Summary: The integration of metabolomics and transcriptomics can provide precise information on gene‐to‐metabolite networks for identifying the function of unknown genes unless there has been a post‐transcriptional modification. Here, we report a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over‐expressing the PAP1 gene encoding an MYB transcription factor, for the identification of novel gene functions involved in flavonoid biosynthesis. For metabolome analysis, we performed flavonoid‐targeted analysis by high‐performance liquid chromatography‐mass spectrometry and non‐targeted analysis by Fourier‐transform ion‐cyclotron mass spectrometry with an ultrahigh‐resolution capacity. This combined analysis revealed the specific accumulation of cyanidin and quercetin derivatives, and identified eight novel anthocyanins from an array of putative 1800 metabolites in PAP1 over‐expressing plants. The transcriptome analysis of 22 810 genes on a DNA microarray revealed the induction of 38 genes by ectopic PAP1 over‐expression. In addition to well‐known genes involved in anthocyanin production, several genes with unidentified functions or annotated with putative functions, encoding putative glycosyltransferase, acyltransferase, glutathione S‐transferase, sugar transporters and transcription factors, were induced by PAP1. Two putative glycosyltransferase genes (At5g17050 and At4g14090) induced by PAP1 expression were confirmed to encode flavonoid 3‐O‐glucosyltransferase and anthocyanin 5‐O‐glucosyltransferase, respectively, from the enzymatic activity of their recombinant proteins in vitro and results of the analysis of anthocyanins in the respective T‐DNA‐inserted mutants. The functional genomics approach through the integration of metabolomics and transcriptomics presented here provides an innovative means of identifying novel gene functions involved in plant metabolism.

Understanding plant metabolism as an integrated system is essential for metabolic engineering aimed at the effective production of compounds useful to human life and the global environment. The “omics” approach integrates transcriptome and metabolome data into a single data set and can lead to the identification of unknown genes and their regulatory networks involved in metabolic pathways of interest. One of the intriguing, although poorly described metabolic pathways in plants is the biosynthesis of glucosinolates (GSLs), a group of bioactive secondary products derived from amino acids that are found in the family Brassicaceae. Here we report the discovery of two R2R3-Myb transcription factors that positively control the biosynthesis of GSLs in Arabidopsis thaliana by an integrated omics approach. Combined transcriptome coexpression analysis of publicly available, condition-independent data and the condition-specific (i.e., sulfur-deficiency) data identified Myb28 and Myb29 as candidate transcription factor genes specifically involved in the regulation of aliphatic GSL production. Analysis of a knockout mutant and ectopic expression of the gene demonstrated that Myb28 is a positive regulator for basal-level production of aliphatic GSLs. Myb29 presumably plays an accessory function for methyl jasmonate-mediated induction of a set of aliphatic GSL biosynthetic genes. Overexpression of Myb28 in Arabidopsis -cultured suspension cells, which do not normally synthesize GSLs, resulted in the production of large amounts of GSLs, suggesting the possibility of efficient industrial production of GSLs by manipulation of these transcription factors. A working model for regulation of GSL production involving these genes, renamed Production of Methionine-Derived Glucosinolate ( PMG ) 1 and 2 , are postulated.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all “omics” sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis , with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Since the completion of genome sequences of model organisms, functional identification of unknown genes has become a principal challenge in biology. Post-genomics sciences such as transcriptomics, proteomics, and metabolomics are expected to discover gene functions. This report outlines the elucidation of gene-to-gene and metabolite-to-gene networks via integration of metabolomics with transcriptomics and presents a strategy for the identification of novel gene functions. Metabolomics and transcriptomics data of Arabidopsis grown under sulfur deficiency were combined and analyzed by batch-learning self-organizing mapping. A group of metabolites/genes regulated by the same mechanism clustered together. The metabolism of glucosinolates was shown to be coordinately regulated. Three uncharacterized putative sulfotransferase genes clustering together with known glucosinolate biosynthesis genes were candidates for involvement in biosynthesis. In vitro enzymatic assays of the recombinant gene products confirmed their functions as desulfoglucosinolate sulfotransferases. Several genes involved in sulfur assimilation clustered with O-acetylserine, which is considered a positive regulator of these genes. The genes involved in anthocyanin biosynthesis clustered with the gene encoding a transcriptional factor that up-regulates specifically anthocyanin biosynthesis genes. These results suggested that regulatory metabolites and transcriptional factor genes can be identified by this approach, based on the assumption that they cluster with the downstream genes they regulate. This strategy is applicable not only to plant but also to other organisms for functional elucidation of unknown genes.

Summary: We analyzed global gene expression in Arabidopsis in response to various hormones and in related experiments as part of the AtGenExpress project. The experimental agents included seven basic phytohormones (auxin, cytokinin, gibberellin, brassinosteroid, abscisic acid, jasmonate and ethylene) and their inhibitors. In addition, gene expression was investigated in hormone‐related mutants and during seed germination and sulfate starvation. Hormone‐inducible genes were identified from the hormone response data. The effects of each hormone and the relevance of the gene lists were verified by comparing expression profiles for the hormone treatments and related experiments using Pearson’s correlation coefficient. This approach was also used to analyze the relationships among expression profiles for hormone responses and those included in the AtGenExpress stress‐response data set. The expected correlations were observed, indicating that this approach is useful to monitor the hormonal status in the stress‐related samples. Global interactions among hormones‐inducible genes were analyzed in a pairwise fashion, and several known and novel hormone interactions were detected. Genome‐wide transcriptional gene‐to‐gene correlations, analyzed by hierarchical cluster analysis (HCA), indicated that our data set is useful for identification of clusters of co‐expressed genes, and to predict the functions of unknown genes, even if a gene’s function is not directly related to the experiments included in AtGenExpress. Our data are available online from AtGenExpressJapan; the results of genome‐wide HCA are available from PRIMe. The data set presented here will be a versatile resource for future hormone studies, and constitutes a reference for genome‐wide gene expression in Arabidopsis.