Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Allwood, J. W., Ellis, D. I., Heald, J. K., Goodacre, R., & Mur, L. A. (2006). Metabolomic approaches reveal that phosphatidic and phosphatidyl glycerol phospholipids are major discriminatory non-polar metabolites in responses by Brachypodium distachyon to challenge by Magnaporthe grisea. Plant Journal, 46, 351–368.

    Article  CAS  PubMed  Google Scholar 

  • Bauer, S., Vasu, P., Persson, S., Mort, A. J., & Somerville, C. R. (2006). Development and application of a suite of polysaccharide-degrading enzymes for analyzing plant cell walls. Proceedings of the National Academy of Sciences of the United States of America, 103, 11417–11422.

    Article  CAS  PubMed  Google Scholar 

  • Bylesjo, M., Eriksson, D., Kusano, M., Moritz, T., & Trygg, J. (2007). Data integration in plant biology: The O2PLS method for combined modeling of transcript and metabolite data. Plant Journal, 52, 1181–1191.

    Article  PubMed  CAS  Google Scholar 

  • Bylesjo, M., Nilsson, R., Srivastava, V., et al. (2009). Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen. Journal of Proteome Research, 8, 199–210.

    Article  PubMed  CAS  Google Scholar 

  • Clough, S. J., & Bent, A. F. (1998). Floral dip: A simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant Journal, 16, 735–743.

    Article  CAS  PubMed  Google Scholar 

  • Ellis, D. I., Dunn, W. B., Griffin, J. L., Allwood, J. W., & Goodacre, R. (2007). Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics, 8, 1243–1266.

    Article  CAS  PubMed  Google Scholar 

  • Eriksson, L., Trygg, J., & Wold, S. (2008). CV-ANOVA for significance testing of PLS and OPLS® models. Journal of Chemometrics, 22, 594–600.

    Article  CAS  Google Scholar 

  • Fiehn, O. (2002). Metabolomics—the link between genotypes and phenotypes. Plant Molecular Biology, 48, 155–171.

    Article  CAS  PubMed  Google Scholar 

  • Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000). Metabolite profiling for plant functional genomics. Nature Biotechnology, 18, 1157–1161.

    Article  CAS  PubMed  Google Scholar 

  • Geladi, P., Macdougall, D., & Martens, H. (1985). Linearization and scatter-correction for near-infrared reflectance spectra of meat. Applied Spectroscopy, 39, 491–500.

    Article  Google Scholar 

  • Gojobori, T. (2007). Curated genome annotation of Oryza sativa ssp japonica and comparative genome analysis with Arabidopsis thaliana—The Rice Annotation Project. Genome Research, 17, 175–183.

    Article  PubMed  Google Scholar 

  • Grata, E., Boccard, J., Guillarme, D., et al. (2008). UPLC-TOF-MS for plant metabolomics: A sequential approach for wound marker analysis in Arabidopsis thaliana. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 871, 261–270.

    Article  CAS  PubMed  Google Scholar 

  • Hall, R. D. (2006). Plant metabolomics: From holistic hope, to hype, to hot topic. The New Phytologist, 169, 453–468.

    Article  CAS  PubMed  Google Scholar 

  • Hall, J. W., & Pollard, A. (1992). Near-infrared spectrophotometry: A new dimension in clinical chemistry. Clinical Chemistry, 38, 1623–1631.

    CAS  PubMed  Google Scholar 

  • Hotelling, H. (1931). The generalization of Student’s ratio. Annals of Mathematical Statistics, 2, 360–378.

    Article  Google Scholar 

  • Ichikawa, T., Nakazawa, M., Kawashima, M., et al. (2006). The FOX hunting system: An alternative gain-of-function gene hunting technique. Plant Journal, 48, 974–985.

    Article  CAS  PubMed  Google Scholar 

  • Ikeda, T., Kanaya, S., Yonetani, T., Kobayashi, A., & Fukusaki, E. (2007). Prediction of Japanese green tea ranking by Fourier transform near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry, 55, 9908–9912.

    Article  CAS  PubMed  Google Scholar 

  • Ishizaki, K., Schauer, N., Larson, T. R., Graham, I. A., Fernie, A. R., & Leaver, C. J. (2006). The mitochondrial electron transfer flavoprotein complex is essential for survival of Arabidopsis in extended darkness. Plant Journal, 47, 751–760.

    Article  CAS  PubMed  Google Scholar 

  • Kikuchi, S., Satoh, K., Nagata, T., et al. (2003). Collection, mapping, and annotation of over 28, 000 cDNA clones from Japonica rice. Science, 301, 376–379.

    Article  PubMed  Google Scholar 

  • Kondou, Y., Higuchi, M., Takahashi, S., et al. (2009). Systematic approaches to using the FOX hunting system to identify useful rice genes. Plant Journal, 57, 883–894.

