The Intestinal Metabolome: An Intersection Between Microbiota and Host

Recent advances that allow us to collect more data on DNA sequences and metabolites have increased our understanding of connections between the intestinal microbiota and metabolites at a whole-systems level. We can also now better study the effects of specific microbes on specific metabolites. Here, we review how the microbiota determines levels of specific metabolites, how the metabolite profile develops in infants, and prospects for assessing a person’s physiological state based on their microbes and/or metabolites. Although data acquisition technologies have improved, the computational challenges in integrating data from multiple levels remain formidable; developments in this area will significantly improve our ability to interpret current and future data sets. Recent advances that allow us to collect more data on DNA sequences and metabolites have increased our understanding of connections between the intestinal microbiota and metabolites at a whole-systems level. We can also now better study the effects of specific microbes on specific metabolites. Here, we review how the microbiota determines levels of specific metabolites, how the metabolite profile develops in infants, and prospects for assessing a person’s physiological state based on their microbes and/or metabolites. Although data acquisition technologies have improved, the computational challenges in integrating data from multiple levels remain formidable; developments in this area will significantly improve our ability to interpret current and future data sets. Henry J. HaiserView Large Image Figure ViewerDownload Hi-res image Download (PPT)Will Van TreurenView Large Image Figure ViewerDownload Hi-res image Download (PPT)Neha GargView Large Image Figure ViewerDownload Hi-res image Download (PPT)Lavanya ReddivariView Large Image Figure ViewerDownload Hi-res image Download (PPT)Jairam VanamalaView Large Image Figure ViewerDownload Hi-res image Download (PPT)Pieter C. DorresteinView Large Image Figure ViewerDownload Hi-res image Download (PPT)Peter J. TurnbaughView Large Image Figure ViewerDownload Hi-res image Download (PPT)Rob KnightView Large Image Figure ViewerDownload Hi-res image Download (PPT)Rapid advances in sequencing technologies over the past decade have allowed researchers worldwide to assess how the intestinal microbiome affects human health.1Tringe S.G. Hugenholtz P. A renaissance for the pioneering 16S rRNA gene.Curr Opin Microbiol. 2008; 11: 442-446Crossref PubMed Scopus (143) Google Scholar Humans develop symbiotic relationships with microbes at a young age.2Dominguez–Bello M.G. Blaser M.J. Ley R.E. et al.Development of the human gastrointestinal microbiota and insights from high-throughput sequencing.Gastroenterology. 2011; 140: 1713-1719Abstract Full Text Full Text PDF PubMed Scopus (271) Google Scholar Factors such as the environment,3Ursell L.K. Van Treuren W. Metcalf J.L. et al.Replenishing our defensive microbes.Bioessays. 2013; 35: 810-817Crossref PubMed Scopus (39) Google Scholar proximity to other humans and animals,4Song S.J. Lauber C. Costello E.K. et al.Cohabiting family members share microbiota with one another and with their dogs.Elife. 2013; 2: e00458Crossref Scopus (51) Google Scholar diet,5Wu G.D. Chen J. Hoffmann C. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (3940) Google Scholar, 6David L.A. Maurice C.F. Carmody R.N. et al.Diet rapidly and reproducibly alters the human gut microbiome.Nature. 2014; 505: 559-563Crossref PubMed Scopus (5306) Google Scholar genetics,7Benson A.K. Kelly S.A. Legge R. et al.Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.Proc Natl Acad Sci U S A. 2010; 107: 18933-18938Crossref PubMed Scopus (863) Google Scholar and temporal variation8Caporaso J.G. Lauber C.L. Costello E.K. et al.Moving pictures of the human microbiome.Genome Biol. 2011; 12: R50Crossref PubMed Scopus (711) Google Scholar affect the assemblage of microbes on our skin, in our mouths, and in our guts.9Costello E.K. Lauber C.L. Hamady M. et al.Bacterial community variation in human body habitats across space and time.Science. 2009; 326: 1694-1697Crossref PubMed Scopus (2138) Google Scholar, 10Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6668) Google Scholar Our microbiota has been compared with a previously unknown organ in terms of its effects; it has extensive metabolic capabilities and carries ∼150-fold more genes than the human genome. Microbes provide the host with a range of otherwise inaccessible metabolic capabilities.11Qin J. Li R. Raes J. et al.A human gut microbial gene catalogue established by metagenomic sequencing.Nature. 