Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of “Unknown Function”

Abstract

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 () 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.

Spatial Mapping and Profiling of Metabolite Distributions during Germination

“…These data are consistent with previous studies of these inbreds that identified Mo17 mature seeds having a higher amino acid content than B73 seeds (Römisch-Margl et al, 2010). As the germination process proceeds, these differences in amino acid content between the two inbreds is reduced, so that by 36 h postimbibition, the profiles are almost identical except for Lys, Thr, and Arg, which are still slightly higher in Mo17, and Trp, which is now slightly higher in B73 (Supplemental Fig.…”

“…For those compounds observed only in two data sets, the missing values were replaced by one-third of the estimated minimum value. Log 2 -ratio plots were calculated for each metabolite as described previously (Quanbeck et al, 2012). The raw data and averages and SE calculations were determined from three biological replicates and are available in the PMR database and Supplemental Tables S2 to S4, respectively.…”

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

“…Log 2 ratios and SE were calculated as described previously (Quanbeck et al, 2012). Metabolomic data were deposited in the PMR database (Hur et al, 2013) under accession number mtFAS.…”

Spatially Resolved Plant Metabolomics: Some Potentials and Limitations of Laser-Ablation Electrospray Ionization Mass Spectrometry Metabolite Imaging

“…As an untargeted approach to gain a broad overview of the complexity of plant metabolic composition, the technology has, in a short time, made significant inroads into helping expand our knowledge of plant biochemistry (Kueger et al, 2012;Etalo et al, 2013;Hunerdosse and Nomura, 2014;Meret et al, 2014). Typically, rich metabolomics data sets already provide us with a valuable means to generate hypotheses relating to plant metabolism, which then become the focus of further, more direct investigation (Quanbeck et al, 2012). New technologies are being developed, and especially, new data-mining strategies are being designed to allow us to look deep into plant metabolism without having first to rely on preconceptions.…”