    Article  CAS  PubMed  Google Scholar 

  • Kovalenko, I. V., Rippke, G. R., & Hurburgh, C. R. (2006). Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 54, 3485–3491.

    Article  CAS  PubMed  Google Scholar 

  • Kusano, M., Fukushima, A., Kobayashi, M., et al. (2007). Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 855, 71–79.

    Article  CAS  PubMed  Google Scholar 

  • Mason, R. L., Chou, Y. M., & Young, J. C. (2001). Applying Hotelling’s T-2 statistic to batch processes. Journal of Quality Technology, 33, 466–479.

    Google Scholar 

  • Mouille, G., Robin, S., Lecomte, M., Pagant, S., & Hofte, H. (2003). Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopy. Plant Journal, 35, 393–404.

    Article  CAS  PubMed  Google Scholar 

  • Munck, L., Nielsen, J. P., Moller, B., et al. (2001). Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics. Analytica Chimica Acta, 446, 171–186.

    Article  CAS  Google Scholar 

  • Pohjanen, E., Thysell, E., Jonsson, P., et al. (2007). A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum. Journal of Proteome Research, 6, 2113–2120.

    Article  CAS  PubMed  Google Scholar 

  • Raamsdonk, L. M., Teusink, B., Broadhurst, D., et al. (2001). A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnology, 19, 45–50.

    Article  CAS  PubMed  Google Scholar 

  • Rantalainen, M., Cloarec, O., Beckonert, O., et al. (2006). Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice. Journal of Proteome Research, 5, 2642–2655.

    Article  CAS  PubMed  Google Scholar 

  • Rodriguez Otero, J. L., Hermida, M., & Centeno, J. (1997). Analysis of dairy products by near-infrared spectroscopy: A review. Journal of Agricultural and Food Chemistry, 45, 2815–2819.

    Article  CAS  Google Scholar 

  • Sato, S., Soga, T., Nishioka, T., & Tomita, M. (2004). Simultaneous determination of the main metabolites in rice leaves using capillary electrophoresis mass spectrometry and capillary electrophoresis diode array detection. Plant Journal, 40, 151–163.

    Article  CAS  PubMed  Google Scholar 

  • Sato, T., Uezono, I., Morishita, T., & Tetsuka, T. (1998). Nondestructive estimation of fatty acid composition in seeds of Brassica napus L. by near-infrared spectroscopy. Journal of American Oil Chemists’ Society, 75, 1877–1881.

    Article  CAS  Google Scholar 

  • Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36, 1627–1639.

    Article  CAS  Google Scholar 

  • Schauer, N., Steinhauser, D., Strelkov, S., et al. (2005). GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Letters, 579, 1332–1337.

    Article  CAS  PubMed  Google Scholar 

  • Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 64, 479–498.

    Article  Google Scholar 

  • Ward, J. L., Harris, C., Lewis, J., & Beale, M. H. (2003). Assessment of H-1 NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana. Phytochemistry, 62, 949–957.

    Article  CAS  PubMed  Google Scholar 

  • Weyer, L. G., & Lo, S.-C. (2002). Spectra-structure correlations in the near-infrared. In J. Chalmers & P. Griffiths (Eds.), Handbook of vibrational spectroscopy (pp. 1817–1837). Chichester: Wiley.

    Google Scholar 

  • Workman, J. (2000). NIR, IR, Raman, and UV-vis spectra featuring polymers and surfactants. In J. Workman (Ed.), Handbook of organic compounds (pp. 77–197). San Diego: Academic Press.

    Google Scholar 

  • Yang, J., & Yen, H. E. (2002). Early salt stress effects on the changes in chemical composition in leaves of ice plant and Arabidopsis. A Fourier transform infrared spectroscopy study. Plant Physiology, 130, 1032–1042.

    Article  CAS  PubMed  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. RIKEN Plant Science Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan

    Makoto Suzuki, Miyako Kusano, Hideki Takahashi, Yumiko Nakamura, Naomi Hayashi, Makoto Kobayashi, Takanari Ichikawa, Minami Matsui & Kazuki Saito

  2. Graduate School of Pharmaceutical Sciences, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan

    Makoto Suzuki & Kazuki Saito

  3. National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, 305-8602, Japan

    Hirohiko Hirochika

Authors
  1. Makoto Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miyako Kusano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hideki Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yumiko Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Naomi Hayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Makoto Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Takanari Ichikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Minami Matsui
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hirohiko Hirochika
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kazuki Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kazuki Saito.

Electronic supplementary material

Below is the link to the electronic supplementary material.

11306_2009_182_MOESM1_ESM.ppt

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)

11306_2009_182_MOESM2_ESM.xls

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)

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11306-009-0182-2

Keywords

Search

Navigation