Xenobiotic Metabolomics: Major Impact on the Metabolome

Caroline H. Johnson1, Andrew D. Patterson2, Jeffrey R. Idle3, and Frank J. Gonzalez1

1Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892; [email protected], [email protected]

2Department of Veterinary and Biomedical Sciences and The Center for Molecular Toxicology and Carcinogenesis, The Pennsylvania State University, University Park, Pennsylvania 16802; [email protected]

3Hepatology Research Group, Department of Clinical Research, University of Bern, 3010 Bern, Switzerland; [email protected]

Abstract

Xenobiotics are encountered by humans on a daily basis and include drugs, environmental

pollutants, cosmetics, and even components of the diet. These chemicals undergo metabolism and

detoxication to produce numerous metabolites, some of which have the potential to cause

unintended effects such as toxicity. They can also block the action of enzymes or receptors used

for endogenous metabolism or affect the efficacy and/or bioavailability of a coadministered drug.

Therefore, it is essential to determine the full metabolic effects that these chemicals have on the

body. Metabolomics, the comprehensive analysis of small molecules in a biofluid, can reveal

biologically relevant perturbations that result from xenobiotic exposure. This review discusses the

impact that genetic, environmental, and gut microflora variation has on the metabolome, and how

these variables may interact, positively and negatively, with xenobiotic metabolism.

Keywords

pharmacometabolomics; gut microflora; interindividual variation; metabotype; UPLC; mass spectrometry

INTRODUCTION

Xenobiotics are foreign compounds that include not only drugs but also environmental

pollutants, dietary supplements, and food additives. Human exposure to xenobiotics is

pervasive; in a human lifetime, one might be exposed to 1–3 million xenobiotics (1). These

compounds can be toxic or harmless, but nonetheless they are treated by the body as foreign.

They are metabolized and ultimately eliminated through the urine, bile, and feces.

Xenobiotics can be eliminated unchanged, but the vast majority utilize endogenous

DISCLOSURE STATEMENT

The authors have no affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

HHS Public Access Author manuscript Annu Rev Pharmacol Toxicol. Author manuscript; available in PMC 2018 December 20.

Published in final edited form as: Annu Rev Pharmacol Toxicol. 2012 ; 52: 37–56. doi:10.1146/annurev-pharmtox-010611-134748.

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mechanisms such as enzymatic functionalization and/or conjugation reactions that facilitate

their elimination, and they use processes that are also involved in the metabolism and

transport of endogenous compounds such as bilirubin, lipids, and steroids. Thus, it is

important to have a comprehensive knowledge of in vivo xenobiotic metabolism so that

potential problems such as the generation of reactive metabolites or bioavailability issues

when coadministering a drug can be ascertained. Metabolomics—the unbiased global survey

of low-molecular-weight molecules or metabolites in a biofluid, cell, tissue, organ, or

organism— represents an ideal solution for understanding and measuring the impact of

xenobiotic exposure on a biological system. The term metabolome was first used in 1998 (2)

and has been defined since as “the set of metabolites synthesized by a biological system” (3,

p. 155); it encompasses all the small metabolites present in a particular biofluid (urine,

blood, sebum, cerebral spinal fluid, saliva), cell, or tissue. As metabolites are the ultimate

downstream products of genomic, transcriptomic, and/or proteomic perturbations, changes

in metabolite concentration and/or flux can reveal biologically relevant changes to the

system.

MANIPULATION OF THE METABOLOME

Genetic and Environmental Influences on the Metabolome

The metabolome can vary among individuals owing to numerous genetic and environmental

factors. Environmental influences include diet, stress, medication, lifestyle, and disease.

Genetic variation includes gender, epigenetics, and polymorphisms in genes encoding

xenobiotic-metabolizing components such as Phase I and II enzymes, transporters, receptors,

and ion channels. Age is also another host factor that can have physiological effects and thus

affect xenobiotic metabolism and elimination. The gut microflora or microbiome of an

individual represents yet another source of extragenomic variation. The combination of all

these factors contributes to interindividual differences, but the interplay between genetic

variation and environmental exposure can further confound results. For example,

environmental exposures and disease can induce epigenetic changes (DNA methylation,

histone modification) that potentially affect drug-metabolizing enzyme activity and capacity;

these effects, in turn, can influence the efficacy and toxicity of a drug among individuals (4).

