SE46:/DS01
Sample Set Information
| ID | SE46 |
|---|---|
| Title | Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana |
| Description | Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana. In the GC-TOF/MS analysis, we acquired quantitative information regarding over 170 metabolites, which has been analyzed by a novel score (ZMC, z-score of metabolite correlation) describing a characteristic metabolite in terms of correlation. Although the 2 mutants revealed no apparent morphological abnormalities, the overall correlation values in mto1 were much lower than those of the wild-type and tt4 plants, indicating the loss of overall network stability due to the uncontrolled accumulation of methionine. In the tt4 mutant, a new correlation between malate and sinapate was observed although the levels of malate, sinapate, and sinapoylmalate remain unchanged, suggesting an adaptive reconfiguration of the network. Gene-expression correlations presumably responsible for these metabolic networks were determined using the metabolite correlations as clues. Two Arabidopsis mutants, mto1 and tt4, exhibited the following changes in entire metabolome networks: the overall loss of metabolic stability (mto1) or the generation of a metabolic network of a backup pathway for the lost physiological functions (tt4). The expansion of metabolite correlation to gene-expression correlation provides detailed insights into the systemic understanding of the plant cellular process regarding metabolome and transcriptome. |
| Authors | Miyako Kusano, Atsushi Fukushima, Masanori Arita, Par Jonsson, Thomas Moritz, Makoto Kobayashi, Naomi Hayashi, Takayuki Tohge, Kazuki Saito, RIKEN PSC |
| Reference | Kusano, Fukushima et al. (2007) BMC Syst Biol 1:53 (PMID: 18028551) |
| Comment |
Related data are deposited in MetaboLights.
Data Analysis Details Information
| ID | DS01 |
|---|---|
| Title | Data processing (GC-MS) |
| Description | Processing of GC-TOF/MS data Nonprocessed MS data from GC-TOF/MS analysis were exported in NetCDF format to MATLAB 6.5 (Mathworks, Natick, MA, USA), where all data-pretreatment procedures, such as smoothing, alignment, time-window setting, and H-MCR, were carried out (Jonsson et al. 2006). The resolved MS spectra were matched against reference mass spectra using the NIST mass spectral search program for the NIST/EPA/NIH mass spectral library (version 2.0) and our custom software for peak annotation written in JAVA. Peaks were identified or annotated based on retention indices (RIs) and the reference mass spectra comparison to the Golm Metabolome Database (GMD) (Kopka et al. 2005) and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and with those of authentic standards, and the metabolites were defined as annotated metabolites on comparison with mass spectra and RIs from these two libraries. The amount of S-adenosyl-methionine was calculated by the sum of the mass numbers at m/z 188 and 236 using Leco ChromaTOF software version 2.32 (LECO, St. Joseph, MI, USA) since this compound was not adequately detected by H-MCR. |
| Comment_of_details |
