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Technology Report ARTICLE

Front. Plant Sci., 10 February 2012 | http://dx.doi.org/10.3389/fpls.2012.00015

Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”

Stephanie M. Quanbeck1, Libuse Brachova1, Alexis A. Campbell1, Xin Guan1, Ann Perera1, Kun He2, Seung Y. Rhee2, Preeti Bais3, Julie A. Dickerson3, Philip Dixon4, Gert Wohlgemuth5, Oliver Fiehn5, Lenore Barkan6, Iris Lange6, B. Markus Lange6, Insuk Lee7, Diego Cortes8, Carolina Salazar9, Joel Shuman10, Vladimir Shulaev9, David V. Huhman11, Lloyd W. Sumner11, Mary R. Roth12, Ruth Welti12, Hilal Ilarslan13, Eve S. Wurtele13 and Basil J. Nikolau1*
  • 1 Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA, USA
  • 2 Department of Plant Biology, Carnegie Institution for Science, Stanford, CA, USA
  • 3 Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA
  • 4 Department of Statistics, Iowa State University, Ames, IA, USA
  • 5 Genome Center, University of California, Davis, CA, USA
  • 6 M. J. Murdock Metabolomics Laboratory, Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
  • 7 Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea
  • 8 Anatomy and Neurobiology, Virginia Commonwealth University, Richmond, VA, USA
  • 9 Department of Biological Sciences, University of North Texas, Denton, TX, USA
  • 10 Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
  • 11 Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK, USA
  • 12 Division of Biology, Kansas State University, Manhattan, KS, USA
  • 13 Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, USA

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs.

Keywords: Arabidopsis, metabolomics, gene annotation, functional genomics, database

Citation: Quanbeck SM, Brachova L, Campbell AA, Guan X, Perera A, He K, Rhee SY, Bais P, Dickerson JA, Dixon P, Wohlgemuth G, Fiehn O, Barkan L, Lange I, Lange BM, Lee I, Cortes D, Salazar C, Shuman J, Shulaev V, Huhman DV, Sumner LW, Roth MR, Welti R, Ilarslan H, Wurtele ES and Nikolau BJ (2012) Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”. Front. Plant Sci. 3:15. doi: 10.3389/fpls.2012.00015

Received: 18 October 2011; Accepted: 17 January 2012;
Published online: 10 February 2012.

Edited by:

Roger Deal, Emory University, USA

Reviewed by:

Kazuki Saito, Chiba University, Japan
Adrian Hegeman, University of Minnesota, USA
Alisdair Fernie, Max Planck Institut for Plant Physiology, Germany

Copyright: © 2012 Quanbeck, Brachova, Campbell, Guan, Perera, He, Rhee, Bais, Dickerson, Dixon, Wohlgemuth, Fiehn, Barkan, Lange, Lange, Lee, Cortes, Salazar, Shuman, Shulaev, Huhman, Sumner, Roth, Welti, Ilarslan, Wurtele and Nikolau. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

*Correspondence: Basil J. Nikolau, Iowa State University, 3254 Molecular Biology Building, Ames, IA 50011, USA. e-mail: dimmas@iastate.edu