OPEN ACCESS ARTICLE

This Article Free via Open Access: OA OA Full Text Full Text (PDF) Supplemental Data OA All Versions of this Article: 143/4/1484    most recent pp.106.090795v1 Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (2) Citing Articles via Google Scholar Google Scholar Articles by Messerli, G. Articles by Zeeman, S. C. Search for Related Content PubMed PubMed Citation Articles by Messerli, G. Articles by Zeeman, S. C. Agricola Articles by Messerli, G. Articles by Zeeman, S. C.

BIOINFORMATICS

Rapid Classification of Phenotypic Mutants of Arabidopsis via Metabolite Fingerprinting^1,[W],[OA]

Institute of Plant Sciences, Eidgenössische Technische Hochschule Zurich, CH–8092 Zurich, Switzerland (G.M., S.C.Z.); Ecole Polytechnique Fédérale de Lausanne, CH–1015 Lausanne, Switzerland (V.P.N., A.C.D.); Institute of Plant Sciences, University of Bern, CH–3013 Bern, Switzerland (M.T.); Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany (A.K., N.S., P.G., A.R.F.); and Institute of Molecular Biology, Academia Sinica, Taipei 115, Taiwan (J.C.)

We evaluated the application of gas chromatography-mass spectrometry metabolic fingerprinting to classify forward genetic mutants with similar phenotypes. Mutations affecting distinct metabolic or signaling 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 (Arabidopsis thaliana) mutants defective in starch metabolism and uncharacterized 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 uncharacterized mutants confirmed that those clustering with known mutants were genuinely defective in starch metabolism. A mutant that 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 that can rapidly classify forward genetic mutants and streamline the process of gene discovery.

² Present address: Center for Integrative Genomics, CH–1015 Lausanne, Switzerland.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Samuel C. Zeeman (szeeman{at}ethz.ch).

^[W] The online version of this article contains Web-only data.

^[OA] Open Access articles can be viewed online without a subscription.

www.plantphysiol.org/cgi/doi/10.1104/pp.106.090795

^* Corresponding author; e-mail szeeman{at}ethz.ch; fax 41–44–632–1044.

Received October 3, 2006; accepted January 28, 2007; published February 2, 2007.

This article has been cited by other articles:

				Y. Lu, L. J. Savage, I. Ajjawi, K. M. Imre, D. W. Yoder, C. Benning, D. DellaPenna, J. B. Ohlrogge, K. W. Osteryoung, A. P. Weber, et al. New Connections across Pathways and Cellular Processes: Industrialized Mutant Screening Reveals Novel Associations between Diverse Phenotypes in Arabidopsis Plant Physiology, April 1, 2008; 146(4): 1482 - 1500. [Abstract] [Full Text] [PDF]