MetabolomicsPeter J. Eastmond

Phone: +44 02476 575096email: p.j.eastmond@warwick.ac.uk

Lecture Plan:

What is the Metabolome and Metabolomics

What is the history of the subject

How does it relate to other ‘omics’

What are its strengths and weaknesses

How is it done

What are its applications

Specific examples

The Metabolome

The term "metabolome" was first used by Olivier et al. in 1998 to describe the set of metabolites synthesized by an organism, in a fashion analogous to that of the genome and proteome.

Some have limited this definition to "the quantitative complement of all of the low molecular weight molecules present in cells in a particular physiological or developmental state".

Metabolomics

Metabolomics was coined by Oliver Fiehn and defined as a comprehensive nonselective analysis in which all metabolites of a biological system were identified and quantified.

Metabonomics

There is a growing consensus that the difference resides in the fact that 'metabolomics' places a greater emphasis on comprehensive metabolic profiling, while 'metabonomics' is used to describe multiple (but not necessarily comprehensive) metabolic changes caused by a biological perturbation.

Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification".

Metabolite profiling

Both Metabolomics and Metabonomics involve nonselective or non bias analysis.

In contrast ‘Metabolite profiling’ involves the identification and quantitation by a particular analytical procedure of a predefined set of metabolites of known or unknown identity and belonging to a selected metabolic pathway.

The History of Metabolomics

Linus Pauling hypothesised on the predictive capacity of chromatographic profiling of bodily fluids for detection and diagnosis of human disease.

Chromatographic separation techniques were developed in the late 1960's.

Robinson and Pauling published “Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography” in 1971.

The Metabolome and Metabolomics were coined in the 1990s.

In January 2007 the Human Metabolome Project, completed the first draft of the human metabolome, consisting of 2,500 metabolites, 1,200 drugs and 3,500 food components.

Metabolomics: Where does it fit?

Integration of genomics, transcriptomics, proteomics and metabolomics is a goal of systems biology.

In theory, is it the best omics?

It has been argued that metabolomics provides the most"functional" information of the omics technologies.

Changes in the transcriptome and proteome do not always result inaltered biochemical phenotypes (the metabolome). The metabolomerepresents the final "omic" level in a biological system, and metabolites represent functional entities, unlike messenger RNA molecules, which constitute the transcriptome. Metabolites thus have a clear function in the life of the biological system and are also contextual, reflecting the surrounding environment. The metabolome can thus be thought of as a looking glass, which if looked through can show information concerning the physiological, developmental, and pathological status of a biological system.

In practice, is it feasible?

Metabolomics was coined by Oliver Fiehn and defined as a comprehensive nonselective analysis in which all metabolites of a biological system were identified and quantified.

It is estimated that the metabolome extends over 7-9 magnitudes of concentration (pmol-mmol), and the number of metabolites in the plants is estimated to exceed 200,000 and only about 10,000 are known.

It is not currently possible to analyse the entire range of metabolites by a single analytical method.

What can be done

Different separation and detection methods can be used either individually or in combination to detect and quantify hundreds or perhaps thousands of metabolites.

Analytical Technologies: Separation

Gas chromatography

It offers high resolution, but requires chemical derivatization for many biomolecules and only volatile chemicals can be analysed without derivatization.

Gas-liquid chromatography - involves a sample being vapourised and injected onto the head of the chromatographic column. The sample is transported through the column by the flow of inert, gaseous mobile phase. The column itself contains a liquid stationary phase which is adsorbed onto the surface of an inert solid.

High performance liquid chromatography

Analytical Technologies: Separation

HPLC has lower resolution than GC, but it does have the advantage that a much wider range of analytes can potentially be measured.

Capillary electrophoresis

Analytical Technologies: Separation

It has a higher theoretical separation efficiency than HPLC and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes.

Analytical Technologies: Detection

Mass spectrometry

Used to identify and to quantify metabolites after separation by GC, HPLC, or CE. In addition, mass spectral fingerprint libraries exist that allow identification of a metabolite according to its fragmentation pattern.

