Abstract
Metabolic profiles and fingerprints of Arabidopsis thaliana plants with various defects in plastidic sugar metabolism or photosynthesis were analyzed to elucidate if the genetic mutations can be traced by comparing their metabolic status. Using a platform of chromatographic and spectrometric tools data from untargeted full MS scans as well as from selected metabolites including major carbohydrates, phosphorylated intermediates, carboxylates, free amino acids, major antioxidants, and plastidic pigments were evaluated. Our key observations are that by multivariate statistical analysis each mutant can be separated by a unique metabolic signature. Closely related mutants come close. Thus metabolic profiles of sugar mutants are different but more similar than those of photosynthesis mutants. All mutants show pleiotropic responses mirrored in their metabolic status. These pleiotropic responses are typical and can be used for separating and grouping of the mutants. Our findings show that metabolite fingerprints can be taken to classify mutants and hence may be used to sort genes into functional groups.
Highlights
30% of the genes of the model organism Arabidopsis thaliana have unknown functions and only 22% are experimentally characterized according to the October 2011 The Arabidopsis information resource (TAIR) statistics (Brown et al, 2005; Clare et al, 2006; Swarbreck et al, 2008)
CULTIVATION AND HARVEST Seeds of Arabidopsis mutants were obtained from the Nottingham Arabidopsis Stock Centre (NASC, Nottingham, UK), and all of the mutant plants were identified by polymerase chain reaction (PCR) and confirmed by reverse transcription (RT)
The triosephosphate/phosphate translocator mutant tpt1 and a fourth photosynthesis complex I (PSI) mutant psaE2 propagated in ecotype Ws displayed phenotypes looking hardly different from their wildtypes Col0 and Wassilewskija, respectively
Summary
30% of the genes of the model organism Arabidopsis thaliana have unknown functions and only 22% are experimentally characterized according to the October 2011 TAIR statistics (Brown et al, 2005; Clare et al, 2006; Swarbreck et al, 2008). Even in the best-studied organism E. coli it is unclear what 40% of the genes are doing (Tohsato et al, 2010) Such deficiencies along with the perspective of the rapidly increasing number of sequenced genomes underline the need to assign functions to unknown genes. The forward strategy of profiling genetic mutants in the context of biochemical pathways harbors the risk that pleiotropic effects mask primary events. We asked with this work if primary events from metabolic profiles can be defined revealing a metabolic signature in spite of secondary changes. One has to consider that mutations outside metabolic pathways might generate pleiotropic effects when analyzed on the basis of changes in metabolism
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