Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis    

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/.

Moving Toward a Comprehensive Map of Central Plant Metabolism

“…Reverse genetics still suffers from the complex interactions between growth conditions and phenotypes, the recessive nature of many mutations, and the possibility of redundancy (including in polyploids), requiring the use of double or triple mutants to reveal phenotypes (44). The move toward archiving metabolic phenotypes of many mutations in a directly comparable manner, such as the database under development for Arabidopsis (49), will help resolve these issues through sharing of relevant data between groups.…”

Integrated omics analysis of specialized metabolism in medicinal plants

“…The development of open source bioinformatics tools including XCMS (Smith et al ., ; Mahieu et al ., ), MetAlign (Lommen, ), MZmine2 (Pluskal et al ., ), MS‐Dial (Tsugawa et al ., ), MS‐FINDER (Tsugawa et al ., ), Mass++ (Tanaka et al ., ), MET‐IDEA (Broeckling et al ., ) and DrDmassPlus (Oikawa et al ., ) allow automation of mass‐feature extraction, peak alignment across different samples and the identification/quantification of each metabolite. Recently, a number of resource databases have been made available, such as AtMetExpress (Matsuda et al ., ), Medicinal Plant Metabolomics Resources (MPMR) (http://metnetweb.gdcb.iastate.edu/mpmr_public/), MetabolomeExpress (Carroll et al ., ), the Metabolite Profiling database for knock‐out mutants in Arabidopsis (MeKO) (Fukushima et al ., ), the Plant Metabolite Network (PMN) (Dreher, ), PlantDB (Exner et al ., ), the KNApSACK database (Afendi et al ., ), PRIMe (a web‐based service that provides datasets of metabolites measured by multidimensional NMR spectroscopy, gas chromatography‐MS, liquid chromatography‐MS and capillary electrophoresis‐MS together with an analytical tool) (Akiyama et al ., ), MeRy‐B (Deborde and Jacob, ), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and KEGG‐plant, METLIN (Smith et al ., ) and MassBank (Horai et al ., ). MPMR represents one of the earliest efforts to build a comprehensive metabolite database focusing on specialized metabolites of medicinal plants, and currently consists of metabolome dataset from 14 plant species (but aiming to include more medicinal plant species in the future).…”

Updates in metabolomics tools and resources: 2014–2015

“…MeKO database and software suite provides a platform for evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants from the GC‐MS datasets, data on differences in metabolite accumulation, and a host of interactive statistical analysis tools (Fig. ) . MassCascade is an open‐source library for LC‐MS data processing where the available functions have been encapsulated in a plug‐in for the workflow environment Konstanz Information Miner, allowing combined use with other statistical workflow nodes .…”