MEDICAL
PROGRESS
Metabolomics: A New Frontier for Research in Pediatrics
SILVIA CARRARO, MD, GIUSEPPE GIORDANO, PHD, FABIANO RENIERO, PHD, GIORGIO PERILONGO, MD, AND EUGENIO BARALDI, MD
SYSTEMS BIOLOGY AND `OMIC' SCIENCES
T
he study of molecular biology has traditionally been based on a reductionist approach: cells are separated into their
components, which are further separated into smaller components (genes, RNA, proteins) to be studied.1
This approach
is fundamental to describing the components of a biological system one by one.
The need has emerged in recent years to move toward the comprehension of the system as a whole (systems biology
approach) to understand not only the functioning of the individual components but also how a system works altogether.1
Systems
biology is defined as the quantitative analysis of the dynamic interaction between several components of a biological system
through the combination of mathematical modeling and experimental biology.1
Systems biology considers the interactions between DNA, RNA, proteins, and metabolites and studies the network of
relationships in which they are involved, characterizing the flow of information at each level of biomolecular organization.2
This
approach views the organism as a whole picture, leading to an integrated comprehension of its functioning in health and disease.
The strength of systems biology lies in a comprehensive investigation of the "omic cascade," with a combination of
transcriptomics, proteomics, and metabolomics (Table), in which these "-omic" terms have been formulated to define approaches
capable of describing the entire set of proteins, mRNAs, and metabolites in a given organism.2,3
Transcriptomics is defined as the study of gene expression. Transcriptome is the term used to describe the full set of mRNA
present in a cell or tissue at any one time. Similarly, the study of protein translation is called proteomics, and a proteome is the
complete set of proteins expressed in a cell or tissue at any one time. Transcriptomics and proteomics are therefore functional
analysis at mRNA and protein level, respectively. Integrating the resulting information can contribute substantially to our
understanding of the molecular processes involved in physiologic mechanisms and their disruption due to disease.
The transcriptome represents the template on which the proteome is created, though the correlation between mRNA and
protein abundance may be not so strong because of biological differences between transcription and translation processes and
experimental differences in the production of transcriptomic and proteomic data.4
Information from these two approaches may
be pooled but still cannot give us the whole picture of how an organism functions.
Metabolomics is the most recent of the "omic" sciences. Nicholson defines metabolomics as the quantitative analysis of all
the metabolites of a biological sample,5
and metabonomics as the quantitative measurements of the multiparametric metabolic
response of a living system to pathophysiological stimuli or genetic modifications.5
Indeed, the terms metabolomics and
metabonomics are often used interchangeably; in this review the term metabolomics will
be used.
Metabolomics takes a non-selective approach, potentially enabling the identification
and quantification of all the metabolites (small molecules 1 kDa) in a biological system
(metabolome).6
As far as metabolomics is concerned, metabolites are not the static
end-product of a unidirectional linear flow of information, but they form a dynamic
network of biocompounds continuously interacting with one another.7
A metabolic profile
consists of the set of metabolites in a cell or tissue, reflecting enzyme expression and
activity, and includes the building blocks and breakdown products of the DNA, RNA,
proteins, and cell membrane cellular components.3
Although the metabolic profile can be
seen as the ultimate expression of the information contained in the genetic code, it is also
affected by several factors unrelated to the genome, such as interactions with commensal
microorganisms (eg, microbial metabolites from the intestinal microflora can be detected
in human serum and urine), nutritional factors, environmental agents, and any exposure
to drugs or toxic substances resulting in discordance between genotype and phenotype.2,8
CSF Cerebrospinal fluid
HPLC High-performance liquid chromatography
IBD Inflammatory bowel disease
LC-MS Liquid chromatography­mass spectrometry
MS Mass spectrometry
MSI Metabolomics standard initiative
NMR Nuclear magnetic resonance
UPLC Ultra performance liquid chromatography
From the Department of Pediatrics (S.C.,
G.G., G.P., E.B.), University of Padova,
Padova, Italy; and the European Commission,
Joint Research Centre (G.G., F.R.), Institute
for Health and Consumer Protection,
Physical and Chemical Exposure Unit,
Ispra (VA), Italy.