2010; 464: 59-65Crossref PubMed Scopus (7030) Google ScholarUnlike the human genome, the microbiome is relatively plastic. It can be rapidly altered through factors such as diet,6David L.A. Maurice C.F. Carmody R.N. et al.Diet rapidly and reproducibly alters the human gut microbiome.Nature. 2014; 505: 559-563Crossref PubMed Scopus (5306) Google Scholar drugs, probiotics, and microbially produced metabolites. Deliberate alterations in the microbiota and/or microbiome can therefore affect health. The intestinal microbiota is viewed increasingly as an important target of pharmacological agents; specific microbes have been shown to deactivate or activate specific xenobiotics, which can alter the effects of different therapeutic agents.12Haiser H.J. Turnbaugh P.J. Is it time for a metagenomic basis of therapeutics?.Science. 2012; 336: 1253-1255Crossref PubMed Scopus (92) Google Scholar The systems-level effects of the entire microbial community on the whole metabolite repertoire are just beginning to be understood.Metabolomics and metabolite profiling analyses have been widely used to identify disease biomarkers. For example, quantification of triglyceride, glucose, and cholesterol levels in the blood can be used to determine the risk of heart disease. Similarly, the first microbiome studies sought to identify taxa that correlated with disease, physiological state, drug use, or dietary intake. However, not all exposures can alter the composition of the microbial community or its gene content; some can affect gene expression.13Maurice C.F. Haiser H.J. Turnbaugh P.J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome.Cell. 2013; 152: 39-50Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar, 14McNulty N.P. Yatsunenko T. Hsiao A. et al.The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins.Sci Transl Med. 2011; 3 (106ra106)Crossref PubMed Scopus (404) Google ScholarHumanized mice (created by transplanting human fecal microbiota into the mouse gut) have metabolomes distinct from those of conventionally raised mice.15Marcobal A. Kashyap P.C. Nelson T.A. et al.A metabolomic view of how the human gut microbiota impacts the host metabolome using humanized and gnotobiotic mice.ISME J. 2013; 7: 1933-1943Crossref PubMed Scopus (228) Google Scholar This observation indicates that different gut microbes can produce changes in metabolites throughout their host. This shift in focus from determining “who is there” toward understanding “what are they doing” is driving current studies of the human microbiota. Metabolomic studies will allow us to move from observing patterns to understanding mechanisms.Metabolomic analyses also help researchers understand the effects of rare taxa and taxa with genomic variations that affect function. Organisms are considered to be of the same species if they have >97% identity in the 16S ribosomal RNA gene. However, genomes from the same species can have large differences in DNA sequences outside the 16S ribosomal RNA gene. Importantly, they often have different sets of gene clusters that regulate production of specialized metabolites (eg, antibiotics, virulence factors, siderophores, and so on) and the composition of the microbial communities can encode many antibiotic resistance genes.16Penders J. Stobberingh E.E. Savelkoul P.H. et al.The human microbiome as a reservoir of antimicrobial resistance.Front Microbiol. 2013; 4: 87Crossref PubMed Scopus (149) Google Scholar Rasko et al determined that among 17 Escherichia coli isolates, the average genome size of a single isolate was 5020 nucleotides, although the pan-genome was ∼13,000 nucleotides.17Rasko D.A. Rosovitz M.J. Myers G.S. et al.The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates.J Bacteriol. 2008; 190: 6881-6893Crossref PubMed Scopus (583) Google Scholar Furthermore, rare taxa might have a large effect on the overall community metabolome if they have important metabolic activities, perhaps acting as keystone species.Although definitions of what constitutes a core microbiome in terms of membership is elusive, there does seem to be at least a core functional profile for the gut microbiota.10Human Microbiome Project ConsortiumStructure, function and diversity of the healthy human microbiome.Nature. 