Microbes and Metabolism

Understanding the role of gutmicrobiome–host metabolic signaldisruption in health and diseaseElaine Holmes, Jia V. Li, Thanos Athanasiou, Hutan Ashrafian andJeremy K. Nicholson

Department of Surgery and Cancer, Imperial College London, London, UK

Review

There is growing awareness of the importance of the gutmicrobiome in health and disease, and recognition thatthe microbe to host metabolic signalling is crucial tounderstanding the mechanistic basis of their interaction.This opens new avenues of research for advancingknowledge on the aetiopathologic consequences of dys-biosis with potential for identifying novel microbially-related drug targets. Advances in both sequencing tech-nologies and metabolic profiling platforms, coupledwith mathematical integration approaches, herald anew era in characterizing the role of the microbiomein metabolic signalling within the host and have farreaching implications in promoting health in both thedeveloped and developing world.

Implications of host–microbiome metabolic interactionsin health and diseaseThe symbiosis between the human host and gut microbiotacan trigger specific biological responses both locally andsystemically. Of the 55 bacterial divisions, only two areprominent in mammalian gut microbiota (Bacteroidetesand Firmicutes), and it is, therefore, likely that host eu-karyotic organisms have co-evolved with gut microbiota inthe context of this symbiosis to achieve a physiologicalhomeostasis [1]. Individual bacterial species presentunique pathological effects and, similarly, shifts in gutbacterial colonies can also prompt specific disease-inducingactivity (dysbiosis) or disease-protective activity (probio-sis). This balance between the bacterial host defence rein-forcement and proinflammatory pathology was firstproposed by the 1908 Nobel laureate Elie Metchnikoffwho noted the therapeutic benefits of acid-producing lacticbacilli [2]. The beneficial effects of gut microbiota include:(i) immune-cell development and homeostasis (Th1 vs. Th2and Th17), (ii) food digestion, (iii) supporting fat metabo-lism, (iv) epithelial homeostasis, (v) enteric nerve regula-tion and (vi) promoting angiogenesis. Conversely,maladapted microbial ecology can impair many of thesehomeostatic and physiological signals so that they result ina number of disease states that include allergy, inflamma-tory bowel disease (IBD), obesity, cancer and diabetes. We

Corresponding author: Nicholson, J.K. (j.nicholson@imperial.ac.uk)

0966-842X/$ – see front matter � 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tim.2011.

describe the relationship of the microbiome with specificphysiological and pathological states and explore the meta-bolome as a window for monitoring the activity of the gutmicrobiome via microbial–mammalian cometabolism.Moreover, we identify potential avenues for exploitationof this host–microbial metabolic axis with respect to im-proving human health.

Metabolic profiling studies, mainly adopting mass spec-trometric (MS) and NMR spectroscopic platforms (Box 1) tomeasure the metabolic composition of biological samples,have been instrumental in characterizing a wide variety ofdiseases and have been used for biomarker screening,elucidating mechanistic information relating to diseaseaetiology and in monitoring responses to therapeutic inter-ventions [3,4]. Subtle changes in metabolic profiles inresponse to physiological perturbations or environmentalstimuli have also been described [5,6]. In many suchstudies, the panel of ‘diagnostic’ biomarkers has includedseveral metabolites of gut microbial or microbial–mamma-lian cometabolic origin. To probe the exact nature of themetabolic handshake between the mammalian host andthe resident gut microbiota, several animal models ofsimplified microbiota have been developed includinggerm-free animals, antibiotic-treated rodent models(resulting in a temporary knockout effect of selected bac-terial groups) and animals with transplanted microbiota,such as the Schaedler microbiota (consisting of eight bac-teria including Escherichia coli var. mutabilis, Streptococ-cus faecalis, Lactobacillus acidophilus, Lactobacillussalivarius, group N Streptococcus, Bacteroides distasonis,a Clostridium sp. and an extremely oxygen-sensitive fusi-form bacterium) [7] or human infant microbiota. The con-sequences of these microbial modifications on the hostmetabolism are summarized in Box 2 with a brief discus-sion of their advantages and limitations as a model forunderstanding the host–microbiome functional relation-ship.

Given the proximity of the gut microbiota with theintestine, it is unsurprising that the microbiome is impli-cated in intestinal diseases. However, the reach of the gutmicrobiota has been shown to extend far beyond localeffects to remote organ systems such as the brain andencompasses more processes than inflammation. Dysbiosis

05.006 Trends in Microbiology, July 2011, Vol. 19, No. 7 349

Box 1. Common spectroscopic techniques for metabolic profiling

Spectroscopic technologies commonly used for the metabolic profiling

of biological fluids and tissues can be used in either an untargeted

mode allowing for screening of the global metabolic composition of

metabolites without the necessity of preselecting a set of analytes, or

alternatively, can be tailored to measure selected classes of metabolite

such as lipids or bile acids. No single analytical method can measure

the total set of metabolites present in a biological sample owing to

analytical limitations and, therefore, the choice of analytical platform is

driven by prior knowledge of the nature of metabolic disruption,

platform availability, cost, robustness and biological matrix.

