Multiparallel analyses of mRNA and proteins are central to today's functional genomics initiatives. We describe here the use of metabolite profiling as a new tool for a comparative display of gene function. It has the potential not only to provide deeper insight into complex regulatory processes but also to determine phenotype directly. Using gas chromatography/mass spectrometry (GC/MS), we automatically quantified 326 distinct compounds from Arabidopsis thaliana leaf extracts. It was possible to assign a chemical structure to approximately half of these compounds. Comparison of four Arabidopsis genotypes (two homozygous ecotypes and a mutant of each ecotype) showed that each genotype possesses a distinct metabolic profile. Data mining tools such as principal component analysis enabled the assignment of "metabolic phenotypes" using these large data sets. The metabolic phenotypes of the two ecotypes were more divergent than were the metabolic phenotypes of the single-loci mutant and their parental ecotypes. These results demonstrate the use of metabolite profiling as a tool to significantly extend and enhance the power of existing functional genomics approaches.

SummaryA new method is presented in which gas chromatography coupled to mass spectrometry (GC±MS) allows the quantitative and qualitative detection of more than 150 compounds within a potato tuber, in a highly sensitive and speci®c manner. In contrast to other methods developed for metabolite analysis in plant systems, this method represents an unbiased and open approach that allows the detection of unexpected changes in metabolite levels. Although the method represents a compromise for a wide range of metabolites in terms of extraction, chemical modi®cation and GC±MS analysis, for 25 metabolites analysed in detail the recoveries were found to be within the generally accepted range of 70±140%. Further, the reproducibility of the method was high: the error occurring in the analysis procedures was found to be less than 6% for 30 out of 33 compounds tested. Biological variability exceeded the systematic error of the analysis by a factor of up to 10. The method is also suited for upscaling, potentially allowing the simultaneous analysis of a large number of samples. As a ®rst example this method has been applied to soil-and in vitro-grown tubers. Due to the simultaneous analysis of a wide range of metabolites it was immediately apparent that these systems differ signi®cantly in their metabolism. Furthermore, the parallel insight into many pathways allows some conclusions to be drawn about the underlying physiological differences between both tuber systems. As a second example, transgenic lines modi®ed in sucrose catabolism or starch synthesis were analysed. This example illustrates the power of an unbiased approach to detecting unexpected changes in transgenic lines.

The concept of metabolite profiling has been around for several decades, but only recent technical innovations have allowed metabolite profiling to be carried out on a large scale - with respect to both the number of metabolites measured and the number of experiments carried out. As a result, the power of metabolite profiling as a technology platform for diagnostics, and the research areas of gene-function analysis and systems biology, is now beginning to be fully realized.

No abstract

Unknown compounds in polar fractions of Arabidopsis thaliana crude leaf extracts were identified on the basis of calculations of elemental compositions obtained from gas chromatography/low-resolution quadrupole mass spectrometric data. Plant metabolites were methoximated and silylated prior to analysis. All known peaks were used as internal references to construct polynomial recalibration curves of from raw mass spectrometric data. Mass accuracies of 0.005 +/- 0.003 amu and isotope ratio errors of 0.5 +/- 0.3% (A + 1/A), respectively, 0.3 +/- 0.2% (A + 2/A), could be achieved. Both masses and isotope ratios were combined when the elemental compositions of unknown peaks were calculated. After calculation, compound identities were elucidated by searching metabolic databases, interpreting spectra, and, finally, by comparison with reference compounds. Sum formulas of more than 70 peaks were determined throughout single GC/MS chromatograms. Exact masses were confirmed by high-resolution mass spectrometric data. More than 15 uncommon plant metabolites were identified, some of which are novel in Arabidopsis, such as tartronate semialdehyde, citramalic acid, allothreonine, or glycolic amide.