2012; 486: 207-214Crossref PubMed Scopus (6668) Google Scholar Identifying biologically important variations against this core remains a challenge. Metabolomic analyses provide a partial picture of metabolism rather than the potential for metabolism, as is provided by genome analysis, and the expression of this core set of functions can change with alterations in available substrates, such as xenobiotics, even if the microbial species membership and abundance remain constant.13Maurice C.F. Haiser H.J. Turnbaugh P.J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome.Cell. 2013; 152: 39-50Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar We review the intimate connections among animal hosts, their microbiota, and the metabolites produced by either one.Different microbial communities metabolize xenobiotic agents and dietary components in different ways to produce variable effects on many tissues in the host, including the brain18Cryan J.F. Dinan T.G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour.Nat Rev Neurosci. 2012; 13: 701-712Crossref PubMed Scopus (2390) Google Scholar (Figure 1). We discuss general metabolomic technologies and their implementation for the study of human health, assess cases in which changes in the gut microbiota alter host metabolic profiles, examine the ways in which the gut microbiota processes xenobiotics and nutritional inputs, and examine the analytical limitations of associating microbial abundances with metabolic profiles.Figure 1Interactions among host, microbiota, and metabolites. In this simplified model, the gut microbiota metabolizes substrate inputs from the host, including diet and xenobiotics, into metabolites that can enter the host’s bloodstream and affect the host peripherally. For example, therapeutic drugs can be inactivated, reducing their efficacy. Alternatively, drugs may converted to derivatives with nontarget and possibly toxic effects. Therefore, changes in these input substrates change the reservoir of available microbial substrates and alter the metabolomic profile of the gut, yielding variable effects on the host. The new host phenotype can in turn have a feedback effect on the microbial community.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Metabolomics in Assessment of Metabolic StatusMetabolomic studies analyze complex systems, including the repertoire of small molecule metabolites in the gut, using high-throughput analytical methods. Mass spectrometry and nuclear magnetic resonance spectroscopy allow robust and sensitive identification of metabolites produced by microbes and host cells in samples such as feces, urine, and tissue (see comprehensive reviews by Dettmer et al19Dettmer K. Aronov P.A. Hammock B.D. Mass spectrometry-based metabolomics.Mass Spectrom Rev. 2007; 26: 51-78Crossref PubMed Scopus (1497) Google Scholar and Slupsky20Slupsky C.M. Nuclear magnetic resonance-based analysis of urine for the rapid etiological diagnosis of pneumonia.Expert Opin Med Diagn. 2011; 5: 63-73Crossref PubMed Scopus (11) Google Scholar). These tools allow researchers to determine the effects that treatments or perturbations have on the host’s metabolic profile by analyzing the presence and quantity of thousands of metabolites simultaneously. Although it is a challenge to assign spectral features, spectral networking platforms,21Rath C.M. Alexandrov T. Higginbottom S.K. et al.Molecular analysis of model gut microbiotas by imaging mass spectrometry and nanodesorption electrospray ionization reveals dietary metabolite transformations.Anal Chem. 2012; 84: 9259-9267Crossref PubMed Scopus (50) Google Scholar, 22Watrous J. Roach P. Alexandrov T. et al.Mass spectral molecular networking of living microbial colonies.Proc Natl Acad Sci U S A. 2012; 109: E1743-E1752Crossref PubMed Scopus (577) Google Scholar aided by open-source metabolome databases such as HMDB,23Wishart D.S. Jewison T. Guo A.C. et al.HMDB 3.0—the Human Metabolome Database in 2013.Nucleic Acids Res. 2013; 41: D801-D817Crossref PubMed Scopus (2225) Google Scholar METLIN,24Smith C.A. O'Maille G. Want E.J. et al.METLIN: a metabolite mass spectral database.Ther Drug Monit. 2005; 27: 747-751Crossref PubMed Scopus (1618) Google Scholar LIPIDS MAPS,25Sud M. Fahy E. Cotter D. et al.LMSD: LIPID MAPS structure database.Nucleic Acids Res. 2007; 35: D527-D532Crossref PubMed Scopus (738) Google Scholar MassBank,26Horai H. Arita M. Kanaya S. et al.MassBank: a public repository for sharing mass spectral data for life sciences.J Mass Spectrom. 2010; 45: 703-714Crossref PubMed Scopus (1331) Google Scholar and NIST,27Stein S.E. Chemical substructure identification by mass spectral library searching.J Am Soc Mass Spectrom. 