Mass spectrometry (MS): metabolites in complex mixtures can be

measured using MS-based methods, which discriminate molecules

according to their mass to charge (m/z) ratio. For the purpose of

metabolic profiling of biofluids, MS methods are generally prefaced

with a separation technology such as gas chromatography (GC-MS),

high performance or ultra performance liquid chromatography

(HPLC-MS or UPLC-MS) or capillary electrophoresis (CE-MS). These

platforms tend to exhibit high sensitivity and have been widely

applied to targeted metabolite classes (e.g. lipids, bile acids and

SCFAs) in the context of characterizing disease. Metabolite identifica-

tion is achieved by use of existing databases of chromatographic

retention times and m/z values and by ion fragmentation patterns.

NMR spectroscopy: exploits the property of spin that nuclei

possess. These nuclei can exist in discrete orientations that relate to

discrete energy states when placed in a magnetic field. Spectra are

measured following perturbation of the system with radiofrequency

pulses and are expressed in the frequency domain, which carries

information on the molecular structure of chemical constituents

based on their chemical shift with respect to a reference standard,

signal intensities and splitting patterns. 1H is typically the choice of

nucleus for high throughput metabolic screening. NMR is an

extremely robust technology and requires little or no sample

preparation. For a comprehensive review of the strengths and

limitations of these profiling methods, see [91].

Mathematical modelling and data integration: spectra obtained

from biological samples are highly multivariate and complex with

various degrees of signal overlap. Interpretation of spectra with

respect to identifying patterns of metabolites relating to a given

physiological or pathological condition is typically achieved using

data reduction and multivariate statistical analysis methods. The

general aim is to classify or predict objects by identifying inherent

patterns in a set of indirect measurements and to relate these

classifications to the metabolites or signals that strongly weigh the

classification in order to identify candidate biomarkers. These

methods operate by compressing the variation in a data set into a

smaller number of components based on latent variables and can

operate either with or without the incorporation of classification

information in the model. As with the profiling methods themselves,

each pattern recognition technique has strengths and limitations, and

the choice of method is made based on considerations such as the

priority of classification over biomarker identification, sensitivity of

method, input data and number of missing values. It is generally

appropriate to use more than one method for the purpose of validation

and maximizing the extraction of information. Common methods

include principal components analysis (PCA), partial least squares

(PLS), PLS-discriminant analysis (PLS-DA), clustering algorithms, self-

organizing maps, neural networks and genetic algorithms. Detailed

descriptions of multivariate methods can be found in [92].

Typically, for ascertaining the covariation of mammalian metabo-

lites with microbial composition and identifying potential biological

associations between specific bacterial species or families and

metabolic profiles of biofluids or tissues, relatively simple correlation

methods have been adopted. Outputs of microbial composition of

faeces including DGGE, FISH and 454 sequencing data have variously

been used to generate correlation matrices with urinary, faecal, serum

or intestinal tissue profiles [46,93] although bidimensional partial

least squares algorithms and other more complex methods can be

used to integrate two disparate -omic datasets [94].

Review Trends in Microbiology July 2011, Vol. 19, No. 7

of the gut microbiota has been implicated in several mod-ern epidemics in the Western world, the most notable beingIBD, certain cancers, heart disease and metabolic syn-drome and associated risk factors such as obesity andhypertension (Table 1). We summarize the role of themicrobiome in the aetiology and development of severaldiseases and examine concurrent changes in the metabo-lome (Table 2). Furthermore, we explore the potential forexploiting this host–microbial relationship with respect todeveloping new therapeutic interventions.

Box 2. Metabolic signatures of microbially-modulated animal mo

The metabolic phenotypes of several classic models of microbial

modulation have been characterized using NMR and MS methods:

Germ-free: several strains of rat and mice have been profiled. NMR

studies have addressed differences in urinary metabolic signatures

between conventional mice (C3H/HeJ) and their germ-free counter-

parts (e.g. 3-hydroxylcinnamic acid, 4-hydroxypropionic acid, hippu-

rate and phenylacetylglucine), liver (glutathione, glycine, hypotaurine

and trimethylamine N-oxide), intestine (alanine, aspartate, glutamate,

lactate, taurine-conjugated bile acids, tyrosine and glycine) and

kidney (betaine, choline, ethanolamine, inosine and myo-inositol)

[95]. Another study on Sprague Dawley germ-free rats has shown the

different urinary levels of 2-oxoglutarate, formate, trimethylamine N-

oxide, hippurate, 4-hydroxypropionic acid, 3-hydroxypropionic acid

and plasma levels of betaine, glucose and lactate compared with

conventional rats [96]. A targeted UPLC-MS method for profiling bile

acids has shown that the relative proportion of taurine-conjugated

bile acids in the liver, kidney, heart and plasma of germ-free rats is

markedly higher than in conventional animals [97].

Germ-free conventionalization: to understand the metabolic trajec-

tory in acclimatized germ-free rats, male Fischer 344 germ-free rats

350

IBDIBD represents two disorders of chronic intestinal inflam-mation, ulcerative colitis (UC) and Crohn’s disease (CD),both of which are associated with a complex genetic sus-ceptibility together with clear evidence for the involvementof environmental triggers. Evidence implicating the role ofmicrobiota in IBD was initially derived from observationsthat some antibiotics improved the disease course ofpatients with this disorder whereas several animal modelsof IBD required bacterial colonisation for inflammation to

dels

were examined and found to have increased urinary levels of

phenylacetylglycine after 6 h following introduction to a standard

laboratory environment. 3-Hydroxypropionic acid was observed at

day 12 and increased up to day 17. The urinary metabolic profile was

within the control range by day 21, suggesting the establishment of a

stable gut microbiota [98].

