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 geneto-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. P lants produce a huge array of compounds used for foods, medicines, flavors, and industrial materials. These plant metabolites are synthesized and accumulated by the networks of proteins encoded in the genome of each plant. However, even after the completion of the genome sequencing of Arabidopsis (1) and rice (2, 3), function of those genes and networks of gene-to-metabolite are largely unknown. To reveal the function of genes involved in metabolic processes and gene-to-metabolite networks, the metabolomics-based approach is regarded as a direct way (4-7). In particular, integration of comprehensive gene expression profile with targeted metabolite analysis is shown to be an innovative way for identification of gene function for specific product accumulation in plant (8) and microorganisms (9). However, to depict a whole-cellular process of metabolism, integration of comprehensive gene expression analysis (transcriptomics), and nontargeted metabolite profiling (metabolomics) is needed. Bioinformatics designed suitably for data mining helps the integration efficiently.The gene expression profiling can be achieved by DNA array analysis. For metabolomics, a nontargeted, high-throughput analytical system is required. Traditionally, GC-MS has been used to detect Ͼ300 metabolites in plant tissues (5, 6). Fourier transform-ion cyclotron MS (FT-MS) is a system for metabolome analysis in which crude plant extract is introduced by means of direct injection without prior separation of metabolites by chromatography (10). The mass resolution (Ͼ100,000) and accuracy (Ͻ1 ppm) of FT-MS is extremely high; hence, comple...