1995; 6: 644-655Crossref PubMed Scopus (102) Google Scholar allow for faster identification and annotation of known and unknown metabolites.28Yang J.Y. Sanchez L.M. Rath C.M. et al.Molecular networking as a dereplication strategy.J Nat Prod. 2013; 76: 1686-1699Crossref PubMed Scopus (374) Google Scholar By comparing preperturbation and postperturbation metabolomic profiles using multivariate statistics, metabolites that are significantly affected by experimental variables can be identified and placed into the larger context of how the host was affected overall.Effects of the Microbiome on the MetabolomeMetabolomic analyses allow for the metabolism of the gut microbiota to be directly compared with metabolic outcomes in the host. Wikoff et al29Wikoff W.R. Anfora A.T. Liu J. et al.Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites.Proc Natl Acad Sci U S A. 2009; 106: 3698-3703Crossref PubMed Scopus (1731) Google Scholar directly tested the effect of the gut microbiota on the host by comparing the plasma metabolomic profile, obtained via untargeted mass spectrometry, between germ-free and conventionally raised mice. They found that concentrations of more than 10% of all metabolites detected in the plasma differed by at least 50% between mice with and without gut microbes. Furthermore, many metabolites were detected only in serum from conventionally raised mice (not germ-free mice). For example, serum levels of tryptophan decreased 40% in serum from conventional mice compared with germ-free mice, likely because of the presence of bacteria that produce tryptophanases.29Wikoff W.R. Anfora A.T. Liu J. et al.Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites.Proc Natl Acad Sci U S A. 2009; 106: 3698-3703Crossref PubMed Scopus (1731) Google ScholarAnother detailed study evaluated the systemic effects of probiotics, prebiotics, and their combination (termed “synbiotics”) in initially germ-free mice colonized with a combination of microbes representing those found in a human infant (Bacteroides distasonis, Clostridium perfringens, Escherichia coli, Bifidobacterium breve, Bifidobacterium longum, Staphylococcus aureus, and Staphylococcus epidermidis).30Martin F.P. Sprenger N. Yap I.K. et al.Panorganismal gut microbiome-host metabolic crosstalk.J Proteome Res. 2009; 8: 2090-2105Crossref PubMed Scopus (142) Google Scholar Dietary supplementation with the probiotic Lactobacillus rhamnosus NCC4007 and the prebiotic galactosyl-oligosaccharides significantly altered the relative proportions of the 7-member community and led to systemic changes in the metabolic profiles of different tissues from mice. For example, a prebiotic increased proportions of B breve, B longum, and B distasonis; decreased proportions of E coli and C perfringens; and altered lipid metabolism by reducing plasma levels of glucose and hepatic levels of triglycerides. Probiotics also had systemic effects, lowering plasma levels of lipoprotein, hepatic levels of glutamine, and glycogen levels. Overall, prebiotics significantly altered the metabolome in the plasma, urine, feces, liver, pancreas, renal cortex, renal medulla, and adrenal glands; probiotics produced differences in all these compartments except the pancreas.Interestingly, another study that evaluated the effects of probiotics and prebiotics in adults found that neither significantly affected the proportions of microbes in fecal samples, but RNA sequencing data showed altered expression of microbial genes that control carbohydrate metabolism.14McNulty N.P. Yatsunenko T. Hsiao A. et al.The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins.Sci Transl Med. 2011; 3 (106ra106)Crossref PubMed Scopus (404) Google Scholar It is possible that the relatively simpler communities that reside in infants are more susceptible to probiotic and prebiotic manipulation than the more diverse and complex communities found in adults. Prebiotics and probiotics might therefore have the largest effects when administered early in life. However, this hypothesis requires testing in animal models.The dietary components that escape digestion in the upper gastrointestinal tract provide most of the substrates for the intestinal microbiota. Fermentation of carbohydrates by the intestinal microbiota leads to the production of short-chain fatty acids such as butyrate, propionate, and acetate. Studies have shown that patients with inflammatory bowel diseases such as ulcerative colitis have fewer butyrate-producing bacteria (eg, Roseburia hominis and Faecalibacterium prausnitzii) in their intestine, resulting in lower levels of butyrate.31Machiels K. Joossens M. Sabino J. et al.A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis.Gut. 2013 Sep 10; ([Epub ahead of print])Google Scholar, 32Wang W. Chen L. Zhou R. et al.Increased proportion of Bifidobacterium and the Lactobacillus group and loss of butyrate-producing bacteria in inflammatory bowel disease.J Clin Microbiol. 