Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-23T18:16:32.332Z Has data issue: false hasContentIssue false

The gut microbial metabolome: modulation of cancer risk in obese individuals

Published online by Cambridge University Press:  23 November 2012

Wendy R. Russell*
Affiliation:
Lifelong Health Division, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen AB21 9SB, UK
Sylvia H. Duncan
Affiliation:
Gut Health Division, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen AB21 9SB, UK
Harry J. Flint
Affiliation:
Gut Health Division, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen AB21 9SB, UK
*
*Corresponding author: Dr Wendy R. Russell, fax +44 1224 716629, email w.russell@abdn.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Obesity is a critical health concern and although genetic factors may predispose an individual to become obese, changes in diet and lifestyle over the last few decades are likely to be significant contributors. Even so, it has been suggested that the causes of the current obesity crisis are not simply explained by changes in eating and exercise habits. Evidence suggests that the gut microbiota may play an important role in obesity and may be a factor in the development of associated disease including diabetes, CVD, non-alcoholic fatty liver disease and cancer. There have been tremendous advances in knowledge regarding the composition of human gut microbiota, but less is known about their function and role within the human host. It is becoming widely accepted that the products of microbial metabolism influence human health and disease, particularly with respect to immune response and inflammation. However, in most cases, the products of microbial metabolism are uncharacterised and their mechanism of action remains unknown. This review addresses the role of the metabolites produced by gut microbiota in cancer and obesity. It is clear that only if the link between microbial diversity and metabolic functionality is firmly established, will the mechanism by which gut microbiota maintains health or contributes to disease development be elucidated.

Type
70th Anniversary Conference on ‘From plough through practice to policy’
Copyright
Copyright © The Authors 2012

Abbreviations:
CRC

colorectal cancer

GI

gastrointestinal

RS

resistant starch

TLR

Toll-like receptor

The colonic microbiota is likely to play an important role in health maintenance or progression to diseases such as colorectal cancer (CRC). Furthermore, the gut microbial community has been proposed by a number of studies to play a role in the development of obesity. Diet is a significant driver both for the composition of the gut microbiota( Reference Walker, Ince and Duncan 1 Reference Flint, Duncan, Scott and Louis 3 ) and for the development of obesity and cancer. This makes it challenging to untangle causative factors that may be due to dietary components, individual gut microbes and the metabolic products of microbial metabolism that are largely derived from dietary components. The diversity of bacterial species in the colon means that collectively they perform an impressive array of metabolic activities, as is evident from both metabolomic and metagenomic analyses( Reference Qin, Li and Raes 4 Reference Holmes, Li and Athanasiou 6 ). There is an increasing awareness that in addition to understanding the composition of the microbiota, their metabolic output will have a profound impact in modulating both gut and systemic health( Reference Duncan, Belenguer and Holtrop 2 , Reference Fleissner, Huebel and Abd El-Bary 7 , Reference Russell, Gratz and Duncan 8 ). Functional analysis of microbial metabolites will therefore be crucial to understanding the impact of diet and of gut micro-organisms on maintenance of health and prevention of disease. This review will focus on the importance of the microbial metabolome in the development of obesity-linked co-morbidities.

The gut microbiota

From birth, the gastrointestinal (GI) tract becomes colonised by a succession of bacterial species and the composition of the microbiota is similar to that of adults from around the time of weaning. In the days following birth, the baby GI tract is primarily colonised by facultative anaerobes and microaerophiles, followed later by obligate anaerobes. In breast-fed infants, for example, Bifidobacterium species generally predominate while appreciable numbers of Bacteroides species are only detected after weaning. In early life Bacteroides species may persist in the gut largely through their ability to utilise host-derived substrates, followed after weaning by the utilisation of plant-derived polysaccharides from the diet( Reference Bjursell, Martens and Gordon 9 ).

The adult human large intestine usually contains more than 200 g of contents and is colonised by hundreds of bacterial species, reaching a total cell density of about 1011 bacterial cells/ml thereby outnumbering host cells about 10-fold. This complex microbial community also harbours about 100-fold more genes than the human genome( Reference O'Hara and Shanahan 10 ). This dense community of bacteria mainly comprise the Gram-negative Bacteroidetes and the Gram-positive Firmicutes and Actinobacteria( Reference Duncan, Belenguer and Holtrop 2 ) with the Bacteroidetes accounting for about one-quarter of the microbiota. Most of the remainder belong to the Firmicutes, which mainly comprise the Lachnospiraceae and Ruminococcaceae families. The next most abundant phylum of Gram-positive bacteria is Actinobacteria that includes the Bifidobacterium species( Reference Flint, Duncan, Scott and Louis 3 ). Inter-individual variation is observed for the gut microbiota and may be greater in infants than in adults( Reference Kurokawa, Itoh and Kuwahara 11 ). Nevertheless, a dominant group of bacterial species has been identified in faecal samples from healthy adult individuals( Reference Walker, Ince and Duncan 1 , Reference Qin, Li and Raes 4 , Reference Tap, Mondot and Levenez 12 ). The top ten most abundant phylotypes or species make up about 30% of the faecal microbiota( Reference Walker, Ince and Duncan 1 , Reference Flint, Scott and Duncan 13 ). Two of the most dominant species are Faecalibacterium prausnitzii and Eubacterium rectale, which employ the butyryl CoA: acetate CoA transferase route for butyrate formation( Reference Duncan, Hold and Barcenilla 14 Reference Louis, Duncan and McCrae 16 ). These species appear to be present in a majority of individuals although F. prausnitzii abundance is diminished in Crohn's sufferers( Reference Willing, Dicksved and Halfvarson 17 , Reference Sokol, Pigneur and Watterlot 18 ) and in the elderly( Reference van Tongeren, Slaets and Harmsen 19 ). Two other dominant species, Eubacterium hallii and new Anaerostipes species are butyrate producers that can utilise lactate( Reference Duncan, Louis and Flint 15 ). Microbiota variation may be a consequence of several factors including acquisition of bacteria at birth( Reference Ley, Backhed and Turnbaugh 20 ), host immune responses, antibiotic usage( Reference Relman 21 ) and diet( Reference Walker, Ince and Duncan 1 ,Reference Duncan, Belenguer and Holtrop 2 , Reference Abell, Cooke and Bennett 22 Reference Davis, Martinez and Walter 24 ).

Methodologies used to study the microbial genome: metagenomics

Large genome programmes including the National Institutes of Health-funded Human Microbiome Project (nihroadmap.nih.gov/hmp/) and the EU funded MetaHIT project (www.metahit.eu/) have sequenced more than fifty Bacteroides and Prevotella species and many Gram-positive bacterial species. The increasing speed and decreasing cost of sequencing is now making metagenomic analysis of the whole microbial community a popular option( Reference Qin, Li and Raes 4 , Reference Kurokawa, Itoh and Kuwahara 11 ). A recent metagenomic analysis of faecal samples of volunteers from four countries suggested that the microbiota belonged to three distinct clusters or ‘enterotypes’( Reference Arumugam, Raes and Pelletier 25 ). On the other hand, another study detected only two putative enterotypes among the ninety-eight adults examined, and presented evidence that these were in fact diet-driven( Reference Wu, Chen and Hoffmann 26 ). While metagenomic analyses can provide an incredible amount of information, interpretation of the data is very much dependent on information gained from previously isolated and characterised bacteria. Functional screening of metagenomic libraries can be achieved using high-throughput screening assays, as have been employed in other ecosystems such as soil( Reference Riesenfeld, Schloss and Handelsman 27 ).

