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Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based metabolite profiling

Abstract

The full-length cDNA over-expressing (FOX) gene hunting system is useful for genome-wide gain-of-function analysis. The screening of FOX lines requires a high-throughput metabolomic method that can detect a wide range of metabolites. Fourier transform-near-infrared (FT-NIR) spectroscopy in combination with the chemometric approach has been used to analyze metabolite fingerprints. Since FT-NIR spectroscopy can be used to analyze a solid sample without destructive extraction, this technique enables untargeted analysis and high-throughput screening focusing on the alteration of metabolite composition. We performed non-destructive FT-NIR-based fingerprinting to screen seed samples of 3000 rice-Arabidopsis FOX lines; the samples were obtained from transgenic Arabidopsis thaliana lines that overexpressed rice full-length cDNA. Subsequently, the candidate lines exhibiting alteration in their metabolite fingerprints were analyzed by gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) in order to assess their metabolite profiles. Finally, multivariate regression using orthogonal projections to latent structures (O2PLS) was used to elucidate the predictive metabolites obtained in FT-NIR analysis by integration of the datasets obtained from FT-NIR and GC-TOF/MS analyses. FT-NIR-based fingerprinting is a technically efficient method in that it facilitates non-destructive analysis in a high-throughput manner. Furthermore, with the integrated analysis used here, we were able to discover unique metabotypes in rice-Arabidopsis FOX lines; thus, this approach is beneficial for investigating the function of rice genes related to metabolism.

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Acknowledgments

We are grateful to Dr. Masaki Mori (National Institute of Agrobiological Sciences) for providing information of pathogen resistance study. We thank Dr. Henning Redestig for discussing the manuscript and helping us improve it; and Mr. Tetsuya Sakurai (RIKEN Plant Science Center) for the management of the FOX screening dataset. We also thank Ms. Makiko Takamune, Ms. Kazue Nakabayashi, and Ms. Yoko Suzuki (RIKEN Plant Science Center) for providing technical assistance. This work was supported by the Special Coordination Fund for Promoting Science and Technology (Japan Science and Technology Agency).

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Correspondence to Kazuki Saito.

Electronic supplementary material

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Discrimination of re-transformants using OPLS-DA based on GC-TOF/MS. OPLS-DA was performed using the GC-TOF/MS dataset for 7 candidate lines that showed unique metabolite fingerprints. The plot of the predictive component (tP) versus orthogonal component 1 (tO) is presented. The black symbols represent the wild type, while the red symbols represent re-transformants. Each symbol indicates an individual transgenic line. Candidates 1–6 showed a difference in metabolite profiles compared with those of the wild type without overfitting. (PPT 110 kb)

Predictive metabolites in the O2PLS model. FDR following CV-ANOVA was used to test significance (α = 0.05). We calculated the p-value of CV-ANOVA and the q-value for FDR, R2Y, and Q2Y. A total of 21 metabolites were determined to be significantly predictive in the O2PLS model. (XLS 2367 kb)

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Suzuki, M., Kusano, M., Takahashi, H. et al. Rice-Arabidopsis FOX line screening with FT-NIR-based fingerprinting for GC-TOF/MS-based metabolite profiling. Metabolomics 6, 137–145 (2010). https://doi.org/10.1007/s11306-009-0182-2

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Keywords

  • FT-NIR
  • GC-TOF/MS
  • FOX hunting system
  • Rice
  • Arabidopsis
  • Metabolomics
  • O2PLS