Genetic Variation

Genetic variation, although a major factor in defining the metabolomes of various

populations, can also be masked by environmental influences. Recent large-scale human

population studies have illustrated how genetic and environmental differences can impact the

metabolome. Twenty-four-hour urine samples collected from 17 distinct populations in

Japan, China, the United States, and the United Kingdom were analyzed by nuclear

magnetic resonance (NMR) spectroscopy-based metabolomics, and the analysis revealed

that geographic differences were a stronger influence than that of gender. Environmental

pressure was also seen among the metabolic phenotypes of Japanese living in Japan and

Japanese living in the United States. These two populations were well differentiated even

though they were genetically similar, whereas the populations from the United States and the

United Kingdom had similar metabolomes (5). Therefore, environmental factors such as

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lifestyle and diet have large effects on the metabolome and may even overshadow genetic

inputs.

Genetic polymorphisms in key drug-metabolizing enzymes can influence the route of

metabolism, and ultimately the bioavailability, efficacy (in the case of drugs), and toxicity of

xenobiotics. Phase I metabolizing enzymes such as the cytochrome P450 (CYP) superfamily

add functional groups that allow for direct excretion or the addition of conjugates that render

the compounds more hydrophilic. CYPs are regulated by nuclear receptors, including

pregnane X receptor (PXR), constitutive androstane receptor (CAR), peroxisome

proliferator-activated receptor α (PPARα), and the aryl hydrocarbon receptor (AHR). There

is a wide range of variation in the expression of the CYP enzymes and nuclear receptors

among individuals; this is due not only to genetic polymorphisms but also to differences that

result from age, gender (progesterone can induce CYP3A4 in women), body weight, and

disease (liver diseases in particular can affect the capacity of a drug-metabolizing enzyme).

The Phase II conjugating enzymes uridine 5’-diphospho-glucuronosyltransferases (UGTs),

sulfotransferases, N-acetyltransferases, and glutathione 5-transferases are also subject to

genetic polymorphisms, some of which cause debilitating diseases. For example, a UGT

polymorphism involving the UGT1A1*28 allele has been linked to Gilbert’s syndrome (6),

in which UGT1A1 has much lower activity, and subjects may develop hyperbilirubinemia

owing to lack of conjugation and elimination of bilirubin. Xenobiotic metabolism can also

be affected by other chemicals in tobacco smoke (7), alcohol (8), and industrial pollutants

[2,3,7,8-tetrachlorodibenzo-p-dioxin activates AHR (9)], and, of course, by coadministration

of pharmaceutical drugs. St. John’s Wort, a dietary supplement for the treatment of mild

depression, is an agonist for PXR, which induces the expression of CYP3A4. Thus, when St.

John’s Wort is coadministered with other drugs such as digoxin (10) and oral contraceptives

(estrogen and progestin) (11), a marked decrease in the plasma concentrations of these drugs

is seen, resulting in lower efficacy. Another interference can come from dietary grapefruit

ingestion, which inhibits drug-metabolizing enzymes such as CYP3A4 and drug transporters

(12). Grapefruit-drug interactions have been seen with antihypertensives, antimicrobials,

benzodiazepines, antihistamines, statins, and chemotherapeutics (13). Therefore, it is

important to establish the exact metabolic pathway and mechanisms of these xenobiotics to

determine the metabolites produced and their effects on the metabolome.

The Microbiome and Metabolome

The metabolome of an organism is also influenced by the symbiotic gut microflora or

microbiome. The metabolome of an individual can contain metabolites that are formed

through the actions of the gut microbiota, and metabolism by these microbes may directly

affect the metabolome of the host. Hippurate and phenylacetylglycine, for example, are seen

in the urinary metabolome and are formed from the microbial breakdown of larger dietary

phenols and phenylalanine, respectively. They generally reflect small disturbances to the

host’s environmental conditions (14). The gut microflora have been associated with diseases

such as inflammatory bowel disease (15), obesity (16), and diabetes (17). In humans, gut

microflora influence immunity and anaerobic metabolism of peptides and proteins, are a

defense against pathogens, and influence the development of intestinal microvilli of the

organism (18).