Methodologies used to study the microbial metabolome: metabolomics

Metabolomics is a comprehensive and non-selective analytical chemistry approach aimed at providing a global description of all the metabolites present in a given biological sample. Although metabolic profiling has been used for decades, modern instrumentation and statistical methodology has found recent application in predicting the outcome of dietary and clinical studies. In particular, metabolomics can prove valuable where the lack of effective biomarkers has made it difficult to establish the long-term implications of intervention, e.g. in outwardly healthy individuals. Human genotyping can give useful information regarding the predisposition to disease and proteomics can provide indicators of disease occurrence. However, metabolites have always been an excellent indicator of human disease, and for this reason they are also likely to be a useful predictor of human health. In addition, metabolomic data provide vital information on the overall function of the gut microbiota. Metabolomic analysis can rely on NMR spectroscopy which can be coupled to liquid chromatography, allowing separation of the metabolites prior to detection. The main advantages of MS over NMR are sensitivity and the ability to perform quantitative and targeted analysis. Recent advances in MS have resulted in robust and powerful methods to study the human metabolome( Reference Villas-Bôas, Mas and Åkesson 28 , Reference Hollywood, Brison and Goodacre 29 ) and the real potential of MS has been achieved through prefacing to GC–MS and liquid chromatography–MS. The plethora of ionisation techniques (e.g. electron ionisation and chemical ionisation) and wide availability of mass analysers make modern MS analysis an extremely versatile technique( Reference Villas-Bôas, Mas and Åkesson 28 , Reference Hollywood, Brison and Goodacre 29 ).

Influence of host and dietary factors on the gut metabolome

The composition of the gut microbiota may be is subject to selective pressure from the host and diet, which can alter the ecosystem homoeostasis affecting the abundance of specific groups. Many factors have the potential to influence the microbiome and these could include sex, age, BMI, gut physiology and immune/inflammatory status (Fig. 1). The colonic microbiota is found to be relatively stable in individuals consuming their normal diet but it is evident that dietary change can influence the abundance of specific bacterial groups( Reference Walker, Ince and Duncan 1 ). There are major compositional differences in the gut microbiota between population groups depending upon staple food intake, as suggested by a study on Italian and African children( Reference De Filippo, Cavalieri and Di Paolo 30 ). The amount and type of the three main macronutrients, carbohydrate, protein and fat, consumed in our diets will impact on the composition of the gut microbiota. The ability of gut microbes to utilise and transform these macro- and micronutrients, however, has consequences for human health that may be more important than the detailed taxonomic changes in the microbiota.

Fig. 1. Host factors with potential to impact on the gut microbiota.

Macronutrient influences on the gut microbiota

Carbohydrate is critical for the host and is the major energy source for the gut microbiota. The principal carbohydrate sources for individuals consuming a western-style diet are resistant starch (RS) with usually lower amounts of NSP, which are consumed mainly in cereals. In addition, oligosaccharides such as fructans are consumed in foods including onions and artichokes. The extent of dietary starch breakdown in the colon is dependent on a number of factors that include amylose content and state of gelatinisation which is mainly determined by the cooking process( Reference Englyst, Kingman and Cummings 31 ). Starch is a complex polysaccharide consisting of a mixture of amylose (1,4-α-linked glucose residues) and amylopectin, a branched polymer composed of amylose chains linked to an amylose backbone by 1,6-α-linkages. The relative proportion of amylose and amylopectin has an effect on the availability of different types of starch for bacterial growth. In a model system, 80% of sequences recovered from starch particles belonged to Ruminococcus bromii, E. rectale and Bifidobacterium species( Reference Leitch, Walker and Duncan 32 ).

Moreover, bacteria related to R. bromii increased almost two-fold in faecal samples when the volunteers switched from normal diets to starch enriched diets( Reference Abell, Cooke and Bennett 22 ). Faecal populations of the Roseburia/E. rectale group( Reference Aminov, Walker and Duncan 33 ) have been shown to respond to controlled variations in dietary carbohydrate intake( Reference Duncan, Belenguer and Holtrop 2 , Reference Russell, Gratz and Duncan 8 ) that may be explained by a dependence on RS for growth. Another recent study compared the effects of diets supplemented either with a type three RS or a source of NSP (wheat bran) in obese subjects. The groups showing the most significant responses to RS on average were Firmicutes bacteria related to R. bromii and E. rectale, with the former group increasing 10-fold on average as a percentage of total bacteria. Two of the fourteen individuals showed no detectable ruminococci in faecal samples, and carbohydrate analysis of these samples revealed <40% fermentation of RS compared with >95% in the other subjects( Reference Walker, Ince and Duncan 1 ).

High fibre diets increase faecal bulking, SCFA production and transit rates along the large intestine( Reference Lewis and Heaton 34 , Reference Lampe, Slavin and Melcher 35 ). Faecal butyrate concentrations have been shown to correlate with the abundance of the Roseburia/E. rectale group( Reference Duncan, Belenguer and Holtrop 2 ) which is likely to be a major contributor to butyrate formation from starch. This group is stimulated at mildly acidic pH in vitro largely because of the inhibition of competing Bacteroides species( Reference Duncan, Louis and Thomson 36 ) suggesting that its preferred niche may be in the proximal colon where rapid fermentation creates mildly acidic conditions. Active fermentation of other fibre sources and prebiotics also tends to decrease pH in the proximal large intestine( Reference Bown, Gibson and Sladen 37 ) and these shifts in pH can impact the gut microbial community structure( Reference Duncan, Louis and Thomson 36 , Reference Walker, Duncan and Leitch 38 , Reference Belenguer, Duncan and Holtrop 39 ).

There is much interest in modulating microbial metabolism in the intestine through dietary additives such as prebiotics that are defined as ‘a selectively fermented ingredient that allows specific changes, both in the composition and/or activity in the GI microbiota that confers benefits upon host well being and health’( Reference Gibson and Roberfroid 40 ). Current prebiotics are mainly carbohydrates of low digestibility that are found naturally in foodstuffs. Candidate prebiotics include xylo-oligosaccharides and galacto-oligosaccharides( Reference Macfarlane, Steed and Macfarlane 41 , Reference Macfarlane, Macfarlane and Cummings 42 ) with most studies focusing on the use of inulin and fructo-ologosaccharides. The high numbers of Bifidobacterium species in the faeces of breast-fed babies is thought to result from their ability to utilise oligosaccharides in breast milk, including galacto-oligosaccharides while formula fed infants tend to harbour a greater diversity of organisms including a higher abundance of enterococci( Reference Harmsen, Wildeboer-Veloo and Raangs 43 ). The metabolite profiles also differ with higher levels of NH3, amines and phenols in bottle fed babies compared with breast-fed infants( Reference Heavey, Savage and Parrett 44 ). Not all oligosaccharides, however, are highly selective( Reference Flint, Duncan, Scott and Louis 3 ) and fructans, for example, have been shown to promote one or more groups of bacteria in addition to their effects on bifidobacteria( Reference Kruse, Kleessen and Blaut 45 Reference Apajalahti, Kettunen and Kettunen 47 ). As there is extensive inter-individual variation in composition of the gut microbiota there is likely to be inter-individual variation in the response of the microbial communities to prebiotics( Reference Ramirez-Farias, Slezak and Fuller 46 ). Recent evidence supports the view that there are likely to be detrimental health consequences of reduced carbohydrate intake especially when combined with an increase in the consumption of protein and/or fat, both of which have been potentially associated with an increased risk of colon cancer( Reference Russell, Gratz and Duncan 8 , Reference Bernstein, Bernstein and Payne 48 ).

Many protein- and protein-derived metabolites are considered to be potentially mutagenic and genotoxic and these include heterocyclic amines and N-nitroso compounds( Reference Russell, Gratz and Duncan 8 ). Studies with germ-free animals implied that the gut microbiota was essential in the formation of N-nitroso compounds( Reference Massey, Key and McWeeny 49 ) and the DNA damage by heterocyclic amines was decreased compared with that of conventional animals( Reference Kassie, Rabot and Kundi 50 ). Additional by-products of amino acid metabolism include polyamines (including putrescine, cadaverine, spermine, spermidine, pyrollidine and piperidine), indoles, NH3, hydrogen sulphide, branched chain fatty acids and the aromatic amino acid metabolites. Various species have been shown to produce these products( Reference Beerens and Romond 51 , Reference Smith and Macfarlane 52 ), but comprehensive screening of the predominant gut species has not been performed.