The authors declare no potential conflicts
of interest.
Submitted for publication May 5, 2008; last
revision received Nov 17, 2008; accepted
Jan 9, 2009.
Reprint requests: Dr Eugenio Baraldi, Department
of Pediatrics, Allergy and Respiratory
Medicine Unit, University of Padova,
Via Giustiniani 3, 35128 Padova, Italy.
E-mail: baraldi@pediatria.unipd.it.
J Pediatr 2009;154:638-44
0022-3476/$ - see front matter
Copyright  2009 Mosby Inc. All rights
reserved.
10.1016/j.jpeds.2009.01.014
638
(Figure 1). Metabolomics is therefore considered the "omic"
science that comes closest to phenotype expression offering
the possibility to look at genotype-phenotype as well as genotype-envirotype
relationships.
Moreover, as recently highlighted by the FDA (www.
fda.gov/nctr/science/centers/metabolomics), characterizing an
individual's metabolomic profile may have a significant role in
predicting the course of disease and the response to treatment.
In line with the growing interest in the potential applications
of this approach, the intent of this review is to provide
an update on the emerging research area of metabolomics.
DIFFERENT APPROACHES TO
STUDY METABOLITES
Metabolite targeting is the most direct approach to
metabolite analyses, aiming to identify a specific metabolite,
the product of an enzymatic pathway or derived from the
degradation of a drug or toxic agent.7,9
Metabolite profiling aims to identify and quantify a
particular set of metabolites belonging to a specific metabolic
pathway or a class of compounds.6
This approach extends
from seminal studies of inborn errors of metabolism in in-
fants.10
In metabolite profiling the metabolites are sought on
the base of the questions asked: there is no chance of coming
across unknown metabolites in the sample.9
Metabolite fingerprinting, on the other hand, is a genuine
"-omic" approach, a truly comprehensive methodology
that aims to classify samples without any a priori hypothesis,
to identify metabolite patterns associated with a given pathological
condition, or exposure to a given toxic agent or drug,
or environmental or genetic change.9
Metabolic fingerprinting
does not necessarily involve identifying each metabolite,
but tries to detect the metabolic characteristics that discriminate
between groups of subjects. The search for these metabolic
patterns is not driven by a researcher's hypothesis, so it
is open to new findings. Unexpected or even unknown metabolites
may turn out to be important in characterizing
specific groups of subjects so new pathophysiological hypotheses
may be formulated.
The exact number of human metabolites is not known
and the last version of Human Metabolome Database reports
more than 6500 entries (version 2.0; November 1, 2008;
www.hmdb.ca).11
Targeted metabolic analysis can be used to
measure a limited number of these metabolites. Metabolomics
can be interpreted as an extension of targeted metabolite
profiling in as much as it enables the study of many more
metabolites.
Once a metabolic pattern typical of a given condition
has been characterized, in fact, the analysis may go on to
identify single biomarkers relevant to sample clustering.9
The
metabolomic approach may thus become a tool for moving
from studying single biomarkers (which can hardly reflect a
whole pathological process) to identifying patterns of biomarkers
capable of distinguishing between health and disease and
characterizing different disease subphenotypes.
The metabolomic approach has its limits, however, due
mainly to the analytical performance of available instruments
and to the sample variability introduced by factors such as diet
and drugs.
METABOLOMICS: ANALYTICAL PLATFORMS
AND PATTERN RECOGNITION METHODS
Nuclear magnetic resonance (NMR) spectroscopy and
mass spectrometry (MS) (often combined with chromatographic
separation) are widely used platforms for performing
Figure 1. "Omics" sciences and their relationship with environment and
phenotype.
Table. Glossary
Systems biology Systematic study of the complex interactions between the single components of a biological system (DNA, RNA,
proteins, metabolites) and characterization of the flux of information at each level of biomolecular organization
through the combination of mathematical modeling and experimental biology.
Transcriptomics The study of the transcriptome, which is the complete set of RNA transcripts produced by the genome at any
time. RNA formation, structure, and function are studied.
