Metabolomics: An Essential Tool to Understand the Function of
Peroxisome Proliferator–Activated Receptor Alpha
JESSICA E. MONTANEZ1, JEFFREY M. PETERS1, JARED B. CORRELL1, FRANK J. GONZALEZ2, AND ANDREW D. PATTERSON1
1Department of Veterinary and Biomedical Sciences and The Center for Molecular Toxicology and Carcinogenesis, The
Pennsylvania State University, University Park, Pennsylvania, USA
2Laboratory of Metabolism, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
The peroxisome proliferator–activated receptor (PPAR) family of nuclear hormone transcription factors (PPARa, PPARb/d, and PPARg) is
regulated by a wide array of ligands including natural and synthetic chemicals. PPARs have important roles in control of energy metabolism and are
known to influence inflammation, differentiation, carcinogenesis, and chemical toxicity. As such, PPARs have been targeted as therapy for common
disorders such as cancer, metabolic syndrome, obesity, and diabetes. The recent application of metabolomics, or the global, unbiased measurement
of small molecules found in biofluids, or extracts from cells, tissues, or organisms, has advanced our understanding of the varied and important roles
that the PPARs have in normal physiology as well as in pathophysiological processes. Continued development and refinement of analytical platforms,
and the application ofnew bioinformatics strategies,haveaccelerated the widespread use ofmetabolomics and have allowed further integration ofsmall
molecules into systems biology. Recent studies using metabolomicsto understand PPARa function, as well as to identify PPARa biomarkers associated
with drug efficacy/toxicity and drug-induced liver injury, will be discussed.
metabolomics; liver; PPARa; chromatography; mass spectrometry.
The peroxisome proliferator–activated receptors (PPARs) are
ligand-activated transcription factors belonging to the nuclear
receptor (NR) superfamily. Structurally, PPARs (a, b/d, and g)
are highly conserved sharing similar domains with other NRs;
with a ligand-independent and a ligand-dependent transcrip-
tional activation function (AF-1 and AF-2, respectively) that
sandwiches the zinc-finger DNA-binding domain, and a
ligand-binding domain (Peters, Shah, and Gonzalez 2012; Fran-
cis et al. 2003). Based on amino acid sequence similarity, the
PPARs belong to group one of the six-member NR superfamily.
More generically, the PPARs are considered part of the
metabolic sensors of the NR superfamily that are distinct from
the steroid and orphan receptors (Desvergne, Michalik, and
Wahli 2006). Given its important and documented role in meta-
bolism and disease, this review focuses predominantly on
PPARa. However, there have been fundamental metabolomics
studies (albeit few compared to PPARa) with respect to
PPARb/d (Roberts et al. 2011; Patterson and Peters 2011) and
PPARg (Watkins et al. 2002; van Doorn et al. 2007), and it is
anticipated that metabolomics will have a similarly significant
impact on understanding the function of these receptors.
PPARa was the first of the PPARs to be discovered and is so
named because it was shown to mediate the proliferation of per-
oxisomesinhepatic tissueofrodents followingadministrationof
the hypolipidemic drug clofibrate (Issemann and Green 1990).
PPARa is expressed mainly in the liver, but is also expressed
in the kidney, heart, skeletal muscle, and brown adipose tissue
(Braissant et al. 1996). Endogenous ligands that bind with the
highest affinity to PPARa are saturated/unsaturated fatty acids,
leukotriene derivatives, and VLDL hydrolysis products. Exam-
ples of synthetic ligands that bind PPARa are the fibrate class
of hypolipidemic drugs, the experimental ligand Wy-14,643
([4-chloro-6-(2,3-xylidino)-2-pyrimidinylthio] acetic acid) as
well as some phthalate monoesters (monoethylhexyl phthalate),
Cattley 1998). PPARa is a major regulator of the mitochondrial
and peroxisomal b-oxidation pathway, and as discussed below,
these pathways have been implicated in the pathogenesis of var-
ious liver complications.
The author(s) declared no potential conflicts of interest with respect to the
research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: ES022186 (A.D.P),
CA124533 (J.M.P), CA141029 (J.M.P), CA140369 (J.M.P), and the NIH
Intramural Research Program (F.J.G.).
Address correspondence to: Andrew D. Patterson, Department of
Veterinary and Biomedical Sciences and The Center for Molecular
Toxicology and Carcinogenesis, The Pennsylvania State University, 322 Life
Sciences Building, UniversityPark,
Abbreviations: ALD, alcoholic liver disease; APAP, acetaminophen;
GC-MS, gas chromatography coupled with mass spectrometry; LC-MS,
liquid chromatography coupledwith
matrix-assisted laser desorption ionization; MDA, multivariate data analysis;
NAPQI, N-acetyl-p-benzoquinone imine; NMR, nuclear magnetic resonance;
NR, nuclear receptor; PPAR, peroxisome proliferator–activated receptor; ROS,
reactive oxygen species; UCP2, uncoupling protein 2; UHPLC-ESI-QTOFMS,
ionization quadrupole time-of-flight mass spectrometry.