Germ-free colonized with simplified microbiota: NMR and

UPLC-MS profiling have also been applied to investigate the

global metabolic signatures of plasma, urine, faecal extracts, liver

tissues and ileal flushes from humanized microbiome mice treated

with probiotics (e.g. Lactobacillus paracasei or Lactobacillus

rhamnosus) [99] and prebiotics (e.g. galactosyl-oligosaccharide)

[100].

Antibiotic treatment: NMR urine profiles of antibiotic treated

rodents show a similarity with germ-free rat models with lower

concentrations of phenolics and other microbial metabolites includ-

ing hippurate, phenylacetylglycine, trimethylamine, dimethylamine

and higher oligosaccharides and choline. Particularly, hippurate level

shifted back to control level within 19 days post a single vancomycin

dose in female NMRI mice [101].

Table 1. Examples of disease models with modulated gut microbiota

Disease Animal model Experimental design Mechanism Refs

Obesity Mice Conventionally reared vs. germ-free Increased food consumption [69]

Obesity Mice Conventionally reared vs. germ-free Increased gut monosaccharide absorption

and induction of hepatic lipogenesis

[69]

Obesity Mice FIAF genetic knockouts Fat storage (increased lipoprotein

lipase activity)

[69]

Obesity Mice (genetic obese

leptin-deficient ob/ob)

Obese ob/ob vs. lean ob/+ and ob+/+ Genes encoding enzymes that breakdown

otherwise indigestible polysaccharides

[70]

Obesity Mice (genetic obese

leptin-deficient ob/ob)

Obese ob/ob vs. lean ob/+ and ob+/+ Increased fermentation [70]

Obesity Mice Obese ob/ob vs. lean ob/+, ob+/+ and

ob/+ mothers

Decreased Bacteroidetes, increased

Firmicutes

[50,51]

Obesity Mice Colonization of germ-free mice Increased energy extraction by bacterial

interactions

[71]

Obesity and

diabetes

Mice Non-mutant vs. CD14 mutant mice Bacterial LPS from Gram-negative bacteria

triggers inflammation in response to

a high fat diet, which results in metabolic

syndrome

[61]

Diabetes Mice NOD (non-obese diabetic)

MyD88 knockout mice

vs. non-mutant mice

A dysfunction in the microbial responsive

immune system can lead to autoimmunity

and diabetes

[41]

Diabetes Rats Biobreeding diabetes resistant

vs. biobreeding diabetes prone

Administration of Lactobacillus johnsonii

isolated from biobreeding diabetes

resistant delays the onset of type

1 diabetes in biobreeding diabetes prone

[72]

IBD Humans IBD patients Reduced diversity of Firmicutes and

Bacteroidetes

[9]

IBD Mice IL10-deficient mutants

vs. non-mutants

Gut inflammation associated with

IL10 deficiency occurs in the presence

of normal gut bacteria

[10]

Cancer Mice Tgfb1-null mice vs. non-mutants GI cancer develops in the context of normal

gut bacteria with a lack of TGFB1

[20]

Cancer Mice Germ-free IL10-deficient mice

vs. germ-free non-mutants

Colitis-associated colorectal cancer can be

reversed by colonisation with normal gut microbiota

[21]

Cancer Mice ApcMin vs. non-mutants GI polyps only occur in a germ-free environment [22]

Cancer Mice Helicobacter-free C3H/HeN

mice vs. C57BL/6 FL-N/35 mice

Enteric microbiota define hepatocellular

carcinoma risk in mice exposed to carcinogenic

chemicals or hepatitis virus transgenes

[73]

Cardiovascular

dyslipidaemia

Mice Normal chow-fed mice

vs. high fat-fed mice

vs. high fat-oligofructose-fed mice

Endotoxaemia significantly and negatively

correlates with Bifidobacterium spp.

[59]

Cardiovascular

dyslipidaemia

Hamsters Grain sorghum lipid

extract-fed hamsters

Bifidobacteria increased in grain sorghum

lipid extract-fed hamsters and positively

associated with HDL plasma cholesterol

[74]

Review Trends in Microbiology July 2011, Vol. 19, No. 7

occur [8]. When compared with controls, patients with IBDdemonstrate a reduced diversity of Firmicutes and Bacter-oidetes, and mucous-invading bacteria such as E. coli havebeen associated with disease-specific activity [9]. Geneticstudies confirm a role for the underlying microbial–hostinteraction in IBD pathogenesis. These include the geno-mic regions of nucleotide-binding oligomerisation domaincontaining 2 (NOD2), which is an intracellular receptorthat recognizes proteins found in bacterial cell wall species.Patients with CD demonstrate an association with theNOD2 gene including three polymorphisms that weakenthe host peptidoglycan response. Carriers of NOD2 have a1.75–4-fold increased risk of CD and are clinically morelikely to undergo surgical gastrointestinal (GI) resection.The mechanisms linking NOD2 require further investiga-tion; however, the microbial–host interaction is likely to bemodulated by an underlying cytokine environment, anddisease development occurs only when these animalsare colonized by normal gut bacteria [10]. In a study of

interleukin-10 (IL10)-deficient mice, which spontaneouslydevelop colitis, gas chromatography (GC)-MS analysisidentified a shift towards a higher plasma low-densitylipoprotein (LDL) to very low-density lipoprotein (VLDL)ratio and higher plasma levels of lactate, pyruvate andcitrate with lower levels of glucose consistent with in-creased fatty acid oxidation and glycolysis [11]. Chemical-ly-induced murine models of colitis have also shownsystematic differentiation in plasma and tissue profilesfrom matched control animals consisting of altered succi-nate, indole-3-acetate, glutamate and glutamine, whichtracked the developmental phases of colitis and its severity[12]. Metabolic profiling studies of IBD in humans haveshown systematic differentiation of CD and UC based onurinary and faecal metabolite profiles. Although reducedconcentrations of butyrate, acetate, methylamine and tri-methylamine and increased excretion of amino acids sug-gestive of malabsorption were characteristic of bothconditions, the extent of metabolic perturbation was greater