Proteomics The study of the proteome. which is the complete set of proteins expressed in a cell or in a tissue at any time.
Protein structure and modifications, expression, and reciprocal interaction are studied.
Metabolomics The analysis and interpretation of global metabolic data of a complex biological system. These metabolic data result
from the information enclosed in the genetic code as well as from the effect of physiological factors (eg, diet,
age, commensal microorganisms), pathological conditions, environmental agents, and exposure to drugs or toxic
agents.
Metabolomics: A New Frontier for Research in Pediatrics 639
metabolomic analyses and profiling.12
These two spectroscopic
techniques are complementary and play a central part
in the "omic" world.
1
H-NMR spectroscopy enables the detection of protoncontaining
metabolites in a sample, different molecules producing
different signals in the NMR spectrum13
(Figure 2).
NMR spectroscopy provides detailed information on
molecular structure of compounds, both pure and in complex
mixtures. Recent technological improvements have led to the
availability of stronger magnetic fields as well as the development
of cryogenic probes, increasing sensitivity and spectrum
dispersion. These improvements have been useful in studies
based on small-molecule analysis, as in the case of metabo-
lomics.
Some of the advantages of this technique are that it is
non-selective, fast and usually demands no sample preparation.
Furthermore, a technique called high-resolution magic
angle spinning (MAS) NMR spectroscopy can be used to
acquire high-resolution NMR data from small pieces of intact
tissues, with no pretreatment.13
MS is a powerful method for identifying and quantifying
metabolites, and it is considered more sensitive than
1
H-NMR.9
The technique generates a spectrum in which biocompounds
are represented according to their masses (m/z).
MS is often applied using tandem-MS methods. A
necessary preliminary step is usually the separation of the
complex mixture sample using chromatography.
Recent applications of MS in metabolomics are based
on instruments like Quadrupole Time-of-Flight (Q-ToF),
and Fourier Transform Mass Spectrometry (FT-MS).
NMR and MS analysis can be performed on very small
samples: for NMR spectroscopy a minimum of 20 L in a
capillary tube and 300 L in a flow system; for MS less than
20 L are needed (for reviews of metabolomics technologies,
see References 9 and 13).
Both NMR and MS are powerful spectroscopic methods
for generating multivariate datasets: NMR and MS spectra
are highly complex and the biological information they
contain can only be extracted using appropriate multivariate
statistical approaches--the so-called pattern recognition methods.
These are computer-based procedures that can be classified
as unsupervised or supervised.14
The unsupervised
methods, such as principal component analysis (PCA), reduce
the complexity of the data contained in the spectra and
represent them by means of plots that the human eye can
interpret. This approach helps to identify any intrinsic sample
clustering, to see whether different groups of subjects (eg,
healthy vs ill) can be discriminated by their spectra charac-
teristics.14
The supervised methods, such as partial least
squares­discriminant analysis (PLS-DA), use a training set of
samples (of known classification) to create a mathematical
model that is then used to test an independent dataset. Unlike
the unsupervised methods, the supervised methods enable us
to predict which group a new sample belongs to on the
strength of its spectra characteristics.14
These bioinformatic methods can therefore be used to
identify signals that enable discrimination between groups.
The next crucial step is the structural identification of the
single metabolites involved in the sample classification.
Identifying biomarkers can involve many analytical
physical and chemistry disciplines: NMR and MS spectroscopies
play a very important part, but no single technique
fulfills all requirements for fully elucidating metabolite structure
and multiple approaches need to be integrated.
Fundamental support for molecular identification comes
from various on-line databases. The most comprehensive
Figure 2. Example of a 500-MHz 1H-NMR spectrum of human urine. Experimental conditions: 200 L urine in 400 L d-phosphate buffer (pH 7);
inverse broad band probe; 5-minute experimental time.
640 Carraro et al The Journal of Pediatrics ˇ May 2009
available database of human metabolites is the Human
Metabolomic Database.11
Other important databases are
KEGG,15
BioCyc,16
Reactome,17
and Metlin.18
METABOLOMICS: ITS ROLE IN
PATHOLOGICAL CONDITIONS
Recently, a number of studies have investigated whether
the metabolomic analysis of biofluids (eg, blood, urine) can be
usefully applied in the diagnosis of certain diseases.
