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/.
Highlights
Atsushi Fukushima*, Miyako Kusano, Ramon Francisco Mejia, Mami Iwasa, Makoto Kobayashi, Naomi Hayashi, Akiko Watanabe-Takahashi, Tomoko Narisawa, Takayuki Tohge, Manhoi Hur, Eve Syrkin Wurtele, Basil J
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)
We focused on 180 Arabidopsis mutants with altered metabolite levels; they were obtained from the Arabidopsis Biological Resource Center
Summary
Atsushi Fukushima*, Miyako Kusano, Ramon Francisco Mejia, Mami Iwasa, Makoto Kobayashi, Naomi Hayashi, Akiko Watanabe-Takahashi, Tomoko Narisawa, Takayuki Tohge, Manhoi Hur, Eve Syrkin Wurtele, Basil J. 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). They facilitated study of the coordination of carbon and nitrogen metabolism in plants (Stitt and Fernie, 2003; Rubin et al, 2009; Pracharoenwattana et al, 2010; Kusano et al, 2011a; Amiour et al, 2012) and studies to gain a better understanding of regulatory networks involved in genetic perturbation (Roessner et al, 2001; Weckwerth et al, 2004; Tohge et al, 2005), to characterize diurnal/circadian behaviors (Urbanczyk-Wochniak et al, 2005; Gibon et al, 2006; Fukushima et al, 2009a; Espinoza et al, 2010; Hoffman et al, 2010), to assess altered regulatory responses to various abiotic stresses (Kaplan et al, 2004; Urano et al, 2009; Caldana et al, 2011; Kusano et al, 2011b; Maruyama et al, 2014; Nakabayashi et al, 2014), and to identify comprehensive metabolite quantitative trait loci (Morreel et al, 2006; Schauer et al, 2006; Carreno-Quintero et al, 2012; Matsuda et al, 2012)
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