A metabolomic study in oats (Avena sativa) highlights a drought tolerance mechanism based upon salicylate signalling pathways and the modulation of carbon, antioxidant and photo-oxidative metabolism.

Although a wealth of information is available on the induction of one or several drought-related responses in different species, little is known of how their timing, modulation and crucially integration influence drought tolerance. Based upon metabolomic changes in oat (Avena sativa L.), we have defined key processes involved in drought tolerance. During a time course of increasing water deficit, metabolites from leaf samples were profiled using direct infusion-electrospray mass spectroscopy (DI-ESI-MS) and high-performance liquid chromatography (HPLC) ESI-MS/MS and analysed using principal component analysis (PCA) and discriminant function analysis (DFA). The involvement of metabolite pathways was confirmed through targeted assays of key metabolites and physiological experiments. We demonstrate an early accumulation of salicylic acid (SA) influencing stomatal opening, photorespiration and antioxidant defences before any change in the relative water content. These changes are likely to maintain plant water status, with any photoinhibitory effect being counteracted by an efficient antioxidant capacity, thereby representing an integrated mechanism of drought tolerance in oats. We also discuss these changes in relation to those engaged at later points, consequence of the different water status in susceptible and resistant genotypes.

High-throughput classification of yeast mutants for functional genomics using metabolic footprinting.

Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.

Separating the inseparable: the metabolomic analysis of plant-pathogen interactions.

Plant-microbe interactions-whether pathogenic or symbiotic-exert major influences on plant physiology and productivity. Analysis of such interactions represents a particular challenge to metabolomic approaches due to the intimate association between the interacting partners coupled with a general commonality of metabolites. We here describe an approach based on co-cultivation of Arabidopsis cell cultures and bacterial plant pathogens to assess the metabolomes of both interacting partners, which we refer to as dual metabolomics.

Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe grisea.

Metabolomic approaches were used to elucidate some key metabolite changes occurring during interactions of Magnaporthe grisea--the cause of rice blast disease--with an alternate host, Brachypodium distachyon. Fourier-transform infrared (FT-IR) spectroscopy provided a high-throughput metabolic fingerprint of M. grisea interacting with the B. distachyon accessions ABR1 (susceptible) and ABR5 (resistant). Principal component-discriminant function analysis (PC-DFA) allowed the differentiation between developing disease symptoms and host resistance. Alignment of projected 'test-set' on to 'training-set' data indicated that our experimental approach produced highly reproducible data. Examination of PC-DFA loading plots indicated that fatty acids were one chemical group that discriminated between responses by ABR1 and ABR5 to M. grisea. To identify these, non-polar extracts of M. grisea-challenged B. distachyon were directly infused into an electrospray ionization mass spectrometer (ESI-MS). PC-DFA indicated that M. grisea-challenged ABR1 and ABR5 were differentially clustered away from healthy material. Subtraction spectra and PC-DFA loadings plots revealed discriminatory analytes (m/z) between each interaction and seven metabolites were subsequently identified as phospholipids (PLs) by ESI-MS-MS. Phosphatidyl glycerol (PG) PLs were suppressed during both resistant and susceptible responses. By contrast, different phosphatidic acid PLs either increased or were reduced during resistance or during disease development. This suggests considerable and differential PL processing of membrane lipids during each interaction which may be associated with the elaboration/suppression of defence mechanisms or developing disease symptoms.