Significant changes in the gut metabolome were observed for volunteers consuming diets in which the protein and carbohydrate ratios were modulated( Reference Russell, Gratz and Duncan 8 ). Using liquid chromatography–MS, derivatives of a wide range of plant phenolics considered to be cancer-protective were reduced in diets high in protein (137 g/d) and low in carbohydrate (22 g/d). Phenylacetic acid, a major metabolite related to protein metabolism was found to have significantly increased. Increasing the carbohydrate content (181 g/d) resulted in significantly increasing some phenolic acids and their derivatives, principally fibre-related phenolics, namely, ferulic acid, 4-hydroxy-3-methoxyphenylpropionic acid and 3-hydroxyphenylpropionic acid.

Bile metabolism, along with lipase activity, are likely to be major factors affecting the delivery of lipids to the colon. Bile acids are predominantly deconjugated and dehydroxylated to secondary bile acids and Bacteroides intestinalis, Bacteroides fragilis and Escherichia coli have been shown to be involved in the production of deoxycholic and lithocholic acid( Reference Fukiya, Arata and Kawashima 53 ). Two major lipid metabolism pathways have been identified; hydration and reduction. The species responsible for the formation of hydrated lipids (i.e. hydroxy fatty acids) were predominantly Clostridium perfringens ( Reference Pearson, Drasar and Wiggins 54 ) and Roseburia species( Reference Devillard, McIntosh and Duncan 55 ), although some other species could produce these metabolites to a lesser extent( Reference Devillard, McIntosh and Duncan 55 ). The species catalysing fatty acid reduction are less well characterised; only species responsible for the conversion of linoleic acid to vaccenic acid have been identified, namely Butyrivibrio fibrisolvens and Roseburia species( Reference McIntosh, Shingfield and Devillard 56 ).

Micronutrient influences on the gut microbiota

It has been shown that the microbiota can synthesise some vitamin B (B3, B5 and B6) and related molecules including biotin, tetrahydrofolate, vitamin K and the corrinoids( Reference O'Keefe, Ou and Aufreiter 57 ). Microbes are considered to affect absorption of certain dietary minerals, with many studies demonstrating that carbohydrate and associated SCFA modulate uptake of Na, Ca and K. Many bacteria also actively accumulate Fe as a siderophore complex( Reference Neilands 58 ). There is very little information regarding the direct effect of the gut microbiota on mineral uptake and the species associated have not been identified.

Non-nutrient/phytochemical influences on the gut microbiota

Epidemiological studies suggest that there is an inverse association between the intake of phytochemical-rich diets and the incidence of CVD, diabetes and cancer( Reference Boeing, Bechthold and Bub 59 ). Edible plant material contains hundreds of compounds most of which play an important role for the plant including protection against pathogens. Biochemically, they can be broadly categorised according to their structure and biosynthetic pathways, but it should be appreciated that many secondary metabolites are derived by combining elements from all of these biosynthetic routes. Some of these compounds particularly if available as small molecules or as their aglycones, may be absorbed in the upper GI tract and directly enter systemic circulation. However, many and in particular those bound to other plant components such as carbohydrates will be available in the colon. Within the colon these phytochemicals are extensively metabolised. Gut microbiota are capable of performing many transformations including: hydrolysis, deamination, dehydrogenation, demethylation, decarboxylation, ring cleavage and chain shortening.

Impact of the gut metabolome on the human host

It is becoming increasingly important to determine which microbial-derived products are responsible for disease development and/or progression (Fig. 2). Once identified these molecules may be used as a potential diagnostic/prognostic tool for inflammatory diseases and related co-morbidities.

Fig. 2. Potential impact of the gut microbiota on the human host.

SCFA

Active fermentation of carbohydrates in the colon results in the formation of SCFA( Reference Barry, Hoebler and Macfarlane 60 ) together with gases, mainly H2, CO2 and methane. The SCFA detected in stool samples are a sub group of fatty acids with aliphatic tails with less than six carbons and include fornic, acetic, propionic, butyric and valeric acid. Branched-chain examples include isovaleric and isobutyric acid. There are also substituted short-chain carboxylic acids such as lactic acid. The total concentration of SCFA in the large intestine may reach upwards of 100 mm ( Reference Macfarlane and Macfarlane 61 ). Dietary shifts can result in changes in SCFA production rates and in the molar proportions of different SCFA detected in faeces. Weight loss diets that are high in protein but low in carbohydrates, for example, were recently shown to reduce faecal butyrate up to four-fold( Reference Duncan, Belenguer and Holtrop 2 ) while higher proportions of propionate and butyrate and lower acetate have been reported to result from increasing prebiotic or fibre intake( Reference Queenan, Stewart and Smith 62 ). SCFA are likely to have several effects upon health. Butyrate is largely considered beneficial for gut health as it is the major energy source for the colonocytes and has a role in CRC prevention as discussed later( Reference Pryde, Duncan and Hold 63 Reference Sleeth, Thompson and Ford 65 ). Propionate is metabolised in the liver and is gluconeogenic. Activation of the gut receptors G protein-coupled receptor (GPR) 41 and GPR43 (also known as NEFA receptors NEFA2 and NEFA3) by SCFA influences gut motility as well as reducing inflammatory responses( Reference Brown, Goldsworthy and Barnes 66 , Reference Tazoe, Otomo and Kaji 67 ). Acetate is metabolised in the peripheral tissues and is a precursor for cholesterol metabolism and lipid formation. A shift in fermentation products away from acetate (normally present at the highest concentration) towards propionate and butyrate may therefore be beneficial and explain the decrease in cholesterol levels in volunteers consuming fibre( Reference Brown, Rosner and Willett 68 ). Increased SCFA concentrations may also increase the solubility of certain minerals such as Ca, and enhance absorption and expression of Ca-binding proteins( Reference Scholz-Ahrens and Schrezenmeir 69 ). Changes in intestinal microbial metabolism following the consumption of inulin fructans has also been shown to improve bone health by increasing Ca absorption while β-glucans may lower total cholesterol levels( Reference Smith, Queenan and Thomas 70 ).

Phytochemicals

There is a vast amount of literature suggesting that plant secondary metabolites have properties beneficial to health( Reference Russell and Duthie 71 ) In particular, almost all plant foods considered to have cancer-preventative properties are rich in compounds derived from the phenylpropanoid pathway. Most of this information is obtained from in vitro data and there is very little evidence from both pre-clinical and human interventions to support this. Bioavailability may be defined as the fraction of an ingested nutrient or metabolite that reaches systemic circulation and specific sites where it can exert specific biological activity. The most abundant phytochemicals consumed may not, however, be the most bio-available to the host. Bioavailability may be influenced by food composition as most of the phytochemicals are likely to be complexed with other plant components. Host factors will also influence bioavailability including enzyme activity and gut transit time. Following ingestion, the absorption of certain phytochemicals will occur in the small intestine where some glycosides will be hydrolysed. Rapidly absorbed plant metabolites will enter systemic circulation following methylation, sulphation and/or glucuronidation. However, many phytochemicals escape absorption early in the GI tract and in particular those bound to plant polymers reach the colon and are metabolised and released by the gut microbiota( Reference Russell, Scobbie and Labat 72 ). Data regarding the metabolism and bioavailability of these products are severely lacking, particularly with reference to the mechanisms of transformation and the species responsible. The involvement of the gut microbiota in conversion of these plant metabolites is demonstrated by the fact that their metabolites appear after 6–8 h in systemic circulation( Reference Russell, Gratz and Duncan 8 ). The majority of plant metabolites present in the form of glycosides will be converted to aglycones prior to further transformation. It is likely that the compounds that reach the functional sites such as cells and tissues will be chemically different from those consumed in the diet. To evaluate their potential role in cancer prevention, the structure, concentration and site of action must be established. In the colon, they can exert a direct action such as an anti-inflammatory effect on the gut mucosa or be absorbed from the colon via hepatic circulation and suppress low-level chronic systemic inflammation.

