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.coexpression ͉ functional genomics ͉ transcriptomics

SummaryThe transcriptional factor DREB/CBF (dehydration-responsive element/C-repeat-binding) speci®cally interacts with the dehydration-responsive element (DRE)/C-repeat (CRT) cis-acting element (A/GCCGAC) and controls the expression of many stress-inducible genes in Arabidopsis. Transgenic plants overexpressing DREB1A showed activated expression of many stress-inducible genes and improved tolerance to not only drought, salinity, and freezing but also growth retardation. We searched for downstream genes in transgenic plants overexpressing DREB1A using the full-length cDNA microarray and Affymetrix GeneChip array. We con®rmed candidate genes selected by array analyses using RNA gel blot and identi®ed 38 genes as the DREB1A downstream genes, including 20 unreported new downstream genes. Many of the products of these genes were proteins known to function against stress and were probably responsible for the stress tolerance of the transgenic plants. The downstream genes also included genes for protein factors involved in further regulation of signal transduction and gene expression in response to stress. The identi®ed genes were classi®ed into direct downstream genes of DREB1A and the others based on their expression patterns in response to cold stress. We also searched for conserved sequences in the promoter regions of the direct downstream genes and found A/GCCGACNT in their promoter regions from À51 to À450 as a consensus DRE. The recombinant DREB1A protein bound to A/GCCGACNT more ef®ciently than to A/GCCGACNA/G/C.

Although numerous physiological studies have addressed the interactions between brassinosteroids and auxins, little is known about the underlying molecular mechanisms. Using an Affymetrix GeneChip representing approximately 8,300 Arabidopsis genes, we studied comprehensive transcript profiles over 24 h in response to indole-3-acetic acid (IAA) and brassinolide (BL). We identified 409 genes as BL inducible, 276 genes as IAA inducible, and 637 genes in total. These two hormones regulated only 48 genes in common, suggesting that most of the actions of each hormone are mediated by gene expression that is unique to each. IAA-up-regulated genes were enriched in genes regulated in common. They were induced quickly by IAA and more slowly by BL, suggesting divergent physiological roles. Many were early auxin-inducible genes and their homologs, namely SAUR, GH3, and IAA. The comprehensive comparison also identified IAA-and BL-specific genes, which should help to elucidate the specific actions of each hormone. The identified genes were classified using hierarchical clustering based on the similarity of their responses to the two hormones. Gene classification also allowed us to analyze the frequency of cis-elements. The TGTCTC element, a core element of the previously reported auxin response element, was not enriched in genes specifically regulated by IAA but was enriched in the 59-flanking region of genes up-regulated by both IAA and BL. Such gene classification should be useful for predicting the functions of unknown genes, to understand the roles of these two hormones, and the promoter analysis should provide insight into the interaction of transcriptional regulation by the two hormones.

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.