The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?

Abstract

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including 1H NMR spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), Fourier Transform mass spectrometry (FT-MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this 'Cinderella approach' will have at the 'functional genomic ball'.

Figure 1

A schematic view of metabolomics. The position of metabolomics is shown in respect to the other omic approaches and the influence of the environment. In addition a scheme is shown for producing pattern recognition models capable of deducing metabolic profiles. The initial phase usually involves the acquisition of a large dataset in terms of both the variables (metabolites) and observations (subjects), commonly using either ¹H NMR spectroscopy or mass spectrometry. However, the use of pattern recognition techniques is an integral part of the approach if the innate metabolic variation associated with different individuals is to be separated from that induced by the insult or physiological stimulus. Following the generation of a database from metabolic responses, this can then be used to build a predictive pattern recognition model that is capable of predicting class membership (e.g. clustering according to the gene deleted).

Figure 2

A comparison of NMR and LC–MS analysis of urinary metabolite profiles. A range of different profiling tools are currently being used for metabolomics, with no one approach providing a complete coverage of the metabolome. These figures show PCA of data derived from a study of rat urine using NMR spectroscopy (a) and LC–MS (b). In these figures each dot represents a sample of rat urine analysed. Samples with similar metabolic profiles will have a similar position in the PCA scores plot, producing the clustering detected. In both models the metabolite profiles could be classified according to gender of the animal or the time the samples were removed. However, in this example more discrete classification was produced using LC–MS. Diagram provided by Mark Hodson and John Haselden of GlaxoSmithKline.

Figure 3

An orthogonal signal corrected (OSC) PLS-DA analysis of four mouse models of cardiac disease using metabolic profiles from ¹H NMR analysis of cardiac tissue extracts. OSC is a data filtering technique that removes variation uncorrelated with class membership. The PLS-DA analysis then clusters spectra from the different mouse models across three axes that represent the most amount of correlated variation in the dataset associated with class membership. Key: open circle, control animals (three different strains); plus, mouse model of Duchenne muscular dystrophy; filled diamond, mouse model of cardiac hypertrophy (Muscle LIM protein knock out mouse), and two models of cardiac arrhythmia: a cardiac sodium channel knock out mouse (Scn^−/+), filled square, and a model where the closure of the cardiac sodium channel is impaired (Scn^Δ/+), filled triangle (taken from Jones et al. 2005).

Figure 4

Principal component analysis of urinary metabolite profiles (left) readily separated two strains of mouse. Inspection of the loadings plot for these models showed that the two mouse strains were categorized according to differences in the metabolic pathways associated with trimethylamine-oxide metabolism (right). Taken from Gavaghan et al. 2000.

Figure 5

Two methods for cross correlating data from different ‘omic’ approaches. (a) shows a heat map plot of correlation coefficients from a partial least squares model of 20 transcripts (along the x-axis) and 200 metabolites (along the y-axis). Regions with an intense red colour are positively correlated. (b) Histograms of bootstraps for correlation coefficients between key metabolite regions and transcripts. The x-axis represents the correlation coefficients while the y-axis represents the number of times this correlation was returned during 10 000 iterations. Key: SCD, stearoylCoA desaturase 1; ApoC, apolipoprotein C III; MVLC, mitochondrial very long-chain acyl CoA thioesterase; GPAT, glycerol 3 phosphate acyltransferase; FAC, fatty acid CoA ligase 4; CH=CH unsaturated lipid resonance, CH₂CH₂CH₂ saturated lipid resonance (taken from Griffin et al. 2004b).

Figure 6

Metabolomic analysis of brain tissue from schizophrenics using high-resolution magic angle spinning ¹H NMR spectroscopy. Tissue was profiled using both a solvent suppressed pulse sequence (a, grey matter; b, white matter) and a T₂ relaxation weighted pulse sequence (A Carr Purcell Meiboom and Gill sequence) to selectively attenuate lipid resonances relative to aqueous metabolites (c, white matter). Spectra were acquired at 700.13 MHz at 3 °C and at a rotor spinning speed at 7000 Hz. This data was examined using multivariate data analysis including partial least squares-discriminate analysis (PLS-DA, a supervised regression extension of principal components analysis). The observation plot (d) of the PLS-DA model demonstrated that spectra of white matter from schizophrenic patients (open circle) formed a cluster apart from tissue from control patients (filled square)(cluster highlighted with broken line). The metabolic difference causing this was identified in the loadings plot (e) of the PLS-DA model, and was largely caused by increases in concentration of lactate (δ 1.32, 4.12), –CH₂CH₂CH₂– lipid group (δ 1.36–1.32), phosphocholine (δ 3.24, 3.68) and N-acetyl aspartate (NAA) (δ 2.04), and decreases in CH₃– terminal lipid groups (δ 0.96–1.04) and myo-inositol (δ 3.52–3.60, 4.08), where δ signifies the centre of the 0.04 p.p.m. chemical shift region used as a variable in the multivariate analysis. A similar PLS-DA model could be built for solvent suppressed spectra from grey matter (f). Key: 1. –CH₂CH₃ lipid group, 2. leucine, isoleucine and valine, 3. lactate (sharp doublet) superimposed on –CH₂CH₂CH₂– lipid resonance (broad resonance), 4. alanine, 5. acetate, 6. NAA, 7. glutamate and glutamine, 8. citrate, 9. creatine, 10. choline, 11. phosphocholine, 12. phosphatidylcholine and glycerophosphocholine, 13. taurine, 14. myo-inositol (series of resonances from 3.52 to 3.60). (g) A diagramatic summary of the transcriptional changes identified alongside the metabolomic analysis. Numbers signify the number of transcripts identified as increased or decreased in expression in each group (taken from Prabakaran et al. 2004).