There are many types of mass spectrometers that not only analyze the ions differently but produce different types of ions; however they all use electric and magnetic fields to change the path of ions in some way.

Sector instrument

Nuclear magnetic resonance (NMR) spectroscopy

Analytical Technologies: Detection

NMR is almost the only detection technique which does not rely on extraction and separation of the analytes, and the sample can thus be analysed in vivo and recovered for further analyses.

Any molecule containing one or more atoms with a non-zero magnetic moment can potentially be detected. In practice metabolites are labelled by feeding substrates containing 1H, 13C, 14N, 15N or 31P isotopes.

NMR is close to being a universal detector. However, it possesses one major disadvantage, which is that it is relatively insensitive compared to mass spectrometry-based techniques.

Data analysis and interpretation

Similar issues to transcriptomics and proteomic data sets.

Use of multivariate statistical analysis is most common.

Metabolomics Applications

Diagnosis

Disease (e.g. coronary heart disease).Toxicology

Functional genomics

Ascribing functions to genes

Systems biology

Integration with data sets from other omics.

Examples

Metabolomics:

Probably none

Metabonomics and metabolite profiling:

Many

Examples

A flux map for Saccharomyces cerevisiae.

The fluxes were estimated through feeding the cells with 13C-labelled glucose, analysis of the isotopomers of the intracellular metabolites, and analysis of the data using the mathematical model of the metabolism. In the wild-type strain there is glucose repression on respiration thus the flux through the TCA cycle is low. When Grr1 is deleted (italics) there is a de-repression of respiration and the flux through the TCA cycle therefore increases.

Gombert et al., (2001) J. Bacteriol. 183, 1441-1451.

Examples

Rubisco improves the carbon efficiency of developing green seeds.Schwender et al., (2004) Nature 432:779-782.

Sucrose

Oil

Oilseed rape pod

Examples

Diagnosis of coronary heart disease

Brindle et al., (2002) Nature Medicine 8, 1439-1444.

The 600 MHz 1H-NMR spectra of serum samples from a typical

NCA patient (a) and a TVD (triple vessel disease) patient.

Examples

(b) QTL likelihood profiles of aliphatic glucosinolates detected in the recombinant inbred line population. (c) Second-order genetic correlations between aliphatic glucosinolates detected in the recombinant inbred line population.

Keurentjes et al., (2006) Nature Genetics 38, 842-849.

Genetic regulation of glucosinolate accumulation in Arabidopsis

Some reading

Reviews

Fiehn O. (2002) Metabolomics-the link between genotypes and phenotypes. Plant Mol Biol. 48, 155-171.

Schauer N, Fernie AR. (2006) Plant metabolomics: towards biological function and mechanism. Trends Plant Sci. 11, 508-516.

Schnackenberg LK, Beger RD. (2006) Monitoring the health to disease continuum with global metabolic profiling and systems biology. Pharmacogenomics. 7, 1077-1086.

Lenz EM, Wilson ID. (2007) Analytical strategies in metabonomics. J Proteome Res. 6, 443-458.

Papers

Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E.; Grainger, D. J. (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine 8, 1439-1444.

Raamsdonk, L. M., B. Teusink, D. Broadhurst, N. S. Zhang, A. Hayes, M. C. Walsh, J. A. Berden, K. M. Brindle, D. B. Kell, J. J. Rowland, H. V. Westerhoff, K. van Dam, and S. G. Oliver 2001. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnology. 19, 45-50.Gombert AK, Moreira dos Santos M, Christensen B, Nielsen J. (2001) Network identification and flux quantification in the central metabolism of Saccharomyces cerevisiae under different conditions of glucose repression. J. Bacteriol. 183, 1441-1451.Keurentjes JJ, Fu J, de Vos CH, Lommen A, Hall RD, Bino RJ, van der Plas LH, Jansen RC, Vreugdenhil D, Koornneef M. (2006) The genetics of plant metabolism. Nature Genetics 38, 842-849.