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Most importantly, with respect to metabolomics, gut microflora are involved in the

metabolism of xenobiotics. Dehydroxylation, decarboxylation, dealkylation, dehalogenation,

and deamination reactions have been reported as gut microflora–mediated reactions (19).

They can influence the xenobiotic metabolite pool among individuals, and this influence

may have major consequences for toxicity. Gut microflora stability itself can be affected by

xenobiotics, in particular digoxin (20), which increases susceptibility to enteric infections

(21). Antibiotic treatment in particular disturbs gut microflora equilibrium and affects many

metabolic pathways, such as those involved in bile acid synthesis and steroid metabolism

(22, 23). Other xenobiotics including those in dark chocolate (21), pomegranate by-products

(24), and probiotics (25) have also been shown to modulate the gut microflora environment.

Phase I and II xenobiotic metabolism is influenced by the gut microflora. p-Cresol sulfate,

phenyl sulfate, and indoxyl sulfate are bacterial metabolites of tyrosine and have been

observed to be elevated by the action of gut microbes (26, 27). Given that sulfation is a key

element of Phase II drug metabolism, this also has implications for xenobiotic elimination.

Furthermore, some microbial species can produce xenobiotics, requiring further metabolism

by the host by CYP enzymes (19). Lhoste et al. (28) reported an example of this in which

germ-free and human microflora–inoculated rats had different levels of UGT, glutathione 5-

transferase, and CYP2C11 enzyme induction when administered catechins. Xenobiotic

metabolism in germ-free or conventionally raised mice also showed different metabolism of

barbiturates owing to gut microflora-influenced liver expression of CAR and PXR (29).

Considering the degree of contribution of the microbiome to the metabolome and the effects

of genetic and environmental stimuli on both, gut flora metabolism adds a further dimension

of complexity to the host’s overall metabolome and an extra source of interindividual

variation.

The concept of a metabotype encompasses all the genetic, environmental, and gut microflora

modifications that are not necessarily readily observable, and it gives each individual a

defining metabolomic fingerprint. The metabotype idea was first conceived and defined as

“a probabilistic multiparametric description of an organism in a given physiological state

based on analysis of its cell types, biofluids or tissues” (30, p. 173). As outlined in Figure 1,

genetic and environmental factors can affect each other and give rise to interindividual

variation and thus a unique metabotype. If one wishes to observe the effect of a specific

intervention on an organism, the metabotype is an important consideration, in particular

during drug development and in defining the drug’s metabolic fate. Patient stratification in

clinical trials may start to rely more on metabotypes so that a population of responders/

nonresponders can be defined; this definition could result in greater success in drug

development by simultaneously considering environmental as well as genomic factors.

Analysis of the Metabolome

Metabolomics was developed to identify and quantitate perturbations in the metabolome

caused by genetic or environmental pressures. The analytical platforms used for

metabolomics have been discussed extensively elsewhere (31–33). In brief, they include

NMR spectroscopy and mass spectrometry (MS) coupled to chemometric or multivariate

data analysis. No single platform can capture the whole metabolome owing to the different

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physical properties of metabolites, so the sample type and chemical constituents to be

measured determine which system is optimal. Numerous NMR spectroscopic methods have

been applied in metabolomics analysis, including magic-angle spinning (34); pulse

sequences to optimize metabolite recovery, such as the Carr- Purcell-Meiboom-Gill spin

echo sequence, which attenuates broad protein and lipoprotein signals (35); and the use of

various nuclides such as 1H, 19F, 13C, and 31P. For sample introduction onto the mass

spectrometer, the instrument can be connected to gas chromatography (GC), capillary

electrophoresis (CE), or liquid chromatography (LC) systems. Ultraperformance liquid

chromatography (UPLC) is the LC system of choice, preferred over the standard high-

performance liquid chromatography (HPLC) system. When combined with orthogonal

quadrupole time-of-flight (QTOF) MS, UPLC provides the advantage of high peak

resolution with a lower limit of detection for ions and accurate mass determination (32).