2014; 52: 398-406Crossref PubMed Scopus (274) Google Scholar In addition to butyrate, propionate can potentiate de novo generation of T regulatory cells in the peripheral immune system. Modulation of butyrate- and propionate-producing microbes might therefore be used to treat inflammatory bowel diseases such as ulcerative colitis. However, the anti-inflammatory mechanisms of butyrate and other short-chain fatty acids remain poorly defined.Predictive Microbial MetagenomesMetagenomic information can help determine how metabolism is affected by different disease states linked to dysbiosis. Studies of obesity have shown that subjects with increased adiposity have lower microbial diversity than lean subjects.33Le Chatelier E. Nielsen T. Qin J. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2630) Google Scholar, 34Turnbaugh P.J. Hamady M. Yatsunenko T. et al.A core gut microbiome in obese and lean twins.Nature. 2009; 457: 480-484Crossref PubMed Scopus (5415) Google Scholar The more diverse microbiota of lean subjects contains significantly higher proportions of microbes correlated with anti-inflammatory responses, such as Faecalibacterium prausnitzii. The less diverse microbiota of obese subjects contains higher proportions of Bacteroides species and Ruminococcus gnavus, each of which could have inflammatory effects.33Le Chatelier E. Nielsen T. Qin J. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2630) Google Scholar Gene content analysis of these groups revealed that the less diverse microbiota appeared to produce lower levels of butyrate, have increased potential for production of hydrogen sulfide, and have reduced capability for management of oxidative stress. One poorly understood aspect of the microbiome, and its potential to produce a variety of metabolites, is whether microbial diversity itself has protective effects for the host or whether low diversity is a side effect of specific disorders (rather than a cause).35Fang S. Evans R.M. Microbiology: wealth management in the gut.Nature. 2013; 500: 538-539Crossref PubMed Scopus (30) Google Scholar This relationship can best be resolved in humans by prospective longitudinal studies.Although it would be ideal to obtain metabolomic and metagenomic data for every sample for which a 16S amplicon profile has been collected, these techniques are currently far more expensive than 16S amplicon profiling. Fully matched data sets are therefore prohibitively expensive and time consuming to produce. However, recent advances in software, including Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt),36Langille M.G. Zaneveld J. Caporaso J.G. et al.Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.Nat Biotechnol. 2013; 31: 814-821Crossref PubMed Scopus (5538) Google Scholar that exploit the strong association between phylogeny and function now allow researchers to estimate the metabolomic functional profile of a community using 16S amplicon sequences. Briefly, PICRUSt takes a phylogenetic tree in which the gene profile of a subset of nodes is known and then uses ancestral state reconstruction to estimate the functional gene content for other uncharacterized nodes. PICRUSt was able to make strong predictions (average Spearman r = 0.82) for inferred metagenomes from 16S marker genes compared with fully sequenced metagenomes obtained from the Human Microbiome Project.Another powerful computational tool is Predicted Relative Metabolic Turnover, which uses gene counts to predict the relative consumption and production of metabolites in a system; it can be used for modeling and hypothesis generation.37Larsen N. Vogensen F.K. van den Berg F.W. et al.Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults.PLoS One. 2010; 5: e9085Crossref PubMed Scopus (1885) Google Scholar Tools such as PICRUSt and Predicted Relative Metabolic Turnover could be cost-effective methods to determine whether additional resources should be used for more comprehensive metabolic profiling and metagenomic sequencing. However, findings must be validated with matched data sets to assess the limits of their performance.Metabolomic Profiles of InfantsChanges to the microbiome and immune system during infancy may have lasting effects, such as contributing to the development of allergies.14McNulty N.P. Yatsunenko T. Hsiao A. et al.The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins.Sci Transl Med. 2011; 3 (106ra106)Crossref PubMed Scopus (404) Google Scholar, 38Heederik D. von Mutius E. Does diversity of environmental microbial exposure matter for the occurrence of allergy and asthma?.J Allergy Clin Immunol. 