351

Review Trends in Microbiology July 2011, Vol. 19, No. 7

in the CD group and was accompanied by increased faecalglycerol, which was not seen in the UC group [13]. Thedifferential urinary signature of IBD from CD and UCincludes alteration of hippurate, 4-cresyl sulfate and for-mate, all potential metabolites of gut microbial activity [14].

Table 2. Examples of metabolites associated with microbial metaassociations with disease

Metabolite class Sample type Metabolites Origi

disea

SCFAs Plasma,

faeces,

tissues

Acetate, butyrate,

propionate

Micro

intest

plant

intest

Polyamines Urine,

plasma,

faeces,

tissues

Putrescine,

cadaverine

Prote

micro

and c

geno

previ

gono

defen

mark

diabe

Methylamines

and products of

choline degradation

Urine,

plasma,

faeces,

tissues

Methylamine,

dimethylamine,

dimethylglycine,

trimethylamine,

trimethylamine

N-oxide

Dieta

the g

dieta

cardi

Benzoates Urine,

plasma

Benzoic acid (plasma),

hippurate (urine),

2-hydroxyhippurate

Decre

consi

anim

show

a clin

and d

mode

Chlorogenic acids Urine,

plasma

Dihydroferulic acid,

dihydroferulic acid-3-O-

sulfate, ferulic acid-4-O-

sulfate, dihydroferulic

acid glucuronide,

feruloylglycine

Chlor

bene

lower

hype

Protein putrefaction products

Tyrosine Urine,

plasma,

faeces

4-cresyl sulfate

4-cresyl

glucuronide

Decre

indol

depre

have

diabe

urina

hydro

Tryptophan Urine,

plasma,

faeces

Indoleactylacetate,

indoleactylglycine,

indolelactate,

3-hydroxyindole,

indoxyl sulfate

Phenylalanine Urine,

plasma,

faeces

Phenylacetlyglycine,

phenylacetylglutamine

Bile acids Urine,

plasma,

faeces

Cholic acid, hyocholic

acid, deoxycholic acid,

chenodeoxycholic acid,

hyodeoxycholic acid,

ursodeoxycholic acid,

glycocholic acid,

glycodeoxylcholic acid,

glycochenodeoxycholic

acid, taurocholic acid,

taurohyocholic acid,

taurodeoxylcholic acid,

taurochenoxycholic acid

Conju

synth

conju

know

endo

gluco

been

broad

(hydr

dehy

sulfat

tertia

highe

352

It has recently been shown via genome-wide associationstudies (GWAS) that there are overlapped genetic signa-tures of CD and UC, and furthermore, that IBD is geneti-cally linked to mutations associated with a number ofnon-GI diseases [15]. Given that these GI conditions are

bolism or microbial–host cometabolism and illustrations of

n of metabolites and examples of association with

se

Refs

bes ferment substances presented in the large

ine, including indigestible oligosaccharides, dietary

polysaccharides or fibre, non-digested proteins and

inal mucin, and produce SCFAs.

[75]

in putrefaction by a range of anaerobic

organisms can result in amines such as putrescine

adaverine, can exert adverse impact including

toxicity on the host. These polyamines have been

ously found to increase resistance of Neisseria

rrhoeae to mediators of the innate human host

ce. Putrescine has also been proposed as a urinary

er for diabetes in a diet-induced mouse model of

tes.

[76,77]

ry choline can be metabolized into methylamines by

ut microbes and has been shown to be modulated in

ry-induced models of obesity, diabetes and

ovascular disease.

[77,78]

ased concentrations of urinary hippurate have been

stently reported as characteristic of obesity in both

al models and humans. 2-Hydroxyhippurate was

n to be positively correlated with colorectal cancer in

ical study whereas high urinary levels of hippurate

imethylamine were characteristic of diabetes in a rat

l.

[79–81]

ogenic acids are known antioxidants and have been

ficially linked to health, and have been shown to

blood pressure in a human cohort with essential

rtension.

[82]

ased plasma concentrations of indoxylsulfate and

eacetate were key discriminators in a rat model of

ssion. Indoxyl sulfate and phenylacetylglutamine

been found in higher concentrations in the plasma of

tic individuals compared to non-diabetics. Abnormal

ry excretion of phenylacetylglutamine, hippurate and

xyhippurates has been reported in autistic children.

[63,83–85]

gated bile acids are cholesterol derivatives

esized in the liver and contain a steroid ring

gated with glycine or taurine. Bile acids are well

n to facilitate lipid absorption and signal systemic

crine functions to regulate triglyceride, cholesterol,

se and energy homeostasis. Gut microbiota have

shown to modify the bile acid profiles through a

range of reactions such as deconjugation

olysis of bile salt conjugates to form free bile acids),

droxylation, oxidation (dehydrogenation) and

ion, resulting in the formation of secondary and

ry bile acids. Deoxycholate has been reported to be

r in the plasma of diabetics.