Both the parent compounds and the metabolites produced have the potential to influence specific microbial groups. Of the secondary metabolites, the group most widely studied are products of the phenylpropanoid pathway, as nearly all plant foods considered to have cancer-preventative properties are rich in these compounds( Reference Russell, Scobbie and Labat 73 ) including cinnamic, phenylacetic, phenylpropionic, coumarins, flavonoids and anthocyanidins( Reference Russell, Scobbie and Labat 72 ).

Although these phenylpropanoid derivatives are most widely studied, many other secondary metabolites and their derivatives are present in the human colon. Moreover, glucosinolates and their metabolites (isothiocyanates and indoles) have also been extensively studied in relation to protection against carcinogenesis and mutagenesis( Reference Higdonm, Delage and Williams 74 , Reference Mithen, Dekker and Verkerk 75 ), while many other N- and S-related compounds have been generally overlooked in terms of metabolism. For some studies, the presence of specific gut metabolites was detected in plasma and urine by MS indicating that these compounds have entered systemic circulation via enterohepatic circulation. For example, urinary phenolic acid metabolites, measured by GC–MS, were significantly increased following consumption of red wine and red grape juice extracts( Reference van Dorsten, Grün and van Velzen 76 ). The best markers of intake included syringic acid, 3- and 4-hydroxyhippuric acid and 4-hydroxymandelic acid. Reductions in p-cresol sulphate, 3-indoxylsulphuric acid and increases in indole-3-acetic acid and nicotinic acid were also observed in urine following consumption of red wine and grape extracts( Reference Jacobs, Fuhrmann and van Dorsten 77 ). In addition, sesamin, a major bioactive lignin found in sesame seeds was metabolised to enterolactone by in vitro incubation with mixed faecal microbiota. Sesamin consumption also demonstrated that this compound was a precursor to enterolactone in vivo ( Reference Peñalvo, Heinonen and Aura 78 ). Ingestion of a range of ellagitannin-rich foods, demonstrated that the microbial metabolite 3,8-dihydroxy-6H-dibenzo[b,d]pyran-6-one (urolithin B) conjugated with glucuronic acid was detected in urine by liquid chromatography–MS( Reference Cerdá, Tomás-Barberán and Espín 79 ). These urolithin metabolites were also presently found to be present in human plasma( Reference Seeram, Henning and Zhang 80 ).

Gut metabolites and human disease

Obesity and cancer are characterised by chronic low-grade inflammation and the products of microbial metabolism have the ability to modulate these effects (Fig. 3). Although the molecular mechanisms are still uncertain, particular receptors appear to have a clear role. These include Toll-like receptor (TLR) 4 and TLR5. The lipopolysaccharide of B. fragilis is unusual and likely to be at least 100–1000-times less toxic than that of E. coli. There is currently much interest in the role of bacterial lipopolysaccharide signalling via TLR4 and invoking a low grade inflammatory response which in turn may impact on metabolic health. Mice deficient in TLR5( Reference Vijay-Kumar, Aitken and Carvalho 81 ) showed increased adipose-tissue mass, reduced insulin sensitivity, increased blood lipids and higher blood pressure when compared with normal mice and a high-fat diet exacerbated these effects and demonstrated features of human metabolic syndrome. Interestingly, when germ-free normal mice were inoculated with the microbiota obtained from TLR5 deficient mice, these mice also developed symptoms of metabolic syndrome( Reference Vijay-Kumar, Aitken and Carvalho 81 ). In this case, the ligands acting on these receptors are unknown.

Fig. 3. Effect of the gut microbiota on the relationship between obesity and cancer.

Bacteria ferment dietary residues to SCFA and acetate, propionate and butyrate are the major acids detected. In addition to being the major energy source for the colonocytes( Reference Hamer, Jonkers and Venema 82 ), butyrate has a role in inhibition colonic inflammation and oxidative stress. At the molecular level, the anticarcinogenic effect of butyrate is a result of regulation of gene expression and the inhibition of histone deacetylease activity. Butyrate is transported across the colonic epithelium by two specific carrier-mediated transport systems both of which have been reported to function as tumour suppressors. SCFA generated by the microbiota modulate the immune response through GPR43( Reference Masklowski, Vieira and Ng 83 ) which are expressed in a wide range of host tissues. In particular, butyrate and propionate, have been identified as physiological ligands. Activation of GPR43 by SCFA contributes to the inhibition of lipolysis and to adipocyte differentiation which may be modulated by fructo-oligosaccharides( Reference Sleeth, Thompson and Ford 65 , Reference Delzenne and Cani 84 , Reference Tang, Chen and Jiang 85 ). Peptidoglycan released from the microbiota has also been shown to prime the innate immune system through NOD1( Reference Clarke, Davis and Lysenko 86 ).

In this context, the potential impact of dietary metabolites extends beyond gut health to include cardiovascular and metabolic health. This includes Type 2 diabetes mellitus which was found to be associated with changes in the gut microbiota regardless of BMI. Specifically, clostridial species were reduced and the ratio of Bacteroides to Firmicutes correlated positively with plasma glucose concentration, but not with BMI( Reference Larsen, Vogensen and van den Berg 87 ). Bacteroides vulgatus and Bifidobacterium species were also lower in the diabetic group( Reference Wu, Wang and Sun 88 ). Subjects with Type 2 diabetes mellitus were also found to have reduced numbers of F. prausnitzii, which correlated with increased inflammatory markers( Reference Furet, Kong and Tap 89 ). Evidence also supports the hypothesis that host recognition of the gut microbiota is essential in preventing onset and progression of Type 1 diabetes. This is likely to involve the myeloid differentiation factor 88 signalling pathway, but the microbial product initiating the response is yet to be identified.

Obesity

Obesity is a major health problem both in developed and in developing nations and arises when energetic content of food ingested is in excess of energy expenditure. Excess body fat is associated with a number of metabolic diseases such as diabetes, CVD and cancer and can also have a major impact on longevity and quality of life. There has been much interest in the potential role of non-digestible dietary carbohydrates for body weight control and obesity( Reference Nilsson, Ostman and Holst 90 ). Drastic reduction in total dietary carbohydrate intake in weight loss diets alters the composition of the colonic microbial community as well as faecal metabolite profiles( Reference Duncan, Belenguer and Holtrop 2 , Reference Duncan, Lobley and Holtrop 91 ). Colonic fermentation provides an additional source of energy to the host via absorption of SCFA that is estimated to contribute about 10% of dietary energetic intake( Reference Bergman 92 ) and bacterial fermentative activity in the colon may contribute to fat deposition( Reference Backhed, Ley and Sonnenburg 93 , Reference Turnbaugh, Ley and Mahowald 94 ). The energy recovered from ingested sugar by this route is, however, less than that for sugar directly absorbed in the small intestine( Reference Roberfroid 95 ). The net effect must therefore depend largely on how alternative sources of dietary carbohydrate influence satiety. High intakes of monosaccharides such as glucose and fructose present in soft drinks appear to increase serum ghrelin, activating hunger signals and decreasing satiety( Reference Dornonville de la Cour, Lindqvist and Egecioglu 96 , Reference Robertson, Bickerton and Dennis 97 ). It has been suggested that fructo-ologosaccharides intake, on the other hand, results in decreased ghrelin levels that may help in the control of food intake( Reference Delzenne, Cani and Neyrinck 98 ).

Obese human subjects on weight loss diets were shown to have altered microbial profiles( Reference Ley, Backhed and Turnbaugh 20 , Reference Ley 99 ). Also, drastic reduction in total dietary carbohydrate intake in weight loss diets alters the composition of the colonic microbial community as well as faecal metabolite profiles( Reference Duncan, Belenguer and Holtrop 2 , Reference Duncan, Lobley and Holtrop 91 ). Microbial metabolites associated with increased weight gain include increased excretion of hypoxanthine, hippurate, dimethylglycine and creatinine in the urine( Reference Zhang, Yan and Gao 100 ). Studies where weight loss was achieved via gastric bypass surgery demonstrated that asparagine, lysophosphatidylcholine (C18:2), nervonic (C24:1) acid, p-cresol sulfate, lactate, lycopene, glucose and mannose were all significantly reduced( Reference Mutch, Fuhrmann and Rein 101 ).