Toxicologic Pathology, 41: 410-418, 2013
Copyright # 2012 by The Author(s)
ISSN: 0192-6233 print / 1533-1601 online
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THE METABOLOME AND METABOLOMICS
metabolome (Table1) may in factbe the most accurate indicator
of cellular physiology (Idle and Gonzalez 2007). The metabo-
lome represents the complete set of small molecules found in
biofluids (blood, plasma, serum, urine, sweat, saliva) and, unlike
the genome, only a small fraction of the metabolome has been
annotated. However, efforts as part of the Human Metabolome
Database and the Human Serum Metabolome in Health and
Disease initiatives have begun to systematically identify and
describe metabolites found in various biofluids (Cottingham
logical information (compared with the genome, transcriptome,
and proteome), appreciation for the complexity and richness of
the metabolome has grown. In 2007, the initial ‘‘draft’’ of the
human metabolome containing 2,500 metabolites was reported
(Wishart et al. 2007); however, in just over 5 years, the Human
Metabolome Database (HMDB) now contains nearly 8,000
metabolites. Further, given that discrete chemical exposures in
humans are thought to be on the order of 2 to 3 million in a
lifetime (Idle and Gonzalez 2007), metabolomics will also be
important for capturing information regarding exposure to
xenobiotics. This is particularly relevant for human studies
where metabolomics approaches are likely to capture not only
endogenous but also xenobiotics and their metabolites (Johnson
et al. 2012; Patterson, Gonzalez, and Idle 2010).
While the precise definition of metabolomics varies
throughout the literature, it can be simply defined as the global,
unbiased measurement of small molecules found in biofluids,
or extracts from cells, tissues, or organisms (Griffin and
Nicholls 2006). In more general terms, metabolomics can be
described as the chemical fingerprints left behind by endogen-
ous biological processes or the biological activity on chemicals
derived from the diet and/or environment (Daviss 2005).
However defined, metabolomics has already provided unprece-
dented views of PPAR biology and function yielding important
insights and validation of their roles in health and disease.
Metabolomics approaches have been described in great detail
elsewhere (Dettmer, Aronov, and Hammock 2007; Patterson
et al. 2010). Briefly, the process (Figure 1) involves extraction
of metabolites from a biofluid (urine, serum, plasma), data
acquisition using a variety of platforms including
UPLC-ESI-QTOFMS, or GC-MS, followed by peak alignment
and normalization using a variety of commercial (Waters Mar-
kerLynx, AB SCIEX MarkerView) and public (XCMS,
MZMine) software tools (Smith et al. 2006; Tautenhahn et al.
2012; Eliasson et al. 2012; Katajamaa, Miettinen, and Oresic
2006; Pluskal et al. 2010), and ultimately multivariate data anal-
ysis (MDA) to identify a metabolite or metabolites that can dis-
tinguish one sample group from the other. It is important to note
that despite many technological advances in chromatographic
separations (GC, LC) and detection platforms (1H-NMR, MS),
there does not yet exist a single platform that can capture all
metabolites as say a microarray chip would for gene expression.
Therefore, a combination of approaches (1H-NMR, LC-MS,
GC-MS) is necessary to increase coverage of the metabolome.
TABLE 1.—Glossary of commonly used terms.
MetabolomeThe collection of all small molecules found in biofluids, or extracts from cells, tissues, or organisms. The metabolome is
highly context (e.g., serum vs. plasma) and analytical platform (GC-MS vs. LC-MS)-dependent.
The global, unbiased measurement of small molecules found in biofluids, or extracts from cells, tissues, or organisms
(Griffin and Nicholls 2006). In more general terms, metabolomics can be defined as the chemical fingerprints left
behind by endogenous biological processes or the biological activity on chemicals derived from the diet and/or
environment (Daviss 2005).
A data dimension reduction technique permitting visualization of the variance existing in a data set. This technique is
widely used in metabolomics studies (Patterson, Lanz, et al.2010b). Variations include supervised approaches such as
projection to latent structures (or partial least squares) discriminant analysis (PLS-DA) and orthogonal projection to
latent structures (OPLS). Scaling of the data (Pareto being perhaps the most common) is important such that lower
abundance metabolites have equal weight compared to larger abundance metabolites.
A machine learning algorithm that uses a collection of simple decision trees to produce a highly accurate classifier. It is
useful for metabolomics data analysis as it handles high dimension data well and does not require scaling of the data (an
important point considering metabolite concentrations can range several orders of magnitude). It is thought to be less
prone to overfitting (i.e., modeling noise) compared to other data modeling approaches.
A liquid chromatography method based on sub 2 mm particles that permits shorter run times (without sacrificing
separation quality) and thus achieving greater sample throughput. The term ultraperformance liquid chromatography is
a term coined by Waters, Corp. (Milford, MA) that is often used interchangeably with UHPLC.
A common analytical technique used in metabolomics analysis that measures the mass-to-charge (m/z) ratio of charged
(gain or loss) molecules. Time-of-flight (TOF) mass spectrometers are most often used in metabolomic studies for their
accurate mass capabilities. Variations include quadrupole TOFs (QTOF) that permit selection of ions for subsequent
fragmentation (MS/MS) to aid in structural elucidation. Triplequadrupole mass spectrometers are often used for tar-
geted metabolite profiling studies using a technique known as multiple reaction monitoring (MRM).
An incredibly powerful separation technique for mixtures in the gaseous state. For metabolomic applications, samples
must be extracted and derivatized such that metabolites become volatile, less polar, and thermally stabile at high
Principal component analysis (PCA)
Ultra-high pressure liquid chromato-
Mass spectrometry (MS)
Gas chromatography (GC)
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