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Published on February 2, 2007; 10.1104/pp.106.090795

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Received October 3, 2006
Accepted January 28, 2007

Rapid Classification of Phenotypic Mutants of Arabidopsis Via Metabolite Fingerprinting

Gaëlle Messerli , Vahid Partovi Nia , Martine Trevisan , Anna Kolbe , Nicolas Schauer , Peter Geigenberger , Jychian Chen , Anthony C. Davison , Alisdair R. Fernie , and Samuel C. Zeeman *

Institute of Plant Sciences, ETH Zurich, CH-8092 Zurich, Switzerland; Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; Institute of Plant Sciences, University of Bern, CH-3013 Bern, Switzerland; Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany; Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan

* Corresponding author; email: szeeman{at}ethz.ch.

We evaluated the application of GC-MS metabolic fingerprinting to classify forward genetic mutants with similar phenotypes. Mutations affecting distinct metabolic or signalling pathways can result in common phenotypic traits that are used to identify mutants in genetic screens. Measurement of a broad range of metabolites provides information about the underlying processes affected in such mutants. Metabolite profiles of Arabidopsis thaliana mutants defective in starch metabolism and uncharacterised mutants displaying a starch-excess phenotype were compared. Each genotype displayed a unique fingerprint. Statistical methods grouped the mutants robustly into distinct classes. Determining the genes mutated in three uncharacterised mutants confirmed that those clustering with known mutants were genuinely defective in starch metabolism. A mutant which clustered away from the known mutants was defective in the circadian clock and had a pleiotropic starch-excess phenotype. These results indicate that metabolic fingerprinting is a powerful tool which can rapidly classify forward genetic mutants and streamline the process of gene discovery.




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