Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana
- Masami Yokota Hirai*,†,
- Mitsuru Yano*,
- Dayan B. Goodenowe‡,
- Shigehiko Kanaya§,
- Tomoko Kimura¶,
- Motoko Awazuhara*,
- Masanori Arita∥,**,
- Toru Fujiwara††,‡‡, and
- Kazuki Saito*,†,§§
- *Department of Molecular Biology and Biotechnology, Graduate School of Pharmaceutical Sciences, Chiba University, Inage-ku, Chiba 263-8522, Japan; †Core Research for Evolutional Science and Technology and ‡‡Precursory Research for Embryonic Science and Technology, Japan Science and Technology Agency, Saitama 332-0012, Japan; ‡Phenomenome Discoveries Inc., 204-407 Downey Road, Saskatoon, SK, Canada S7N 4L8; §Department of Bioinformatics and Genomics, Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara 630-0101, Japan; ¶Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, and ††Biotechnology Research Center, University of Tokyo, Bunkyo-ku, Tokyo 113-8657, Japan; ∥Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba 277-8561, Japan; and **Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Koto-ku, Tokyo 135-0064, Japan
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Communicated by Marc C. E. Van Montagu, Ghent University, Ghent, Belgium, May 12, 2004 (received for review February 6, 2004)
Abstract
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.
Footnotes
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↵ §§ To whom correspondence should be addressed. E-mail: ksaito{at}faculty.chiba-u.jp.
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Abbreviations: FT-MS, Fourier transform-ion cyclotron MS; GLS, glucosinolate; OAS, O-acetyl-l-serine; PCA, principal component analysis; BL-SOM, batch-learning self-organizing map; C, control; -S, S-deficient; -N, N-deficient; -SN, S- and N-deficient; MAM-1, methylthioalkylmalate synthase-1; S-GT, S-glucosyltransferase; NR, nitrate reductase.
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See Commentary on page 9949.
- Copyright © 2004, The National Academy of Sciences