In the field of cardiovascular diseases, the NMR-based
metabolomic analysis of blood samples has been successful in
the discrimination between subjects with hypertension and
normal blood pressure.19
A recent study has also demonstrated
that the urinary biomarker formate, identified through
an NMR-based metabolomic approach, correlates inversely
with blood pressure in human populations.20
Metabolomic
analysis based on HPLC-MS proved capable of characterizing
patients who develop myocardial ischemia after an exercise
test.21
It has been demonstrated that NMR-based
metabolomic analysis of blood samples can distinguish between
people with and without coronary heart disease.22,23
The analysis is more effective than the conventional risk
factors (age, sex, lipoprotein levels, smoking habit, blood
pressure) in establishing the severity of the disease.22
By
means of a targeted MS-based metabolomic platform, Lewis
et al24
recently demonstrated significant metabolite abnormalities
in blood samples as soon as 10 minutes after a
myocardial infarction, even though presently used indicators
of myocardial injury are reliably detected only a few hours
after the infarction.
Individuals with type 1 diabetes reportedly have a characteristic
serum NMR-based metabolomic profile that seems
as good as a full set of biochemical variables in diagnosing
diabetic nephropathy.25
In diabetic patients, characteristic
serum metabolomic features have also been shown to be
associated with vascular complications and premature death.26
Characteristic metabolomic profiles have likewise been described
in patients with type 2 diabetes.27,28
Some attempts have been made to apply metabolomics
as a diagnostic tool in clinical investigations in the field of
neurology. NMR-based metabolomic analysis of cerebrospinal
fluid (CSF) samples was capable of discriminating between
patients with bacterial meningitis, cases of viral meningitis
and controls.29
Patients who had already been given
antimicrobial therapy before sample collection for metabolomic
analysis (and who consequently had a negative CSF
culture) were still correctly assigned to the bacterial meningitis
group using the metabolomic approach.29
Dunne et al
evaluated the ability of the NMR-based metabolomic analysis
of CSF to characterize patients with subarachnoid hemorrhage
and identify those with the worst outcome due to
prolonged vasospasm.30
Ghauri et al demonstrated a biochemical
profile typical of patients who died of Alzheimer's
disease in samples collected post mortem.31
Combined NMR spectroscopy and pattern recognition
methods have been proposed for analyzing fecal extracts from
patients with inflammatory bowel disease (IBD)32
. The preliminary
study was unable to reveal any significant differences
in the metabolomic profiles of patients with Crohn's disease
versus those with ulcerative colitis; however, the authors did
demonstrate that metabolomics can clearly discriminate IBD
patients from healthy subjects.32
A few studies have assessed the potential role of
metabolomics in following up patients who have received an
organ transplant. The urinary NMR-based metabolomic profile
can distinguish between kidney recipients with normally
functioning versus nonfunctioning grafts.33,34
Another study
of a patient who had 2 liver transplantations, identified a
characteristic early metabolomic profile (obtained by NMRspectroscopic
analysis of blood samples) that was associated
with the failure of the first graft but was no longer seen after
the second transplant, when the organ was functioning prop-
erly.35
A promising application for metabolomics is in cancer
screening and early diagnosis. In particular, there may be a
role for NMR-based metabolomic analysis of blood samples
in the early identification of women with epithelial ovarian
cancer.36
HPLC-based metabolomics has been applied successfully
to urine samples to distinguish patients with liver
cancer from those with cirrhosis or hepatitis.37
Finally, a
recent study was able to discriminate, with high level of
sensitivity and specificity, between patients with and without
bladder cancer by HPLC-MS­based metabolomic analysis of
urine samples, suggesting a potential role for such analysis as
a screening tool for bladder cancer.38
In the oncological field,
metabolomics can also be used to analyze tissue samples,39-42
enabling the tumor's metabolic profile to be characterized and
different types of cancer to be distinguished. Being a nondestructive
technique, NMR spectroscopy is particularly suitable
for analyzing tissues that can subsequently be used for histopathologic
analyses.