[84,86]

Table 2 (Continued )

Metabolite class Sample type Metabolites Origin of metabolites and examples of association with

disease

Refs

Lipids Plasma,

faeces

Acylglycerols,

sphingomyelin,

cholesterol,

phosphatidylcholines,

phosphoethanolamines,

triglycerides

Gut microbiota regulate lipid synthesis and metabolism

directly through various microbial activities. Undigested

and non-absorbed glycerides in colon may be hydrolysed

into free fatty acids and glycerol by bacterial lipases.

Glycerol can be subsequently converted into

3-hydroxypropanal (3-HPA) and then 1,3-propanediol

(1,3-PDO) by a NAD+-dependent oxidoreductase by a wide

range of colonic bacteria including the genera Klebsiella,

Enterobacter, Citrobacter, Clostridum and Lactobacillus

members. Lactobacilli, particularly Lactobacillus reuteri,

are most efficient in accumulating 3-HPA.

[87–89]

Organic acids Urine,

plasma,

faeces

Lactate, formate High plasma levels of lactate and formate have been found

to be characteristic of several parasitic infections and urinary

formate has also been shown to inversely correlate with

high blood pressure.

[4,90]

Review Trends in Microbiology July 2011, Vol. 19, No. 7

also associated with gut microbial abnormalities, this begsthe question as to whether a significant transgenomic net-work process is in operation linking both microbial andhuman genes to disease aetiopathogenesis. These studiescollectively support a key role for the microbiota in IBDswith contribution to the immunological component of thedisease as well as exerting direct metabolic effects. Howev-er, the mechanisms by which these microbial alterationscontribute to IBD pathogenesis remain unknown. Thus,specific studies probing key functionally-active microbiotaare of importance.

CancerThe association of the gut microbiota with cancer is mostcommonly observed with GI tumours, as expected, al-though there are examples of these microbiota modifyingthe cancer risk to other systems such as in breast tumours[16]. The bacterium Helicobacter pylori has been proposedto have an aetiological relationship with gastric cancer [17]and has been applied to track the early migration of Homosapiens suggesting that humans were already infected byH. pylori before their migrations from Africa [18] and havedemonstrated a direct gut microbe–host relationship eversince [19]. Colonic cancer is the third most common cancerin industrialized countries and is the second leading causeof cancer deaths. Several knockout mice models haveelucidated a growing understanding of colonic cancersand gut microbiota. Transforming growth factor beta 1(Tgfb1)-null mice develop colonic cancer in the presenceof conventional gut microbiota [20], whereas the germ-freeIl10-deficient mice are resistant to colitis-associated colo-rectal cancer and resistance is lost upon colonization withnormal gut microbiota [21]. Conversely, tumour-proneApcMin mice demonstrate only a modest reduction in GIpolyps when raised in a germ-free environment [22]. Theseeffects of the gut microbiota on cancer development reflecta complex interplay between the gut genome and thephysiological environment of the host and the microbiota.Other examples include colonic cancer models with disor-dered transforming growth factor beta (TGFB) signalling,such as Tgfb1-null, Smad3-null and Rag2-null mice, andpredispose the development of cancer with pro-inflamma-tory gut bacterial species such as H. pylori and Helicobacterhepaticus [23,24].

Metabolic profiling studies in colon and other cancershave also drawn attention to the role of the gut microbiota inaetiogenesis showing profound modulation of lipid and ste-rol pathways, and changes in faecal amino acids, short-chainfatty acids (SCFAs) and amines [25–27]. The underlyingmechanisms of shifts in microbial ecology contributing tocolonic tumourigenesis include dietary changes and subse-quent shifts in metabolic expression. Specifically, there is anepidemiological association between colorectal cancer andraised sulfide production by sulfur-reducing bacteria (SRB)such as Desulfovibrio vulgaris that is typically seen in meat-rich Western diets [28]. Here, SRBs compete with methano-genic bacteria for hydrogen to produce hydrogen sulfide.This is supported by evidence that demonstrates that sulfideacts as an oxidation inhibitor of the SCFA n-butyrate in thecolonic epithelium. Butyrate is a potent histone deacetylaseinhibitor that has epigenic activity in colonocytes and micro-RNA-dependent p21 gene expression activity leading tocolonic cancer prevention [29] while also offering importantepithelial regulatory activities such as ion absorption, mem-brane lipid control, cellular detoxification and mucus for-mation. An association between 4-cresol and colon cancer[30] has been reported. The production of 4-cresol is depen-dent upon intestinal environmental factors such as thecomposition of the microbiota, food intake and pH of theintestinal tract [31]. 4-Cresol is synthesized from tyrosineand phenylalanine via 4-hydroxylphenylacetate by gutmicrobiota. Clostridium difficile and certain Lactobacillusstrains are known to produce p-cresol by decarboxylation of4-hydroxyphenylacetate [32,33]. Subsequently, 4-cresol isexcreted in the form of 4-cresyl glucuronide and 4-cresylsulfate in urine [34] through glucuronidation and sulfation.

