Poster: Bioinformatics

Abs # 910: Integrated studies of transcriptome and metabolome analyses of Arabidopsis under nutritional stresses

Presenter:	Hirai, Masami Y., myhirai@p.chiba-u.ac.jp
Authors	Hirai, Masami Y. (A) (B)  Yano, Mitsuru  (A)   Kanaya, Shigehiko  (C)   Arita, Masanori  (D)   Fujiwara, Toru  (E)   Goodenowe, Dayan B. (F)   Saito, Kazuki  (A) (B)
Affiliations:	(A): Graduate School of Pharmaceutical Sciences, Chiba Univ. (B): Crest, JST (C): Dept. Bioinformatics and Genomics, NAIST (D): Computational Biology Research Center, AIST (E): Graduate School of Agricultural and Life Sciences, Univ. Tokyo (F): Phenomenome Discoveries Inc.
Web Site:	http://www.p.chiba-u.ac.jp/lab/idenshi/index-e.html

With the completion of the Arabidopsis genome sequence and the improvement of technologies to analyze gene expression and protein accumulation comprehensively, the focus of plant biology have shifted from gene identification to gene function elucidation. The changes in expression and protein profiles result in more complicated change in metabolite accumulation pattern. Hence, metabolic profiling can be a powerful tool to clarify the mechanisms of various phenomena in living cells. Our aims are to analyze non-targeted metabolic profile and to obtain new knowledge by integration of expression and metabolic profiles. Transcripts of 3-week-old S (sulfur)- or N (nitrogen)-starved Arabidopsis were analyzed using DNA macroarray. Global expression profiles of S- and N-starved plants were similar to each other. Different sets of genes were regulated by nutritional stresses in leaves and roots. By statistical analysis and cluster analysis, the genes induced specifically under S or N deficiency were identified. Using the same plant materials, the accumulation of metabolites was analyzed comprehensively. By ultra-high resolution Fourier transform ion cyclotron (FT)-MS, 1,000 to 1,500 mass peaks were detected. We have developed a new program to identify compounds from accurate m/z obtained by FT-MS. Even in case that mass peaks cannot be identified, they are useful as fingerprints and hence global metabolic profile can be analyzed. As is the case with gene expression profiles, metabolic profiles of S- and N-starved plants were similar to each other. By cluster analysis, the metabolites accumulated specifically in S- or N-starved plants were identified. In this presentation we will report what was clarified by the integration of transcriptome and metabolome analyses.