Cancer

CRC accounts for approximately 17 000 deaths in the UK annually and progression of the disease is likely to result from a combination of genetic and environmental factors. The majority of CRC is thought to be sporadic in origin with the most common form, adenocarcinoma, developing from glandular cells lining the colonic wall. There are several genetic events commonly occurring in CRC at the molecular level that include inactivation of the tumour suppressor genes such as p53 along with activation of oncogenes including the ras family of genes. Not all colorectal polyps progress to cancer, suggesting that perhaps other factors can influence malignant transformation. There is increasing evidence supporting the role of inflammation in the pathogenesis of the gut, including diseases such as CRC. Compared with healthy subjects, patients suffering from inflammatory bowel disease are considerably more likely to develop CRC( Reference Rhodes and Campbell 102 ). One of the similarities between inflammatory bowel disease-associated and sporadic CRC is the importance of cyclooxygenase-2 induction and this enzyme is induced in response to mediators of inflammation, cytokines and endotoxins. In the healthy colon, the thick mucin layer helps to protect the epithelial cells from direct contact with bacterial cells( Reference Johansson, Johansson-Haque and Okret 103 ). Moreover, tight junctions are located in the epithelium that create a barrier which regulates permeability of the epithelial layer in response to various signals including cytokines. Maintenance of an intact epithelial layer is one of the mechanisms that limit bacterial translocation. Bacterial cells may also provide a source of regulatory signals that, for example, direct the differentiation of T-helper cells producing IL-17 (T-helper 17 cells) and T-regulatory cell activity. These signals influence the maturation of the gut and the immune system. In the absence of bacteria these defence systems are likely to be weakened.

Most of the bacteria in the colon possess a number of microbial-associated molecular pattern moieties that are well recognised by cells of the innate immune system. These molecular motifs are recognised by TLR. For example, bacterial lipopolysaccharide, an endotoxin is recognised by TLR4, a recognition receptor of the innate immune system. Bacterial flagellin is recognised by TLR5 and lipoteichoic acid by TLR2. Deficiency in TLR2 has been reported to lead to both increased tumour burden and size in mice with dextran sodium sulphate-induced colitis. This has been related to increased cytokine levels of, for example, IL-6; TNFα that may drive inflammation and induce CRC. Separately, both TLR2 and TLR5 play a role in the suppression of tumour formation, via the activation of specific anti-tumour immunity. Mice deficient in TLR4 were protected against the development of neoplasia as TLR4 signalling can promote colon carcinogenesis by stimulating tumour infiltration of T-helper 17 cells. One of the key lipopolysaccharide producing species in the colon is E. coli ( Reference Erridge, Duncan and Bereswill 104 ) although the abundance of this species is likely to be a small proportion of the total bacterial load. There have been a number of bacterial species associated with CRC including the Gram-positive Streptococcus species and the Gram-negative Helicobacter species, B. fragilis and E. coli. It is not only the balance of bacterial species in the colon that is important for maintaining gut health; in addition the impact of changing bacterial composition in response to dietary intake also drives bacterial metabolite formation.

Microbial metabolites may be a key factor in regulating inflammatory and immunological responses in the colon. Metabolism of dietary carbohydrates will result in the formation of SCFA and as discussed earlier butyrate plays an important role in health maintenance. Increasing the protein content of the diet particularly by increasing red meat intake is likely to result in increased levels of toxic metabolites that include heterocyclic amines, fecapentaenes, nitrosamines, super oxide radicals and hydrogen sulphide( Reference Gill and Rowland 105 ). Fecapentaenes are mutagens that are reportedly synthesised by Bacteroides species that may alkylate DNA to form mutagenic adducts. Hydrogen sulphide is also a toxic microbial metabolite formed by sulphate reducing bacteria, including Desulfovibrio piger, in the colon. D. piger can metabolise lactic acid that may accumulate in bowel disease( Reference Vernia, Caprilli and Latella 106 , Reference Vernia and Cittadini 107 ) while reducing sulphate to sulphide( Reference Marquet, Duncan and Chassard 108 ) and meat is a major source of sulphur that promotes the growth of sulphate reducing bacteria( Reference Christl, Gibson and Cummings 109 ). The genotoxic potential of hydrogen sulphide is in part mediated by oxidative free radicals and cyclooxygenase-2 is up-regulated in epithelial cells following administration of hydrogen sulphide at physiological concentrations( Reference Magee, Richardson and Hughes 110 ). Colonic bacteria also play a role in the formation of N-nitroso compounds, the levels of which are elevated following intake of high protein diets, particularly meat( Reference Russell, Gratz and Duncan 8 ). Cooking meat generates heterocyclic amines that can be further transformed to genotoxic intermediates. The production of these products is likely to be linked to increased risk of CRC( Reference Hughes, Magee and Bingham 111 ).

Secondary bile acids, mainly deoxycholic acid and chenodeoxycholic acid, are formed by microbial conversion of the primary acids that are formed in the liver and secreted into the duodenum. Approximately one litre of bile enters the duodenum each day; however, bile acid excretion is related to fat and red meat intake that are potential risk factors for CRC( Reference Reddy 112 ). Epidemiological studies reported higher concentrations of secondary bile acids in CRC patients compared with healthy controls( Reference Gill and Rowland 105 ). Secondary bile acids can cause DNA damage( Reference Gill and Rowland 105 ) by the production of oxygen radicals and reactive nitrogen species( Reference Payne, Bernstein and Bernstein 113 , Reference Dvorak, Payne and Chavarria 114 ). Bile acids enter enterohepatic circulation therefore the concentration decreases along the GI tract, however, elevated bile acid levels may modulate the abundance of certain bacterial species including F. prausnitzii ( Reference Lopez-Siles, Khan and Duncan 115 ) that has been reported to have potent anti-inflammatory activity( Reference Sokol, Pigneur and Watterlot 116 ).

Conclusions

The large intestine may appear to be a hostile environment for bacteria to inhabit, but nonetheless represents a densely populated microbial ecosystem. Moreover, on balance the host is likely to benefit from these multi-species communities in the gut when the balance of species provides mostly beneficial metabolites. Microbial imbalance however may well result in a less favourable metabolic output that will make an impact on inflammatory status and disease progression. As an example, carefully controlled dietary intervention studies have revealed that high protein diets (mainly meat-based) result in elevated levels of hazardous metabolites and a decrease in cancer protective metabolites. High protein diets are satiating and therefore, in the short term may be efficacious in achieving weight loss( Reference Johnstone, Horgan and Murison 117 ). In addition, the health risks associated with consuming high meat protein diets may be partially ameliorated by including cereals in the diet and/or exchanging meat protein for plant protein. The phenolic content of the latter may afford some health benefits. Understanding of the microbial ecosystem of the human colon will continue to benefit from a range of molecular approaches which should be developed in parallel with metabolomic approaches. Our dietary intake clearly has an important influence on the species composition of gut microbiota, but this appears insufficient to explain the extent of variation that is seen between individuals. As MS methodologies continue to develop and its usage increases a clearer and much needed understanding of the complex interplay between diet, the gut microbiota and human health will be achieved. There is also a need to link detailed microbial diversity to metabolic functionality to ascertain if general dietary advice is sufficient or there may be a need, in certain circumstances, to provide personalised nutritional advice.

Acknowledgements

The authors are grateful for funding from the Scottish Government Food, Land and People Programme. The authors declare no conflict of interest and contributed equally to the writing of this manuscript.

