Uncovering the effect of silent plant phenotypes from the combination of multivariate analysis and functional metabolic network analysis

DOI

Bibliographic Information

Other Title
  • GC-TOF/MSを用いたメタボロミクス方法論の確立－多変量解析と相関係数によるfunctional metabolic networks

Abstract

Silent phenotypes are genetically modified organisms that do not show apparent changes in morphology, yield, or growth rates when compared with parental lines under given physiological conditions. They undergo, however, subtle but systematic metabolic changes that are difficult to detect by targeted metabolite analysis. To analyze silent plant phenotypes, we used a double knock-out mutant of Serat genes (serat2;1 serat2;2); the mutant is known to exhibit relatively reduced amount of O-acetyl-L-serine, cysteine, and glutathione, which play major roles in the sulfur assimilation. In our metabolome analysis, 30 replicates of wild-type roots and their 30 mutants were analyzed using GC-TOF/MS. When multivariate analysis was applied to the non-processed GC/MS files (470 peaks in root; 514 peaks in leaf), the PLS-DA score separated the groups of wild-type clearly from the mutants. Lastly, significance of correlations was assessed considering the number of reaction steps that connect the metabolite pairs in the metabolic network.

Journal

Details 詳細情報について

  • CRID
    1390282680605147392
  • NII Article ID
    130006988779
  • DOI
    10.14841/jspp.2006.0.005.0
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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