Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis. Issue 3 (14th May 2014)

• Record Type: Journal Article
• Authors: Fukushima, Atsushi; Kusano, Miyako; Mejia, Ramon Francisco; Iwasa, Mami; Kobayashi, Makoto; Hayashi, Naomi; Watanabe-Takahashi, Akiko; Narisawa, Tomoko; Tohge, Takayuki; Hur, Manhoi; Wurtele, Eve Syrkin; Nikolau, Basil J.; Saito, Kazuki
• Is Part Of: Plant physiology. Volume 165:Issue 3(2014)
• Page Start: 948
• Page End: 961
• Publication Date: 2014-05-14
• Subjects: Plant physiology -- Periodicals; Botany -- Periodicals; Periodicals; Electronic journals; 571.2
• DOI: 10.1104/pp.114.240986
• Languages: English
• ISSNs: 0032-0889

Abstract

Abstract : Metabolomic characterization of 50 Arabidopsis mutants and the database construction offers a functional genomics tool . Abstract: Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis ( Arabidopsis thaliana ) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO ). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/ .