2012; 130: 44-50Abstract Full Text Full Text PDF PubMed Scopus (133) Google Scholar, 39Blaser M. Antibiotic overuse: Stop the killing of beneficial bacteria.Nature. 2011; 476: 393-394Crossref PubMed Scopus (278) Google Scholar Distinct changes in the microbiota occur during the first 2 years of life and correlate with changes in environment and diet; these can be tracked by studying changes in infants’ fecal metabolomes. A study of infants at risk for celiac disease showed that the metabolomes of infants younger than 6 months of age were dominated by sugars, including lactose and glucose. However, after 6 months, their metabolomes shifted, increasing concentrations of amino acids and short-chain fatty acids. Principal coordinates analysis showed that the metabolome of children at 2 years of age resembles more closely that of adults because of increased levels of acetate and butyrate.40Sellitto M. Bai G. Serena G. et al.Proof of concept of microbiome-metabolome analysis and delayed gluten exposure on celiac disease autoimmunity in genetically at-risk infants.PLoS One. 2012; 7: e33387Crossref PubMed Scopus (179) Google Scholar These findings are supported by 16S amplicon studies showing that the infant microbiota comes to resemble that of adults from the same community at 2 years of age.41Yatsunenko T. Rey F.E. Manary M.J. et al.Human gut microbiome viewed across age and geography.Nature. 2012; 486: 222-227PubMed Google Scholar It is also apparent that the intestinal microbiota of infants is specifically adapted to metabolize the infant’s earliest nutrient source: breast milk. Specific Bifidobacterium species have genomes enriched in genes that regulate the processing of human milk–derived oligosaccharides. These might have a competitive advantage that places them among the first colonizers of the human intestine.42Sela D.A. Chapman J. Adeuya A. et al.The genome sequence of Bifidobacterium longum subsp. infantis reveals adaptations for milk utilization within the infant microbiome.Proc Natl Acad Sci U S A. 2008; 105: 18964-18969Crossref PubMed Scopus (616) Google ScholarXenobiotic MetabolismIn addition to diet-derived macronutrients, the microbes residing in the gastrointestinal tract may be exposed to a variety of xenobiotic compounds (antibiotics, other drugs, and diet-derived bioactive compounds). Because the gut microbiome encodes so many enzymes with different activities, it is not surprising that many of the xenobiotic compounds are often metabolized by the gut microbiota. It has been at least 40 years since we began to appreciate the contribution of microbes to xenobiotic metabolism.43Scheline R.R. Metabolism of foreign compounds by gastrointestinal microorganisms.Pharmacol Rev. 1973; 25: 451-523PubMed Google Scholar, 44Goldman P. Peppercorn M.A. Goldin B.R. Metabolism of drugs by microorganisms in the intestine.Am J Clin Nutr. 1974; 27: 1348-1355PubMed Google Scholar, 45Goldman P. Biochemical pharmacology of the intestinal flora.Annu Rev Pharmacol Toxicol. 1978; 18: 523-539Crossref PubMed Scopus (63) Google Scholar However, we are only beginning to uncover the mechanisms of this process. Adding to the complexity of these interactions, xenobiotics can also modulate the expression and activity of the gut microbiome.13Maurice C.F. Haiser H.J. Turnbaugh P.J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome.Cell. 2013; 152: 39-50Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar Metabolites of microbial origin may interfere with host metabolism of xenobiotics, and diet-derived nutrients can regulate microbial metabolism of xenobiotics.One of the first studies to provide detailed evidence for the interaction between the gut microbiota and metabolism of xenobiotics was from Clayton et al in 2009.46Clayton T.A. Baker D. Lindon J.C. et al.Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism.Proc Natl Acad Sci U S A. 2009; 106: 14728-14733Crossref PubMed Scopus (563) Google Scholar Their study leveraged a powerful metabolomic analysis pipeline to correlate the presence of the microbial metabolite p-cresol with a reduction in the ratio of sulfonated to glucuronidated acetaminoph