References

1. Walker, AW, Ince, J, Duncan, SH et al. (2011) Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J 5, 220230.Google Scholar
2. Duncan, SH, Belenguer, A, Holtrop, G et al. (2007) Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73, 10731078.CrossRefGoogle ScholarPubMed
3. Flint, HJ, Duncan, SH, Scott, KP & Louis, P (2007) Interactions and competition within the microbial community of the human colon: links between diet and health. Environ Microbiol 9, 11011111.Google Scholar
4. Qin, JJ, Li, RQ, Raes, J et al. (2010) A human gut microbial gene catalogue established by metagenome sequencing. Nature 464, 5970.CrossRefGoogle Scholar
5. Chen, W, Liu, F, Ling, Z et al. (2012) Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer. PLoS ONE 7, e39743.Google Scholar
6. Holmes, E, Li, JV, Athanasiou, T et al. (2011) Understanding the role of gut microbiome-host metabolic signal disruption in health and disease. Trends Microbiol 19, 349359.Google Scholar
7. Fleissner, KF, Huebel, N, Abd El-Bary, MM et al. (2010) Absence of intestinal microbiota does not protect mice from diet-induced obesity. Br J Nutr 104, 919929.Google Scholar
8. Russell, WR, Gratz, SW, Duncan, SH et al. (2011) High protein, reduced carbohydrate weight loss diets promote metabolite profiles likely to be detrimental to colonic health. Am J Clin Nutr 93, 111.CrossRefGoogle ScholarPubMed
9. Bjursell, MK, Martens, EC & Gordon, JI (2006) Functional genomic and metabolic studies of the adaptations of a prominent adult human gut symbiont, Bacteroides thetaiotaomicron, to the suckling period. J Biol Chem 281, 3626936279.CrossRefGoogle Scholar
10. O'Hara, AM & Shanahan, F (2006) The gut flora as a forgotten organ. EMBO Rep 7, 688693.Google Scholar
11. Kurokawa, K, Itoh, T, Kuwahara, T et al. (2007) Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res 14, 169181.Google Scholar
12. Tap, J, Mondot, S, Levenez, F et al. (2009) Towards the human intestinal microbiota phylogenetic core. Environ Microbiol 11, 25742584.CrossRefGoogle ScholarPubMed
13. Flint, HJ, Scott, KP, Duncan, SH et al. (2012) Microbial degradation of complex carbohydrates in the gut. Gut Microbes 3, 118.Google Scholar
14. Duncan, SH, Hold, GL, Barcenilla, A et al. (2002) Roseburia intestinalis sp. a novel saccharolytic, butyrate-producing bacterium from human faeces. Int J Syst Evolut Microbiol 52, 16151620.Google Scholar
15. Duncan, SH, Louis, P & Flint, HJ (2004) Lactate-utilising bacteria, isolated from human faeces, that produce butyrate as a major fermentation product. Appl Environ Microbiol 70, 58105817.Google Scholar
16. Louis, P, Duncan, SH, McCrae, S et al. (2004) Restricted distribution of the butyrate kinase pathway among butyrate-producing bacteria from the human colon. J Bacteriol 186, 20992106.Google Scholar
17. Willing, BP, Dicksved, J, Halfvarson, J et al. (2012) A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phylotypes. Gastroenterology 139, 18441854.CrossRefGoogle Scholar
18. Sokol, H, Pigneur, B, Watterlot, L et al. (2008) Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbe analysis of Crohn disease patients. Proc Natl Acad Sci USA 105, 1673116736.CrossRefGoogle Scholar
19. van Tongeren, SP, Slaets, JP, Harmsen, HJ et al. (2005) Fecal microbiota composition and frailty. Appl Environ Microbiol 71, 64386442.Google Scholar
20. Ley, RE, Backhed, F, Turnbaugh, P et al. (2005) Obesity alters gut microbial ecology. Proc Natl Acad Sci USA 102, 1107011075.Google Scholar
21. Relman, DA (2012) The human microbiome: ecosystem resilience and health. Nutr Rev 70, S2S9.CrossRefGoogle ScholarPubMed
22. Abell, GCJ, Cooke, CM, Bennett, CN et al. (2008) Phylotypes related to Ruminococcus bromii are abundant in the large bowel of humans and increase in response to a diet high in resistant starch. FEMS Microbiol Ecol 66, 505515.CrossRefGoogle ScholarPubMed
23. Flint, HJ, Duncan, SH, Scott, KP et al. (2007) Interactions and competition within the microbial community of the human large intestine: links between diet and health. Environ Microbiol 9, 11011111.Google Scholar
24. Davis, LMG, Martinez, I, Walter, J et al. (2010) A dose dependent impact of prebiotic galactooligosaccharides on the intestinal microbiota of healthy adults. Int J Food Microbiol 144, 285292.CrossRefGoogle ScholarPubMed
25. Arumugam, M, Raes, J, Pelletier, E et al. (2011) Enterotypes of the human gut microbiome. Nature, 174180.CrossRefGoogle ScholarPubMed
26. Wu, GD, Chen, J, Hoffmann, C et al. (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105108.CrossRefGoogle ScholarPubMed
27. Riesenfeld, CS, Schloss, PD & Handelsman, J (2004) Metagenomics: genomic analysis of microbial communities. Ann Rev Genetics 28, 525552.Google Scholar
28. Villas-Bôas, SG, Mas, S, Åkesson, M et al. (2005) Mass spectrometry in metabolome analysis. Mass Spectr Rev 24, 613646.Google Scholar
29. Hollywood, K, Brison, DR & Goodacre, R (2006) Metabolomics: Current technologies and future trends. Proteomics 6, 47164723.CrossRefGoogle ScholarPubMed
30. De Filippo, C, Cavalieri, D, Di Paolo, M et al. (2010) Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci USA 107, 1469114696.Google Scholar
31. Englyst, HN, Kingman, SM & Cummings, JH (1992) Classification and measurement of nutritionally important starch fractions. Eur J Clin Nutr 46, S33S50.Google Scholar
32. Leitch, ECM, Walker, AW, Duncan, SH et al. (2007) Selective colonization of insoluble substrates by human faecal bacteria. Environ Microbiol 9, 667679.Google Scholar
33. Aminov, RI, Walker, AW, Duncan, SH et al. (2006) Molecular diversity, cultivation, and improved FISH detection of a dominant group of human gut bacteria related to Roseburia and Eubacterium rectale . Appl Environ Microbiol 72, 63716376.Google Scholar
34. Lewis, SJ & Heaton, KW (1997) Increasing butyrate concentration in the distal colon by accelerating intestinal transit. Gut 41, 245251.Google Scholar
35. Lampe, JW, Slavin, JL, Melcher, EA et al. (1992) Effects of cereal and vegetable fiber feeding on potential risk-factors for colon cancer. Cancer Epidemiol Biomarkers Prevention 1, 207211.Google Scholar
36. Duncan, SH, Louis, P, Thomson, JM et al. (2009) The role of pH in determining the species composition of the human colonic microbiota. Environ Microbiol 11, 21122122.Google Scholar
37. Bown, RL, Gibson, JA, Sladen, GE et al. (1974) Effects of lactulose and other laxatives on ileal and colonic pH as measured by a radiotelemetry device. Gut 15, 9991004.Google Scholar
38. Walker, AW, Duncan, SH, Leitch, ECM et al. (2005) pH and peptide supply can radically alter bacterial populations and short-chain fatty acid ratios within microbial communities from the human colon. Appl Environ Microbiol 71, 36923700.CrossRefGoogle ScholarPubMed
39. Belenguer, A, Duncan, SH, Holtrop, G et al. (2007) Impact of pH on lactate formation and utilization by human fecal microbial communities. Appl Environ Microbiol 73, 65266533.Google Scholar
40. Gibson, GR & Roberfroid, MB (1995) Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. J Nutr 125, 14011412.Google Scholar
