Metabolomics can be regarded as the end point of
the “omics” cascade.
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Metabolomics is an emerging field in analytical biochemistry and can be regarded
as the end point of the "omics" cascade. Whereas genomics deals with the analysis
of the complete genome in order to understand the function of single genes,
the majority of functional genomics studies are currently based on the analysis
of gene expression (transcriptomics) and comprehensive protein analysis (proteomics).
As we are amassing knowledge of the genome, the transcriptome, and the proteome,
we have largely forgotten the metabolome. However, changes in the metabolome
are the ultimate answer of an organism to genetic alterations, disease, or
environmental influences. The metabolome is therefore most predictive of phenotype
(Fiehn 2002; Weckwerth 2003). Consequently, the comprehensive and quantitative
study of metabolites, or metabolomics, is a desirable tool for either diagnosing
disease or studying the effects of toxicants on phenotype.
One of course wonders why metabolomics has lagged behind other "omics" technologies.
Possibly this is because the number of metabolites varies dramatically based
on how they are counted. Investigators also debate about what compounds are
considered metabolites; for example, should vitamins or smaller peptides be
included? According to a simple and widely used definition, a metabolite is
any substance involved in metabolism either as a product of metabolism or necessary
for metabolism. In any case 3,000 major metabolites seem a reasonable number.
If we attempt a global and quantitative evaluation, the technology involved
is daunting because the physical properties of the compounds are so divergent
and they vary dramatically in concentration. Moreover, the metabolome is a
dynamic system subjected to significant environmental influences, for example,
temporal or dietary.
It is difficult to envision a single platform being developed in the near
future that is able to analyze quantitatively all metabolites simultaneously.
Thus with all metabolites as our goal, the technological hurdle seems to be
the limiting step. At the other extreme, metabolomics can be seen as metabolite
profiling or "just" analytical chemistry. So it is nothing new, simply multi-analyte
chemistry that biochemists have been doing for decades. Of course metabolomics
is simultaneously both and neither of these. Although an "omics" or global
view of metabolism is a goal, by no means is universal coverage of all metabolites
required for tremendous biological insight. Also whether we work on complete
coverage of a single metabolic pathway or on a more global approach to examine
multiple metabolites, such multi-analyte analysis is by no means trivial. Nevertheless,
successful implementation of metabolomics requires analytical instrumentation
that offers high throughput, resolution, reproducibility, and sensitivity,
and only an assembly of different analytical platforms will currently provide
maximum coverage of the metabolome. To date, metabolomics-type studies rely
primarily on nuclear magnetic resonance (NMR) or mass spectrometry coupled
to chromatography.
Currently, two complementary approaches are used for metabolomic investigations.
In one approach--metabolic profiling--quantitative analytical methods are developed
for metabolites in a pathway or for a class of compounds. This approach produces
independent information that can be interpreted in terms of known biochemical
pathways and physiological interactions. These data represent an independent
legacy database since they are quantitative. The disadvantage is that the system
is not a universal or "omics" approach. However, the tremendous advances in
technology over the past years allow the constant expansion of the number of
analytes quantified simultaneously. Technologically, we are at a point where
it is often as simple to measure many compounds as to measure one. If we take
one step further and assemble a suite of quantitative methods analyzing key
metabolites from different biochemical pathways, we can transform metabolic
profiling into metabolomics.
The second approach is metabolic fingerprinting. In such metabolomic investigations,
the intention is not to identify each observed compound but to compare patterns
or fingerprints of metabolites that change in response to disease or toxin
exposure. Comparison of fingerprints, often NMR or mass spectra or chromatograms,
is performed using statistical tools such as hierarchical cluster analysis
or principal component analysis. If these types of analyse results in sample
segregation into unique metabolic clusters, further efforts can be made to
elucidate the discriminating compounds and subsequently to evaluate these monocytes
as potential biomarkers. Being semiquantitative and simultaneously applicable
to a wide range of metabolites--this is a true "omics" approach. Such methods
are attractive, as they allow investigators to cast a wide net both generating
and testing hypotheses. However, the nature of the data makes the observations
instrument/platform dependent. The implementation of NMR-based metabolic fingerprinting
has marked the beginning of a metabolomics approach as a tool in biochemistry
and has proven to be a powerful technique (Nicholson et al. 2002). However,
it will only detect high abundance metabolites. Complementary to NMR, mass
spectrometry-based tools will provide coverage for metabolic fingerprinting
in a lower concentration range, and their use is increasing steadily (Plumb
et al. 2003).
The combination of metabolic profiling and fingerprinting will lead to the
realization of metabolomics. In one approach, changes in fingerprints correlating
to metabolite profiles will be linked to a physiological state, without exact
knowledge of fingerprint components. In another approach, discriminating compounds
identified in fingerprints will become the focus for quantitative metabolite
analyses. Therefore, metabolomics will contribute to our biological understanding
both in a mechanistic as well as a predictive manner. However, it could also
assist us in improving human health and may be among the first of the "omics" technologies
to reach the clinic. Through multiple metabolomics projects, a powerful list
of likely markers of variations in health can evolve (Watkins and German 2002).
Analyzing this set of biomarkers in a single high throughput assay will provide
the clinician with a powerful diagnostic tool.
In genomics and transcriptomics we saw economies of scale as institutional
support developed generating infrastructure behind the technologies. Similar
support will be necessary to advance metabolomics. For example, a centralized
effort to provide isotopic-labeled standards for a wide range of metabolites
would tremendously accelerate work in metabolomics as would the development
of an integrated pathway map to aid in data interpretation. Such a map would
introduce us also to the next level of measuring flux through pathways.
Although metabolomics is still in an early evolutionary stage, we can expect
to see exciting new developments in the near future. As more quantitative metabolomic
databases evolve, we can integrate them with data sets from the other "omics" technologies
to enhance the data value and provide greater biological insight than any one "omics" technique
alone can offer.
Katja Dettmer
Bruce D. Hammock
Cancer Research Center
University of California, Davis
Davis, California
E-mail: bdhammock@ucdavis.edu
Katja Dettmer is a postdoctoral researcher in professor Bruce Hammock's laboratory
at the University of California, Davis. She is conducting research in the field
of metabolomics, focusing on the development of mass spectrometry-based
tools for metabolic fingerprinting in biofluids as well as metabolic profiling
methods.
Bruce Hammock is a Distinguished Professor of Entomology and a scientist
in the Cancer Research Center at the University of California, Davis. He directs
an analytical-metabolomics laboratory that pioneered the use of immunochemical
diagnostics in the environmental field. His research interests include development
of recombinant viral pesticides, mammalian xenobiotic metabolism, environmental
chemistry, and biosensor development.
References
Fiehn O. 2002. Metabolomics--the link between genotypes and phenotypes. Plant
Mol Biol 48:155-171.
Plumb RS, Stumpf CL, Granger JH, Castro-Perez J, Haselden JN, Dear GJ. 2003.
Use of liquid chromatography/time-of-flight mass spectrometry and multivariate
statistical analysis shows promise for the detection of drug metabolites in
biological fluids. Rapid Commun Mass Spectrom 17:2632-2638.
Nicholson JK, Connelly J, Lindon JC, Holmes E. 2002. Metabonomics: a platform
for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153-161.
Watkins SM, German J B. 2002. Toward the implementation of metabolomic assessments
of human health and nutrition. Curr Opin Biotechnol 13:512-516.
Weckwerth W. 2003. Metabolomics in systems biology. Annu Rev Plant Biol 54:669-689.
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Last Updated: May 10, 2004
