METABOLOMICS.......???

Metabolomic involves the systematic estimation of small molecules from a range of organisms, followed by statistical analyses and other investigations of that large quantity of data.

The draft sequences for the human genome has focused attention on the tremendous effort still required to understand the function of expressed genes, and the way in which genes and the proteins they encode interact within cells and organisms. Metabolomics, or the global analysis of cellular metabolites, provides a powerful new tool for gaining insight into functional biology. Snapshots of the level of small molecules within a cell, and how those levels change under different conditions, are complementary to gene expression and proteomic studies, and are actively being applied to studies of infectious diseases, production and model organisms, as well as human cells and plants.

It requires analytical techniques such as chromatography, molecular spectroscopy and mass spectrometry, coupled with multivariate data analysis methods. The aim of metabolomics is to obtain the widest possible coverage, in terms of the type and number of compounds analysed. This is achieved by making use of several, complementary analytical methods.

For target compound analysis and metabolic profiling, main techniques are gas chromatography (GC), high performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR). These approaches rely on chromatographic separations, often coupled with well-developed calibrations for specific analytes.

In metabolic fingerprinting, samples are analysed as crude extracts without any separation step, using NMR, direct injection mass spectrometry (MS), or Fourier transform infrared (FT-IR) spectroscopy. These fingerprinting approaches are often combined with multivariate analysis, to get the most out of the data.

Gas Chromatography (GC)

Developments involving gas chromatography have been responsible for the recent upsurge of interest in plant metabolomics. GC provides high-resolution compound separations, and can be used in conjunction with a flame ionisation detector (GC/FID) or a mass spectrometer (GC/MS). Both detection methods are highly sensitive and universal, able to detect almost any organic compound, regardless of its class or structure. However, most of the metabolites found in plant extracts are too involatile to be analysed directly by GC methods. The compounds have to be converted to less polar, more volatile derivatives before they are applied to the GC column. Efficient derivatisation methods are available, but relatively low sample throughput is a drawback of the GC method, particularly when there are many samples to be examined.

High Performance Liquid Chromatography (HPLC)

HPLC, with UV detection, is probably the most common method used for targeted analysis of plant materials, and for metabolic profiling of individual classes. A derivatisation step is not essential (unless needed for detection), since involatile and volatile substances may be measured equally well. Selection of compounds arises initially from the type of solvent used for extraction (as with all methods that use an extraction step), and then from the type of column and detector. For example HPLC/UV will only detect compounds with a suitable chromophore; a column selected for its ability to separate one class of compounds will not generally be useful for other types. HPLC profiling methods all rely to a great extent on comparisons with reference compounds. The full UV spectrum (measured for each peak when UV-diode array detectors are used) gives some useful information on the nature of compounds in complex profiles, but often indicates the class of the compound rather than its exact identity.

Nuclear Magnetic Resonance (NMR)

In principle, proton (¹H) NMR can detect any metabolites containing hydrogen. Signals can be assigned by comparison with libraries of reference compounds, or by two-dimensional NMR. ¹H NMR spectra of plant extracts are inevitably crowded not only because there is a large number of contributing compounds, but also because of the low overall chemical shift dispersion. ¹H spectra are also complicated by spin-spin couplings which add to signal multiplicity, although they are an important source of structural information. In ¹³C NMR, the chemical shift dispersion is twenty times greater and spin-spin interactions are removed by decoupling. Despite these advantages, the low sensitivity of ¹³C NMR prevents its routine use with complex extracts. Sensitivity can be enhanced when seedlings are grown in the presence of ¹³C enriched carbon dioxide, but this is obviously only an option for laboratory based studies.

Direct Injection Mass Spectometry

It is also possible to obtain metabolite 'mass profiles' without any chromatographic separation. Such profiles are obtained by injecting crude extracts into the electrospray ionisation source of a high-resolution mass spectrometer. This technique generates mainly protonated, deprotonated or adduct molecules, such as [M+H]⁺, [M+cation]⁺ or [M-H]^- for each species present in the mixture, with little or no fragmentation. Thus a fingerprint spectrum is obtained with a single peak for each metabolite, separated from other metabolites according to (accurate) molecular mass. The fingerprint can be used as a classification tool, for example in taxonomy. Some mass analysers are capable of ultra-high resolution and permit the mass to be determined to four or five decimal places. This allows unique formulae to be assigned to peaks with masses of a few hundred or so. The coupling of high sensitivity with high resolution provides a method of determining a rough estimate of the number of metabolites present and a valuable first indication, from the formulae, of their identities. Its main weakness is the inability to separate isomers of the same molecular mass.

Multivariate Analysis

Plant extracts are very complex in composition and, if many samples are examined, it is difficult to make meaningful comparisons of large numbers of spectra or chromatograms 'by eye'. Multivariate statistical methods can be extremely useful, as they are able to compress data into a more easily managed form. This can assist in visualizing, for example, how a given sample relates to other samples - a central issue in metabolomics. Multivariate analysis is practically essential in the fingerprinting approaches, but is also helpful in techniques where individual metabolites are explicitly quantified (eg GC/MS).

Principal component analysis (PCA) is a well-known and effective method of data compression. PCA transforms the original data (e.g. intensity values in a spectrum) into a set of 'scores' for each sample, measured with respect to the principal component axes ('loadings'). The PC scores replace the original variates, and are: (i) ordered, with successive PCs accounting for decreasing amounts of variance, and (ii) orthogonal, with no correlation between the scores on different axes. Due to these properties, a small number of PCs can replace the many original variates without much loss of information.

Scatter plots of the scores on the first few PC loadings provide an excellent means of visualizing and summarising the data and often reveal patterns that cannot be discerned in the original data. The scores plots may show clustering of similar samples, separation of different sample types, or the presence of outliers. Plots of the loadings themselves may be used to explore which compounds are most responsible for, say, separating samples into groups: the most important compounds (peaks) tend to correspond to high absolute loading values.