Metabolomics by numbers: acquiring and understanding global metabolite data

doi:10.1016/j.tibtech.2004.03.007

Trends in Biotechnology

Volume 22, Issue 5, 1 May 2004, Pages 245-252

https://doi.org/10.1016/j.tibtech.2004.03.007 Get rights and content

Abstract

In this postgenomic era, there is a specific need to assign function to orphan genes in order to validate potential targets for drug therapy and to discover new biomarkers of disease. Metabolomics is an emerging field that is complementary to the other ‘omics and proving to have unique advantages. As in transcriptomics or proteomics, a typical metabolic fingerprint or metabolomic experiment is likely to generate thousands of data points, of which only a handful might be needed to describe the problem adequately. Extracting the most meaningful elements of these data is thus key to generating useful new knowledge with mechanistic or explanatory power.

Section snippets

Measuring the metabolome

The ultimate starting point of a metabolomic experiment is to quantify all of the metabolites in a cellular system (i.e. the cell or tissue in a given state at a given point in time). Currently this is impossible, given the lack of simple automated analytical strategies that can effect this in a reproducible and robust way. The main challenges are the chemical complexity and heterogeneity of metabolites, the dynamic range of the measuring technique, the throughput of the measurements, and the

Technology platforms for metabolomics

Metabolites are chemical entities and can be analysed by the standard tools of chemical analysis such as molecular spectroscopy and MS. The resolution, sensitivity and selectivity of these technologies can be enhanced or modified by coupling them to gas chromatograpy (GC) or liquid chromatography (LC) steps. The technologies commonly exploited for different metabolomic strategies are shown in Figure 1. Generally, the technology platform of choice depends on the type of sample to be analysed.

Databases for metabolomics

In a recent study, Lyman and Varian estimated that in 2000 the world produced between 1 and 2 exabytes (1–2×10¹⁸ bytes) of ‘unique’ information per year (http://www.sims.berkeley.edu/how-much-info). This flood of data is roughly 250 megabytes for every man, woman and child on earth! IBM's estimates are that information within the life sciences doubles every 6 months (http://www.bio-itworld.com/champions/janet_perna.html); this data explosion comes from genomic sequencing, the ‘omics’ and

A paradigm shift from metabolic pathways to networks and neighbourhoods

There has been a shift from mental constructs involving metabolic pathways to those based on metabolic networks and neighbourhoods 37, 38, and many would argue that the ‘Boehringer’ metabolic pathways map needs to be updated both radically and conceptually. An excellent example of this is illustrated by the experiments of Willmitzer and colleagues [39] on the carbon sink in potatoes. The aim of these experiments was to increase the amount of starch in the tubers by the ‘rational’ overproduction

Hypothesis-generating strategies from metabolome data

Many of the pattern-recognition strategies currently pursued in metabolomics, and indeed in the analyses of all ‘omic data, are based on ‘unsupervised’ techniques [48] (Figure 2), such as hierarchical cluster analysis in which a ‘tree-like’ dendrogram (as commonly seen in taxonomic and phylogenetics) is produced. Clustering methods are used to assess, in a multivariate manner, how similar a set of samples are to one another on the basis of their metabolite profiles, although many of the methods

From metabolomics to systems biology

“When a thing was new, people said, ‘It is not true’. Later, when the truth became obvious, people said, ‘Anyway, it is not important.’ And when its importance could not be denied, people said, ‘Anyway, it is not new.” William James (1842–1920).

‘Systems biology’ describes a range of techniques, including the ‘omics and mathematical modelling, for understanding systems ‘as a whole’ 63, 64, and it is widely recognized that metabolomics will have a major part to play in its development [65].

Concluding remarks

The field of metabolomics is gaining increasing interest across all disciplines, including functional genomics, integrative and systems biology, pharmacogenomics, and (surrogate) biomarker discovery for drug discovery and therapy monitoring. As more researchers get ‘tooled up’ for metabolomics, the realization that it is easy to generate floods (or, more accurately, torrents!) of data will become apparent. Thus, in the new postgenomic era of biology, we shall need well-curated databases, very

Acknowledgements

R.G., S.V. and D.B.K. are most grateful to the UK BBSRC, UK EPRSC and UK NERC for supporting our work.

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Trends in Biotechnology

Metabolomics by numbers: acquiring and understanding global metabolite data

Abstract

Section snippets

Measuring the metabolome

Technology platforms for metabolomics

Databases for metabolomics

A paradigm shift from metabolic pathways to networks and neighbourhoods

Hypothesis-generating strategies from metabolome data

From metabolomics to systems biology

Concluding remarks

Acknowledgements

Trends Biotechnol.

FEBS Lett.

Trends Anal. Chem.

Anal. Biochem.

J. Chromatogr. A.

J. Chromatogr. B

Curr. Opin. Struct. Biol.

Trends Genet.

Curr. Opin. Biotechnol.

Trends Biotechnol.

Anal. Chim. Acta

Trends Genet.

Cell

Artif. Intell.

FEMS Microbiol. Rev.

Whole-genome random sequencing and assembly of Haemophilus influenzae Rd

Science

Sequence and organization of the human mitochondrial genome

Nature

The complete genome sequence of Escherichia coli K-12

Science

The sequence of the human genome

Science

Metabolomics – the link between genotypes and phenotypes

Plant Mol. Biol.

The human metabolome

Understanding the Control of Metabolism

Snapshots of systems: metabolic control analysis and biotechnology in the post-genomic era

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

Nat. Biotechnol.

Parallel analysis of transcript and metabolic profiles: a new approach in systems biology

EMBO Rep.

Topics in xenobiochemistry: do metabolic pathways exist for xenobiotics? The micro-metabolism hypothesis

Xenobiotica

Analysis of proteins and proteomes by mass spectrometry

Annu. Rev. Biochem.

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BioEssays

So what's the deal with metabonomics? Metabonomics measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins

Anal. Chem.

Understanding ‘global’ systems biology: metabonomics and the continuum of metabolism

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Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics

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Bioinformatics

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Proc. Natl. Acad. Sci. U. S. A.

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OMICS

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Minimum information about a microarray experiment (MIAME) – toward standards for microarray data

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A systematic approach to modeling, capturing, and disseminating proteomics experimental data

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Databases, data modelling and schemas

Network biology: understanding the cell's functional organization

Nat. Rev. Genet.

Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using ¹H-NMR-based metabonomics