METABOLOMICS: PEDIATRIC STUDIES
In one publication, the untargeted metabolomic approach
was applied to studying disorders due to inborn errors
of metabolism.43
This study used untargeted metabolomic
analyses on plasma samples. Investigating disorders of propionate
metabolism (methylmalonic acidemia [MMA] and propionic
acidemia [PA]) the authors demonstrated that the
most important metabolite for the discrimination between
healthy and ill subjects was propionyl carnitine, which is,
indeed, the target compound for screening newborn for
MMA and PA by tandem-MS. This result validates the role
of untargeted metabolomic analysis in identifying biomarkers
of disease. Moreover, the untargeted metabolomic analysis
showed that many other compounds are important in differentiating
both between healthy and ill subjects and between
MMA and PA subjects.43
Another study described a characteristic metabolomic
profile in the CSF of children with influenza-associated encephalopathy,
suggesting that it might be possible to identify
Metabolomics: A New Frontier for Research in Pediatrics 641
specific biomarkers useful for the early diagnosis of this dis-
ease.44
Metabolomics can be effective in identifying asthmatic
children.45
The metabolomic approach was applied to analyzing
exhaled breath condensate (breathomics): this is a
biofluid collected in a totally non-invasive way by cooling
down the exhaled air and its composition is believed to mirror
the composition of airway lining fluid.46
The authors demonstrated
that NMR-based metabolomic analyses of exhaled
breath condensate can clearly discriminate between asthmatic
and healthy children. On submitting the spectra to partial
least square analysis, they achieved an approximately 95%
success rate in their classification (Figure 3). Many authors
believe that asthma should no longer be considered a single
disease and that efforts should be made to identify the different
biochemical-inflammatory profiles underlying asthma
symptoms in order to treat them with specifically targeted
therapies.47
The metabolomic approach may have a role in
characterizing asthma subphenotypes, potentially leading to
more tailored therapies. The metabolomic analysis of exhaled
breath condensate has been also applied to the study of
respiratory diseases in adults.48
A study has assessed the role of metabolomics in identifying
early urinary biomarkers of acute kidney injury after
cardiopulmonary bypass surgery.49
Using ultra performance
liquid chromatography (UPLC)/MS­based metabolomicanalyses
on urine samples collected 4 and then 12 hours after
surgery, the authors were able to identify the children who
were to develop acute kidney injury over the next 3 days.
From a clinical standpoint, identifying early metabolic biomarkers
may improve our understanding of the pathophysiological
mechanisms involved in acute kidney injury and
enable a timely diagnosis of this disease.
METABOLOMICS IN NUTRITION SCIENCE
In numerous studies metabolomic analysis has been
used to assess the effects of diet on metabolism. Bertram et al
compared the metabolomic profiles of 2 groups of children
receiving a diet rich in milk or meat proteins. They succeeded
in separating the children according to their diet and identified
a significantly greater urinary excretion of creatine in the
children on a diet rich in meat.50
A study conducted in adults
demonstrated that the urinary metabolomic profile of meat
eaters differs considerably from that of vegetarians.51
Solanky
et al52
described consistent metabolomic plasma profiles in a
group of healthy premenopausal women adopting specific
soy-rich dietary measures. Walsh et al53
demonstrated that
changes in dietary intake of phytochemicals induce modifications
in an individual's metabolomic urinary profile. Law et
al54
showed that the metabolomic approach can be used to
provide a comprehensive picture of the metabolic changes
induced by ingestion of green tea.
Nutritionists are now considering the metabolomic approach
with a view to establishing individual nutritional phenotypes--or,
in other words, how diet interacts with an
individual's metabolism--to provide a comprehensive definition
of nutritional status and predicting health and disease
outcomes.55
It is worth mentioning that in studying the metabolic
effects of dietary interventions, other methodologies play an
important role, such as the in vivo labeling of stable isotopes.