Marked changes in the urinary composition of gut micro-bial metabolites have been found to be characteristic ofseveral other cancers. For example, quinolinate, 4-hydro-xybenzoate and gentisate concentrations were higher inkidney cancer patients whereas 3-hydroxyphenylacetateand 4-hydroxybenzoate were anticorrelated with renal can-cer [35]. Urinary hippurate, 4-hydroxyphenylacetate andformate, among other metabolites, have been reported todifferentiate ovarian cancer from breast cancer patients andhealthy controls [36], and urinary hippurate was found to beone of the strongest discriminators of lung cancer [37] andhas also been found in relatively high concentrations in

353

Diabetes, Metabolic Syndrome,Cardiovascular Disease & Cancer

Gutmicrobiome

Metabolicsurgery(BRAVEEffects)

Oxidativestress

Lipidperoxidation

Inflammatorycytokines

Insulin resistance

Free fatty acids

Sex steroids

Sex steroidreceptor

Sex hormonebinding globulin

Bile acidmetabolites

Insulin

Insulin & IGF-1receptor

Inflammation

AdipokinesSteatosis Metabolic syndromerisk factors

OBESITY

TRENDS in Microbiology

Figure 1. Schematic of the gut microbial contribution to obesity and related diseases.

Review Trends in Microbiology July 2011, Vol. 19, No. 7

tumour tissue itself [38]. A GC-MS analysis of urine frompatients with osteosarcoma also identified that gut micro-bial metabolites, in particular putrescine, hippurate and 4-hydroxyphenylpropionate, comprised part of the metabolicsignature of the disease [37]. Further evidence of the contri-bution of the microbiota to cancer is the finding that one ofthe most discriminatory metabolites in a study of 50 breastcancer patients and matched controls was 4-hydroxypheny-lacetate [39], a microbial product of tyrosine degradation.

Adding to the challenge of identifying biomarkers ofspecific cancers and elucidating potential aetiological rolesof the gut microbiota is the impact of diet on both themicrobiome composition and the disease. For example, theWestern diet alters the colonic proportions of bile acids toincrease the proportion of primary bile acids transformedto secondary bile acids by intestinal bacteria. Deoxycholicacid (one of the secondary bile acids) is associated withseveral models of carcinogenesis and correlated with theenzymatic activity of 7a-dehydroxylating bacteria [40].Several species of Clostridium demonstrate high and low7a-dehydroxylase activity, which could represent a noveltarget for modifying GI cancer risk. The central role of thegut microbiota in cancer and obesity is illustrated inFigure 1. Therefore, in addition to the direct relationshipof particular microorganisms with specific cancers, such asH. pylori with gastric cancer, the gut microbiota alsocontribute indirectly via cholesterol and lipid metabolismwith growing evidence that obesity is associated withcancer risk and poorer prognosis.

DiabetesThe global epidemic of obesity is associated with a dramat-ic increase in the prevalence of type 2 diabetes mellitus(T2DM). T2DM is characterized by insulin resistance

354

whereas type 1 diabetes mellitus is characterized by a lossof insulin-producing beta cells in the pancreatic islets ofLangerhans, which results in insulin deficiency. The notionthat gut microbiota are key players in the onset anddevelopment of diabetes is becoming more widely acceptedas the evidence base grows. Experimental evidence hasshown that the non-obese diabetic (NOD) mouse lackingthe innate microbial-recognition immune system receptorMyD88 is resistant to type 1 diabetes [41]. If MyD88knockout mice were depleted of normal intestinal flora,type 1 diabetes would ensue, whereas if NOD mice werecolonized with the altered Schaedler flora (ASF), the dia-betes would be attenuated [41]. This alluded to the role ofgut microbiota and the innate immune receptor MyD88 inpriming the immune system in the context of type 1diabetes autoimmunity. As type 2 diabetes is stronglyassociated with obesity, these two conditions could belinked by an underlying physiological process includingthe disordered regulation of gut bacterial profiles.

From the metabolic profiling literature, several studieshave reported modulated choline degradation productsand bile acids as characteristic of insulin resistance anddiabetes in animal models. Studies involving high fat diet-induced initiation of insulin resistance in animal modelsfound alterations in the plasma lipids and in the concen-trations of urinary metabolites such as phenylacetate,hydroxyphenylacetylglycine and hydroxyindoleacetic acid,although host species and strain influenced the exactmetabolic composition of the biofluids in these animalmodels [42–45]. Low urinary concentrations of hippurateand higher concentrations of dimethylamine have beenfound in Zucker obese and Goto-kakizaki rats in compari-son with the respective control strains [43,46]. A strepto-zotocin-induced rat model of diabetes reported acetate,

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ethanol and lactate among the key discriminatory meta-bolites in addition to a modulated plasma lipid profile [44].NMR spectroscopy was applied to characterize the plasmaprofiles of congenic strains of rats derived from crossing adiabetic and control animal. Quantitative trait loci wereused to generate a correlation matrix with the plasmametabolite profiles and linkage to benzoate was found tobe a consequence of deletion of a uridine diphosphateglucuronosyltransferase [47]. The ability of the microbiotato influence the expression of diabetes in animal modelsand the clear impact upon the choline degradation path-way in diabetic animals and man emphasizes the potentialof the microbes to contribute to disease aetiology andexpression.

ObesityObesity has traditionally been considered to be a disorderof energetic and nutritional surplus, which in some cases isassociated with a genetic predisposition. Recently, howev-er, the evidence for the role of the gut microbiome hasoffered new insight into aspects of our understanding ofobesity pathogenesis. These include the association of gutmicrobiota with the following: intestinal permeability,systemic quantity of adipose tissue and body weight.

