deboy, R. T., Eckburg, P. B., Turnbaugh, P. J., Samuel,

B. S., Gordon, J. I., Relman, D. A., Fraser-Liggett, C. M. & Nelson, K. E. (2006)Science 312, 1355–1359.

41. Macpherson, A. J., Gatto, D., Sainsbury, E., Harriman, G. R., Hengartner, H.& Zinkernagel, R. M. (2000) Science 288, 2222–2226.

42. Macpherson, A. J. & Uhr, T. (2004) Science 303, 1662–1665.43. Gilmore, M. S. & Ferretti, J. J. (2003) Science 299, 1999–2002.44. Clayton, T. A., Lindon, J. C., Cloarec, O., Antti, H., Charuel, C., Hanton, G.,

Provost, J. P., Le Net, J. L., Baker, D., Walley, R. J., et al. (2006) Nature 440,1073–1077.

45. Cloarec, O., Dumas, M. E., Trygg, J., Craig, A., Barton, R. H., Lindon, J. C.,Nicholson, J. K. & Holmes, E. (2005) Anal. Chem. 77, 517–526.

46. Wold, S., Antti, H., Lindgren, F. & Ohman, J. (1998) Chemometrics IntelligentLab. Syst. 44, 175–185.

12516 � www.pnas.org�cgi�doi�10.1073�pnas.0601056103 Dumas et al.

Dow

nloa

ded

by g

uest

on

Sep

tem

ber

24, 2

020