For example, the effects of enteral nutrition on protein balance
and kinetics have been studied in children with Crohn's
disease, and the effect of parenteral nutrition was investigated
in infants undergoing abdominal surgery.56,57
METABOLOMICS IN PHARMACOLOGY
AND TOXICOLOGY
NMR- and MS-based metabolomics are widely used to
test drug toxicity.58
In preclinical candidate drug studies,
metabolomics can be useful in describing how a drug affects
an individual's metabolism, providing information on the site
and mechanism of toxicity, and revealing the time course of
its metabolic effects.59
Knowing the metabolomic profile associated with a
given disease may also help us to monitor biological and
metabolic responses to treatment.58
For example, patients
with type 2 diabetes mellitus reportedly have an early characteristic
metabolomic profile in response to treatment with
thiazolidinediones.60
Among AIDS patients, NMR-based
metabolomic analysis can distinguish cases treated with antiretroviral
therapy from HIV-negative subjects, suggesting
that the technique may become a tool for monitoring the
metabolic effects of the therapy and possible side effects.61
Another promising opportunity afforded by metaboloFigure
3. Sample discrimination obtained by means of pattern
recognition methods in childhood asthma (modified from Reference 45).
642 Carraro et al The Journal of Pediatrics ˇ May 2009
mic analysis concerns studying xenobiotic effects on an organism--potentially
enabling us to predict the effectiveness or
toxicity of a drug on the basis of the individual's pretreatment
metabolomic characteristics, and helping us to understand the
mechanisms behind idiosyncratic toxicity.58
This branch of
metabolomics is called pharmacometabolomics. An elegant
study conducted in mice demonstrated that the hepatotoxic
effects of the analgesic acetaminophen could be predicted on
the basis of the pretreatment urinary profiles.62
This report
was later confirmed by a study showing that the baseline
metabolite profiles could be used to predict the response to
different xenobiotic measures in experimental animals.63
These studies have introduced the concept that an individual's
response to a xenobiotic is determined not only by genetic
characteristics, but also by other factors such as diet, aging,
gut microbiota and physiological diurnal variation.63
Pharmacometabolomics
appears to be an extremely promising branch
of metabolomics for screening human populations, implying
the concrete possibility of a genuinely customized approach to
treatment.
STANDARDIZATION IN METABOLOMICS
The metabolomic profile of a biological sample is affected
by several physiological factors, including age, sex, diet,
and circadian variations (Figure 1), and the related changes in
human metabolic profiles have been investigated.64,65
As an
example, a study conducted on healthy subjects adopting a
strict diet and particular lifestyle showed a relatively small
intersubject and intrasubject variability.66
Another study in
which no dietary restrictions were applied found a significant
degree of inter-individual variability in urinary metabolomic
profile.67
This variability is reduced if a standard diet is
followed on the day before urine collection.68
In metabolomics, more than in the other "-omic" sciences,
it is therefore crucially important to collect background
data on the individual concerned to help interpret the results.
In addition, for metabolomic experiments, it is important
to develop standard protocols regarding sample collection
and storage, chemical analyses, data processing and the exchange
of information.69
The Metabolomics Standard Initiative (MSI) was created,
with the support of the Metabolomic Society (www.
metabolomicssociety.org), to recommend standard protocols
for use in all aspects of metabolomic research.70
Such standardization
will facilitate the future development of applications
of the metabolomic approach.
CONCLUSIONS
Although it has been used in only a limited number of
clinical studies so far, metabolomics seems to have promising
applications. This approach carries the promise of a more
individualized medicine thanks to the possibility of predicting
the course of disease and the response to treatment on the
basis of an individual's metabolomic profile.
Metabolomics has potential applications in both the
diagnosis of diseases and the development of treatment strategies,
with the advantage of the analysis being performed on
biofluid samples that are collected noninvasively (eg, urine or
exhaled breath condensate) or by only minimally invasive
procedures (eg, blood drawing). Metabolomics can lead to the
discovery of new noninvasive metabolic biomarkers that could
be translated, in the near future, into clinical tools for diagnosing
diseases and developing treatment strategies, paving
the way toward a medicine tailored to the patient.
We thank our mentor, Professor Franco Zacchello, for his helpful
and brilliant advice.
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