41. Macfarlane, GT, Steed, H & Macfarlane, S (2008) Bacterial metabolism and health-related effects of galacto-oligosaccharides and other prebiotics. J Appl Microbiol 104, 305344.Google ScholarPubMed
42. Macfarlane, S, Macfarlane, GT & Cummings, JH (2006) Review article: Prebiotics in the gastrointestinal tract. Aliment Pharmacol Ther 24, 701714.Google Scholar
43. Harmsen, HJM, Wildeboer-Veloo, ACM, Raangs, GC et al. (2000) Analysis of intestinal flora development in breast-fed and formula-fed infants by using molecular identification and detection methods. J Pediatr Gastroenterol Nutr 30, 6167.CrossRefGoogle ScholarPubMed
44. Heavey, PM, Savage, SA, Parrett, A et al. (2003) Protein-degradation products and bacterial enzyme activities in faeces of breast-fed and formula-fed infants. Br J Nutr 89, 509515.Google Scholar
45. Kruse, HP, Kleessen, B & Blaut, M (1999) Effects of inulin on faecal bifidobacteria in human subjects. Br J Nutr 82, 375382.Google Scholar
46. Ramirez-Farias, C, Slezak, K, Fuller, Z et al. (2009) Effect of inulin on the human gut microbiota: stimulation of Bifidobacterium adolescentis and Faecalibacterium prausnitzii . Br J Nutr 101, 541550.Google Scholar
47. Apajalahti, JH, Kettunen, H, Kettunen, A et al. (2002) Culture-independent microbial community analysis reveals that inulin in the diet primarily affects previously unknown bacteria in the mouse cecum. Appl Environ Microbiol 68, 49864995.Google Scholar
48. Bernstein, H, Bernstein, C, Payne, CM et al. (2005) Bile acids as carcinogens in human gastrointestinal cancers. Mutat Res 589, 4765.Google Scholar
49. Massey, RC, Key, PE, McWeeny, DJ et al. (1987) An investigation of apparent total N-nitroso compounds in beer. IARC Sci Publ 8421984221.Google Scholar
50. Kassie, F, Rabot, S, Kundi, M et al. (2001) Intestinal microflora plays a crucial role in the genotoxicity of the cooked food mutagen 2-amino-3-methylimidazo [4,5-f]quinoline. Carcinogenesis 22, 17211725.Google Scholar
51. Beerens, H & Romond, C (1977) Sulfate-reducing anaerobic bacteria in human feces. Am J Clin Nutr 30, 17701776.Google Scholar
52. Smith, EA & Macfarlane, GT (1996) Studies on amine production in the human colon: enumeration of amine forming bacteria and physiological effects of carbohydrate and pH. Anaerobe 2, 285297.Google Scholar
53. Fukiya, S, Arata, M, Kawashima, H et al. (2009) Yokota Conversion of cholic acid and chenodeoxycholic acid into their 7-oxo derivatives by Bacteroides intestinalis AM-1 isolated from human feces. FEMS Microbiol Lett 293, 263270.Google Scholar
54. Pearson, JR, Drasar, BS & Wiggins, HS ( 1973). Proceedings: the role of faecal flora in the production of hydroxystearic acid. Gut 14, 830831.Google Scholar
55. Devillard, E, McIntosh, FM, Duncan, SH et al. (2007) Metabolism of linoleic acid by human gut bacteria: different routes for biosynthesis of conjugated linoleic acid. J Bacteriol 189, 25662570.Google Scholar
56. McIntosh, FM, Shingfield, KJ, Devillard, E et al. (2009) Mechanism of conjugated linoleic acid and vaccenic acid formation in human faecal suspensions and pure cultures of intestinal bacteria. Microbiology 155, 285294.Google Scholar
57. O'Keefe, SJ, Ou, J, Aufreiter, S et al. (2009) Products of the colonic microbiota mediate the effects of diet on colon cancer risk. J Nutr 139, 20442048.Google Scholar
58. Neilands, JB (1981) Iron absorption and transport in microorganisms. Annu Rev Nutr 1, 2746.CrossRefGoogle ScholarPubMed
59. Boeing, H, Bechthold, A, Bub, A et al. (2012) Critical review: vegetables and fruit in the prevention of chronic diseases. Eur J Nutr 51, 637663.Google Scholar
60. Barry, JL, Hoebler, C, Macfarlane, GT et al. Estimation of the fermentability of dietary fibre in vitro: a European interlaboratory study. Br J Nutr 74, 303322.Google Scholar
61. Macfarlane, GT & Macfarlane, S (2002) Diet and metabolism of the intestinal flora. Biosci Microflora 21, 199208.Google Scholar
62. Queenan, KM, Stewart, ML, Smith, KN et al. (2007) Concentrated oat beta-glucan, a fermentable fiber, lowers serum cholesterol in hypercholesterolemic adults in a randomized controlled trial. Nutrition J 6, 6.CrossRefGoogle ScholarPubMed
63. Pryde, SE, Duncan, SH, Hold, GL et al. (2002) The microbiology of butyrate formation in the human colon. FEMS Microbiol Lett 217, 133139.Google Scholar
64. McIntyre, AP, Gibson, P & Young, GP (1993) Butyrate production from dietary fibre and protection against large bowel cancer in a gut model. Gut 34, 386391.Google Scholar
65. Sleeth, ML, Thompson, EL, Ford, HE et al. (2010) Free fatty acid receptor 2 and nutrient sensing: a proposed role for fibre, fermentable carbohydrates and short chain fatty acids in appetite regulation. Nutr Res Rev 23, 135145.Google Scholar
66. Brown, AJ, Goldsworthy, SM, Barnes, AA et al. (2003) The Orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J Biol Chem 278, 1131211319.CrossRefGoogle ScholarPubMed
67. Tazoe, H, Otomo, Y, Kaji, I et al. (2008) Roles of short-chain fatty acids receptors, GPR41 and GPR43 on colonic functions. J Physiol Pharmacol 59, 251262.Google ScholarPubMed
68. Brown, L, Rosner, B, Willett, WW et al. (1999) Cholesterol-lowering effects of dietary fiber: a meta-analysis. Am J Clin Nutr 69, 3042.Google Scholar
69. Scholz-Ahrens, KE & Schrezenmeir, J (2007) Inulin and oligofructose and mineral metabolism: the evidence from animal trials. J Nutr 137, 2513S2523S.Google Scholar
70. Smith, KN, Queenan, KM, Thomas, W et al. (2008) Physiological effects of concentrated barley beta-glucan in mildly hypercholesterolemic adults. J Am College Nutr 27, 434440.Google Scholar
71. Russell, W & Duthie, G (2011) Plant secondary metabolites and gut health: the case for phenolic acids. Proc Nutr Soc 70, 389396.Google Scholar
72. Russell, WR, Scobbie, L, Labat, A et al. (2009) Selective bio-availability of phenolic acids from Scottish strawberries. Mol Nutr Food Res 53, 8591.Google Scholar
73. Russell, WR, Scobbie, L, Labat, A et al. (2009) Phenolic acid content of fruits commonly consumed and locally produced in Scotland. Food Chem 115, 100104.Google Scholar
74. Higdonm, JV, Delage, B, Williams, DE et al. (2007) Cruciferous vegetables and human cancer risk: epidemiologic evidence and mechanistic basis. Pharmacol Res 55, 224236.Google Scholar
75. Mithen, RF, Dekker, M, Verkerk, R et al. (2000) The nutritional significance, biosynthesis and bioavailability of glucosinolates in human foods. J Sci Food Agric 80, 967984.Google Scholar
76. van Dorsten, FA, Grün, CH, van Velzen, EJ et al. (2010) The metabolic fate of red wine and grape juice polyphenols in humans assessed by metabolomics. Mol Nutr Food Res 54, 897908.CrossRefGoogle ScholarPubMed
77. Jacobs, DM, Fuhrmann, JC, van Dorsten, FA et al. (2012) Impact of short-term intake of red wine and grape polyphenol extract on the human metabolome. J Agric Food Chem 60, 30783085.Google Scholar
78. Peñalvo, JL, Heinonen, SM, Aura, AM et al. (2005) Dietary sesamin is converted to enterolactone in humans. J Nutr 135, 10561062.Google Scholar