The initial association between gut bacterial species andweight gain was derived from studies where decreasingdietary fibre intake resulted in excess body weight anddiabetes, and was hypothesized to result from a change ingut microbiota as a consequence of altered nutrient supplyand digestion. Subsequently, it has been demonstratedthat consumption of a high fat diet results in a decreaseof total gut bacterial levels and an increase in Gram-negative bacteria. Four bacterial mechanisms have beenidentified to result in excess bodily energy gain: (i) micro-biota increase energy bioavailability by transforming in-creased proportions of non-digestible food intobiochemically absorbable nutrients; (ii) the influence ofintrinsic bacterial metabolism to generate and raise sys-temic levels of SCFAs to activate triglyceride synthesis;(iii) high fat diets can result in a responsive bacterialmetabolism resulting in pathology (such as microbial con-version of choline to methylamines leading to a cholinedeficient state, which induces liver disease); and (iv) theability of the microbiome in regulating gut gene expressionto favour an obese state. This could occur through thereduction of lipoprotein lipase activity through the inhibi-tion of angiopoietin-like 4 (Angptl4) and fasting-inducedadipocyte factor (FIAF) to increase free fatty acids andadipose levels [48,49].

Experimental evidence has revealed that germ-free miceare less obese than normal controls but gain weight andhave decreased Angptl4 expression following colonizationby conventional gut bacteria [1]. The ob/ob leptin-deficientobese mouse has a microbiome with a 50% reduction inBacteriodetes and a concurrent increase in Firmicutes,which might be associated with increased food consumptionin these animals. Obese humans also demonstrate an alter-ation of the Firmicutes to Bacteroidetes ratio that can bealtered by weight loss [50,51]. Furthermore, bacterial mod-ulation of obesity is also suggested by the transmission ofobesogenic bacterial profiles in ex-germ-free mice [52]. The

fact that consistent differences have been noted in themetabolic profiles of animals and humans following caloricrestriction also concurs with the notion that body weight andweight change is associated with alteration in microbialcomposition. Among these microbial signature changes in-clude increased urinary excretion of hippurate, 4-hydroxy-phenylacetic acid, phenylacetylglycine and decreasedacetate and lactate [53,54].

Bariatric surgery offers the most consistent method ofweight loss in morbidly obese patients, and also achievesthe resolution of metabolic dysfunction (including the res-olution of T2DM). Bariatric surgery procedures modify thegut microbiome to achieve a low inflammatory and weightloss profile that might reverse the effects of the metabolicsyndrome [55]. Work on a bariatric animal model hasrevealed that these operations work through the BRAVEeffects: (i) bile flow alteration, (ii) reduction of gastric size,(iii) anatomical gut rearrangement and altered flow ofnutrients, (iv) vagal manipulation and (v) enteric guthormone modulation [56]. Furthermore, substantial shiftsof the main gut phyla following metabolic surgery (in arodent animal model) towards higher levels of Proteobac-teria and lower levels of both Firmicutes and Bacteroideteshave been demonstrated as compared to controls [57]. Themicrobial effects of successful weight loss and anti-diabeticoperations could lead to novel mechanisms and therapiesfor obesity and T2DM. In a recent study by Mutch et al.[58], where weight loss was achieved in obese subjectsfollowing Roux-en-Y gastric bypass surgery, markedalterations in the plasma profile were found with manyclasses of metabolites changing post surgery. In addition tolipids, indoles, fatty acids and amino acids, 4-cresyl sulfatewas characteristic of the obese profiles [58] and mightindicate an altered clostridial profile. To date, investigat-ing microbial functions in obesity have been approachedvia both weight gain (obese patients and murine models)and weight loss (bariatric surgery in human and murinemodels) with good consensus that body weight influencesor is influenced by the host microbiota. The exact nature ofthis host–microbial interplay requires further mechanisticinvestigation.

Cardiovascular dyslipidaemia and metabolicendotoxaemiaDyslipidaemia is a disorder of lipoprotein metabolism,which results in a systemic increase of disease-inducingblood lipids such as cholesterol, triglycerides and LDLparticles and a decrease in the levels of protective lipidssuch as high-density lipoprotein (HDL) particles. A sus-tained high fat diet can induce dyslipidaemia that resultsin a proinflammatory response including an increase ofGram-negative bacterial outer membrane lipopolysaccha-ride (LPS) levels to result in a ‘metabolic endotoxaemia’demonstrable in both rodents [59] and humans [60]. Aninfusion of LPS into rodents caused clinical features ofmetabolic syndrome such as weight gain, insulin resis-tance and hepatic lipid overload. Fat has been demonstrat-ed to be a highly efficient transporter of LPS from the gutlumen to the bloodstream and its effects can be delayed inmice lacking the LPS-CD14 receptor [61]. Non-fatty dietsare associated with decreased levels of LPS and lipoprotein

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particles can buffer its proinflammatory effects. High fatdiets demonstrate a shift in gut bacterial ecology by in-creasing the Gram-negative:Gram-positive ratio whereasan increased fibre intake has been demonstrated to reversethese changes [59] and heralds a possible avenue to pro-mote dietary therapy for the prevention of metabolic endo-toxaemia and atherogenic dislipidaemia.