Recent advances in GC-MS technology for metabolomics analysis include the GCxGC-

TOF-MS system, which allows for a much more complex sample analysis that can detect

thousands more peaks. It uses two orthogonal separation phases, expanding the

chromatographic plane and thus creating additional peak capacity in which peaks can be

resolved. This setup enhances resolution and reduces the problem of coeluting peaks (36).

Accurate quantitation can then be carried out by triple-quadrupole MS through multiple

reaction monitoring to verify the concentration of the biomarker in each sample.

The most common chemometric techniques for data analysis include dimension-reduction

methods such as principal components analysis, projection to latent structures discriminant

analysis (PLS-DA), and orthogonal projection to latent structures. These methods are useful

for revealing any systematic variation in the data and for finding patterns or groupings. As a

complementary approach, the machine-learning algorithm Random Forests has been

implemented in some metabolomics studies (37–40). This method is particularly superior for

handling high-dimension data and provides a robust measurement of misclassification error

(32). Another advancement in data analysis tools for metabolomics is the release of XCMS

Online (https://xcmsonline.scripps.edu), which is a user-friendly program allowing the

processing and analyzing of MS data. New innovative technologies and data processing

techniques as well as enhancements to databases and data analysis methods are constantly

under development to further optimize metabolomics as a powerful and essential analytical

technique that can be applied in most academic settings. In addition, The Human

Metabolome Database (http://www.hmdb.ca) and the METLIN Metabolite Database (http://

metlin.scripps.edu) are of great value for interpretation of metabolomics data and metabolite

identification.

APPLICATIONS OF METABOLOMICS IN XENOBIOTIC STUDIES

Knowing the metabolic fate of a xenobiotic will greatly aid in understanding its potential

toxicity and also its mechanism of toxicity. Global metabolomics approaches can determine

changes to metabolic pathways that may not be seen through normal, targeted biochemical

assays or may not be present owing to the time delay from gene product to metabolic

product. Multivariate data analysis and LC-MS were first combined for detection of

xenobiotic metabolites by Plumb et al. (41). Since then, UPLC-MS-based metabolomics in

particular have been applied successfully to numerous xenobiotic studies (see Table 1) and

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have revealed novel metabolites and pathways (42–52). Many of these novel metabolites

were discovered for xenobiotics that are used by a large percentage of the population. Thus,

metabolomics has expanded the knowledge surrounding these xenobiotics and led the way to

understanding their metabolism, side effects, and possible health consequences. A good

example of the power of using metabolomics for xenobiotic research involves

acetaminophen (APAP) metabolism. Although this over-the-counter analgesic has been

available for more than 50 years, three new metabolites of APAP—S-(5-acetylamino-2-

hydroxyphenyl)mercaptopyruvic acid; 3,3′-biacetaminophen; and a benzothiazine

compound—were recently discovered, which was surprising considering the wealth of

knowledge surrounding APAP and its metabolism (46). Recently published studies discussed

below further demonstrate the value and power of UPLC-MS-based metabolomics for

xenobiotic and toxicology research.

Cyclophosphamide and Ifosfamide

Cyclophosphamide (CP) and ifosfamide (IF) are isomeric prodrugs used in cancer

chemotherapy. Both drugs undergo complex Phase I and II metabolism to numerous

metabolites. However, treatment with IF is known to cause nephrotoxicity and neurotoxicity,

whereas CP treatment does not. Selective IF toxicity is thought to result from the production

of 2-chloroacetaldehyde. The latter is converted to 2-chloroacetic acid (CAA), which can

react with cellular thiols to produce S-carboxymethylcysteine (SCMC) and thiodiglycolic

acid (TDGA). Although it is possible that SCMC and TDGA can induce encephalopathy and

mitochondrial dysfunction via IF dosing, there are no reports to suggest CP toxicity from

SCMC and TDGA production.