79. Cerdá, B, Tomás-Barberán, FA & Espín, JC (2005) Metabolism of antioxidant and chemopreventive ellagitannins from strawberries, raspberries, walnuts, and oak-aged wine in humans: identification of biomarkers and individual variability. J Agric Food Chem 53, 227235.Google Scholar
80. Seeram, NP, Henning, SM, Zhang, Y et al. (2006) Pomegranate juice ellagitannin metabolites are present in human plasma and some persist in urine for up to 48 hours. J Nutr 136, 24812485.CrossRefGoogle ScholarPubMed
81. Vijay-Kumar, M, Aitken, JD, Carvalho, FA et al. (2010) Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328, 228231.Google Scholar
82. Hamer, HM, Jonkers, D, Venema, K et al. (2008) Review article: The role of butyrate on colonic function. Aliment Pharmacol Ther 27, 104119.Google Scholar
83. Masklowski, KM, Vieira, AT, Ng, A et al. (2009) Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature 461, 12821287.CrossRefGoogle Scholar
84. Delzenne, NM & Cani, PD (2011) Interaction between obesity and the gut microbiota: relevance in nutrition. Annu Rev Nutr 31, 1531.Google Scholar
85. Tang, Y, Chen, Y, Jiang, H et al. (2011) G-protein-coupled receptor for short-chain fatty acids suppresses colon cancer. Int J Cancer 128, 847856.CrossRefGoogle ScholarPubMed
86. Clarke, TB, Davis, KM, Lysenko, ES et al. (2010) Recognition of peptidoglycan from the microbiota by NOD1 enhances systemic innate immunity. Nat Med 16, 228231.Google Scholar
87. Larsen, N, Vogensen, FK, van den Berg, FWJ et al. (2010) Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085.Google Scholar
88. Wu, W, Wang, M, Sun, Z et al. (2012) The predictive value of TNF-α and IL-6 and the incidence of macrovascular complications in patients with type 2 diabetes. Acta Diabetol 49, 37.Google Scholar
89. Furet, JP, Kong, LC, Tap, J et al. (2010) Differential adaptation of human gut microbiota to bariatric surgery-induced weight loss: links with metabolic and low-grade inflammation markers. Diabetes 59, 30493057.CrossRefGoogle ScholarPubMed
90. Nilsson, AC, Ostman, EM, Holst, JJ et al. (2008) Including indigestible carbohydrates in the evening meal of healthy subjects improves glucose tolerance, lowers inflammatory markers, and increases satiety after a subsequent standardized breakfast. J Nutr 138, 732739.Google Scholar
91. Duncan, SH, Lobley, GE, Holtrop, G et al. (2008) Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes 11, 17201724.CrossRefGoogle Scholar
92. Bergman, EN (1990) Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev 70, 567590.Google Scholar
93. Backhed, F, Ley, RE, Sonnenburg, JL et al. (2005) Host-bacterial mutualism in the human intestine. Science 307, 19151920.CrossRefGoogle ScholarPubMed
94. Turnbaugh, PJ, Ley, RE, Mahowald, MA et al. (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 10271031.Google Scholar
95. Roberfroid, MB (1999) Concepts in functional foods: the case of inulin and oligofructose. J Nutr 129, 1398S1401S.Google Scholar
96. Dornonville de la Cour, C, Lindqvist, A, Egecioglu, E et al. (2005) Ghrelin treatment reverses the reduction in weight gain and body fat in gastrectomised mice. Gut 54, 907913.Google Scholar
97. Robertson, MD, Bickerton, AS, Dennis, AL et al. (2005) Insulin-sensitizing effects of dietary resistant starch and effects on skeletal muscle and adipose tissue metabolism. Am J Clin Nutr 82, 559567.Google Scholar
98. Delzenne, NM, Cani, PD & Neyrinck, AM (2007) Modulation of glucagon-like peptide 1 and energy metabolism by inulin and oligofructose: experimental data. J Nutr 137, 2547S2551S.CrossRefGoogle ScholarPubMed
99. Ley, RE (2010) Obesity and the human microbiome. Curr Opin Gastroenterol 26, 1 511.Google Scholar
100. Zhang, Y, Yan, S, Gao, X et al. (2012) Analysis of urinary metabolic profile in aging rats undergoing caloric restriction. Aging Clin Exp Res 24, 7984.Google Scholar
101. Mutch, DM, Fuhrmann, JC, Rein, D et al. (2011) Metabolite profiling identifies candidate markers reflecting the clinical adaptations associated with Roux-en-Y gastric bypass surgery. PLoS ONE 4, e7905.Google Scholar
102. Rhodes, JM & Campbell, BJ (2002) Inflammation and colorectal cancer: IBD-associated and sporadic cancer compared. Trends Mol Med 8, 1016.Google Scholar
103. Johansson, AS, Johansson-Haque, K, Okret, S et al. (2008) Ethyl pyruvate modulates acute inflammatory reactions in human endothelial cells in relation to the NF-kappaB pathway. Br J Pharmacol 154, 13181326.Google Scholar
104. Erridge, C, Duncan, SH, Bereswill, S et al. (2010) The induction of colitis and ileitis in mice is associated with marked increases in intestinal concentrations of stimulants of TLRs 2, 4, and 5. PLoS ONE e9125.Google Scholar
105. Gill, CI & Rowland, IR (2002) Diet and cancer: assessing the risk. Br J Nutr 88, S73S87.Google Scholar
106. Vernia, P, Caprilli, R, Latella, G et al. (1988) Fecal lactate and ulcerative colitis. Gastroenterology 95, 15641568.Google Scholar
107. Vernia, P & Cittadini, M (1995) Short-chain fatty acids and colorectal cancer. Eur J Clin Nutr 49, S18S21.Google Scholar
108. Marquet, P, Duncan, SH, Chassard, C et al. (2009) Lactate has the potential to promote hydrogen sulphide formation in the human colon. FEMS Microbiol Lett 299, 128134.Google Scholar
109. Christl, SU, Gibson, GR & Cummings, JH (1992) Role of dietary sulphate in the regulation of methanogenesis in the human large intestine. Gut 33, 12341238.Google Scholar
110. Magee, EA, Richardson, CJ, Hughes, R et al. (2012) Contribution of dietary protein to sulfide production in the large intestine: an in vitro and a controlled feeding study in humans. Am J Clin Nutr 72, 14881494.Google Scholar
111. Hughes, R, Magee, EA & Bingham, S (2000) Protein degradation in the large intestine: relevance to colorectal cancer. Curr Iss Intest Microbiol 1, 5158.Google ScholarPubMed
112. Reddy, BS (1980) Dietary fibre and colon cancer: epidemiologic and experimental evidence. Can Med Assoc J 123, 850856.Google Scholar
113. Payne, CM, Bernstein, C, Bernstein, H et al. (1999) Reactive nitrogen species in colon carcinogenesis. Antioxid Redox Signal 1, 449467.Google Scholar
114. Dvorak, K, Payne, CM, Chavarria, M et al. (2007) Bile acids in combination with low pH induce oxidative stress and oxidative DNA damage: relevance to the pathogenesis of Barrett's oesophagus. Gut 56, 763771.Google Scholar
115. Lopez-Siles, M, Khan, TM, Duncan, SH et al. (2012) Cultured representatives of two major phylogroups of human colonic Faecalibacterium prausnitzii can utilize pectin, uronic acids, and host-derived substrates for growth. Appl Environ Microbiol 78, 420428.Google Scholar
116. Sokol, H, Pigneur, B, Watterlot, L et al. (2008) Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci USA 105, 1673116736.Google Scholar
117. Johnstone, AM, Horgan, GW, Murison, SD et al. (2008) Effects of a high-protein ketogenic diet on hunger, appetite, and weight loss in obese men feeding ad libitum . Am J Clin Nutr 87, 4455.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1. Host factors with potential to impact on the gut microbiota.

Figure 1

Fig. 2. Potential impact of the gut microbiota on the human host.

Figure 2

Fig. 3. Effect of the gut microbiota on the relationship between obesity and cancer.