NeuropathologyThe composition of the intestinal microbiota is extremelyrelevant in neurogastroenterology, which deals with theinteractions of the central nervous system and the gut(gut–brain axis). Several neuropathological diseases arethought to be associated with the gut microbiota. Autism isa disorder of neural development with impaired socialbehaviour and often involves GI symptoms. Previous stud-ies examined the faecal microbial profiles of autistic chil-dren, which indicated 10-fold higher numbers ofClostridium spp. compared with healthy subjects [62].Many species of Clostridium are known to produce neuro-toxins, which could contribute to the autism spectrum.Metabolic alterations in gut host–microbial cometabolismincluding higher urinary levels of hippurate and phenyla-cetylglutamine and tryptophan/nicotinic acid metabolismhave been observed in autistic children [63]. The bile acidshave been shown to differ across various neurologicalconditions. For example, glycocholate (GCA), glycodeoxy-cholate (GDCA) and glycochenodeoxycholate have beenshown to be altered in plasma profiles in Alzheimer’sdisease (AD) [64], whereas tauroursodeoxycholic has beenshown to modulate p53-mediated apoptosis in AD and to beneuroprotective in Huntington’s disease [64]. Clearly, gutmicrobiota not only exert a local effect on the GI tract butalso impact remote organs such as the brain throughchemical signalling. Further investigation of the gut–brainaxis may provide valuable mechanistic insight into a rangeof neuropathological and developmental diseases.

Concluding remarksAdvances in technology in both metabolic profiling andmicrobial phenotyping methods have improved our abilityto derive correlations between the microbial and metabolicphenotypes, and mathematical modelling tools have beendeveloped to accommodate high density data such as thosegenerated by metabonomic and metagenomic methods.New methods of integrating these -omics datasets havebeen developed to extract correlations between specificmicrobes and metabolites. Several methods ranging fromsimple correlations to bidirectional partial least squaresapproaches have been explored (Box 1), but more work isneeded both on the preprocessing and data modellingcomponents of this integration process. One limitation ofthe technology at present is that most of the microbialphenotyping tools, such as fluorescence in situ hybridiza-tion (FISH), denaturing gradient gel electrophoresis(DGGE) and 454 sequencing, map the content of themicrobes present without giving any indication of activity.A much needed breakthrough is to further profile themicrobiotal metabolism by establishing which microbialproducts are formed directly from specific substrates(originating from the human diet or faecal mucins) using

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stable-isotope labelling (U-13C glucose) approaches [65]. Inthe disease areas summarized in this review and beyond,there is scope for further elucidation of the role of the gutmicrobiota and development of potential therapeutic tar-gets based on the underpinning metabolic linkages or evenon the microbes themselves.

Although the fact that there is a strong relationshipbetween the mammalian host and its enteric microbiota,which impacts upon health, cannot be disputed, much ofthe literature is confusing and consensus on the exactmechanisms by which microbes modulate disease process-es has not yet been reached. For example the ratio ofFirmicutes:Bacteroidetes has been reported to differ inobesity in several studies, whereas in other studies, nomodulation of this ratio has been found with weight gain orloss. This may be in part due to the fact that the Gram-positive and Gram-negative bacteria in the gut are inde-pendent and thus ratios of particular bacterial groups orfamilies may be uninformative. Sequencing analysis at amore refined level may provide a better understanding ofthe structure and function of the microbiota. A recentinternational study has identified three robust clustersof microbiota (known as enterotypes) that are not nationor continent specific. Metagenomic reads from populationsat different geographical locations were mapped usingDNA sequence homology to 1511 reference genomes toreveal that intestinal microbiota variation is generallystratified, not continuous. This confirmed the existenceof a select series of host–microbial symbiotic relationships,where certain genes are significantly correlated with ageand physiological characteristics such as body mass index.These enterotypes can be applied to identify novel bio-markers or targets of disease [66].

To date, most of the research into the effect of microbialmetabolism on the host has been focused on diseases thatpredominate in the Western world. One potential avenuefor exploiting the host–micobiome metabolic crosstalk is touse the knowledge of the mechanisms involved in weightgain to promote ‘thrifty’ bacteria in populations in devel-oping countries where malnutrition is one of the biggestclinical problems. Studies in humans and in animal modelsof parasitic infection have indicated that a three-wayrelationship exists between the host and its enteric micro-biota and parasites, with each parasite causing specificchanges in the gut microbial contribution to the urine,plasma and faecal metabolomes. Therefore, there is clearpotential for adapting the analytical strategies and knowl-edge gained from other disease areas and applying them topromote health in developing countries.

Exploration of the gut–brain axis and the role of themicrobiota in modifying behaviour is also an exciting butunderdeveloped area of research. The possibility of under-standing the metabolic basis of the link between the gutand brain and, moreover, the ability to influence thisconnection is tantalizing. For example, germ-free micehave been shown to have lower neurotrophic factor expres-sion levels and to mount a more severe elevation in corti-costerone levels than specific pathogen free (SPF) mice.Moreover, it was shown that this response could be re-versed by colonization with Bifidobacterium infantis orreconstitution with SPF faeces but potentiated by E. coli

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[67]. The potential for uncovering new bacterial targets ordietary strategies for treating neurological aspects of suchdiseases is enormous. Some specific probiotics such asLactobacillus farciminis have been demonstrated to havean impact on spinal neuronal activation [68].

As the drive towards research consortia strengthens,multidisciplinary teams with the capacity for combiningmicrobial phenotyping, metabolic profiling and clinicalexpertise become a reality and should serve to developthe current understanding of the metabolic language ofmammalian–microbial communication. The benefits ofattaining this knowledge are clear. The gut microbiomefunctions as a virtual organ and significantly extends themetabolic capacity of the host. This transgenomic metabo-lism offers a new paradigm for developing novel therapiesfor many diseases and has potential to beneficially impactupon a range of acute and chronic pathologies.

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