UPLC-ESI-QTOFMS-based metabolomics (i.e., metabolomics based on ultraperformance

liquid chromatography–electrospray ionization–quadrupole time-of-flight mass

spectrometry) was thus employed to perform a comprehensive comparative analysis of IF

and CP metabolism. This was to determine whether IF or CP produced additional

metabolites that could contribute to the observed pathologies. Twenty-four-hour urine

samples were collected and analyzed from C57BL/6 mice dosed with IF (50 mg kg–1) or CP

(50 mg kg–1) (43). Multivariate data analysis, specifically orthogonal projection to latent

structures models, revealed 12 IF and 11 CP urinary metabolites, five of which were novel.

A range of metabolic reactions produced the 23 metabolites, including dechloroethylation,

hydroxylation, ketonization, dehydroxylation, alkylation, ring-opening, and conjugation

reactions (Figure 2). Metabolomics revealed that one of the differences observed between

the two prodrugs was increased excretion of CP ring-opened and ketonized metabolites

compared with IF. In addition, the dechloroethylation reaction produced higher

concentrations of IF metabolites than CP metabolites (twofold). CAA was also produced

from the dechloroethylation reactions, which, in turn, produced SCMC and TDGA. SCMC

and TDGA excretion was quantitated by triple-quadrupole MS, revealing that SCMC urinary

excretion increased 32-fold and 44-fold above endogenous levels after administration of IF

and CP. TDGA urinary excretion was increased by 14-fold and 17-fold after treatment with

IF and CP, respectively. There were no significant differences in SCMC and TDGA

excretion between the two prodrugs, which verified that the SCMC and TDGA metabolites

did not confer toxicity with CP administration.

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Hence, the results from this study (43) instead suggested that the toxic nature of IF could

actually be derived from the CAA metabolite itself. The relative excretion of the

dechloroethylated metabolites was much greater from IF dosing compared with CP dosing,

signifying that CAA was produced in higher quantities upon IF administration, but because

of the unstable nature of CAA, it was not quantified. As there was no significant difference

between IF and CP with regard to SCMC and TDGA production, another mechanism of

reaction may exist. Indeed, others observed a decrease in IF-induced nephropathy and

glutathione depletion when IF was combined with N-acetylcysteine administration (53). In

theory, this experiment would have produced N-acetyl SCMC, which would have blocked

the production of TDGA, thus implying that TDGA is in fact the toxic metabolite. Another

mechanism of IF toxicity could have resulted from favorable glutathione versus cysteine

conjugation of CAA, which may have led to glutathione depletion. This then directs further

studies on the differential metabolism and toxicity of the prodrugs to focus on the potential

of CAA as a contributing toxicity factor.

Fenofibrate

Fibrate drugs are used for treatment of dyslipidemia resulting from increasing fatty acid β-

oxidation; lower serum triglycerides result in reduced insulin resistance (54). Fenofibrate is

well tolerated, but some adverse effects have been observed in rodent model systems,

predominantly increased oxidative stress and myotoxicity (55–57). In humans, fenofibrate

increases serum creatinine levels (58) and is associated with renal disorders (59); these

scenarios are infrequent, but the toxicology of fenofibrate in humans is a concern. Fibrates

are agonists of the nuclear receptor PPARα that control expression of genes involved in lipid

oxidation, gluconeogenesis, and amino acid metabolism (60, 61). Chronic dosing of fibrates

to rats, which activates PPARα, can result in hepatotoxicity and hepatocarcinogenesis, but

the same is not seen in humans and nonhuman primates (61, 62). Fibrate metabolites may

therefore contribute to the toxicity seen in rats. Three comprehensive UPLC-ESI-QTOFMS-

based metabolomics studies were carried out to ascertain the full metabolic map of

fenofibrate in different species. Previously, fenofibrate metabolites were reported in rats,

guinea pigs, dogs, and humans. Fenofibric acid (FA) and reduced fenofibric acid (RFA) were

seen in all species (63, 64), whereas fenofibric acid ester glucuronide (FAEG) and reduced

fenofibric acid ester glucuronide (RFAEG) were seen in all species except dog (65). The

metabolomics studies analyzed fenofibrate metabolism in cynomolgus monkeys