Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding

Abstract

Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean lineage representing ~35 years of breeding (launch years 1972–2008) and increasing yield potential. Selected varieties included six conventional and three genetically modified (GM) glyphosate-tolerant lines. A metabolomics approach utilizing capillary electrophoresis (CE)-time-of-flight-mass spectrometry (TOF-MS), gas chromatography (GC)-TOF-MS and liquid chromatography (LC)-quadrupole (q)-TOFMS resulted in measurement of a total of 732 annotated peaks. Ionomics through inductively-coupled plasma (ICP)-MS profiled twenty mineral elements. Orthogonal partial least squares-discriminant analysis (OPLS-DA) of the seed data successfully differentiated newer higher-yielding soybean from earlier lower-yielding accessions at both field sites. This result reflected genetic fingerprinting data that demonstrated a similar distinction between the newer and older soybean. Correlation analysis also revealed associations between yield data and specific metabolites. There were no clear metabolic differences between the conventional and GM lines. Overall, observations of metabolic and genetic differences between older and newer soybean varieties provided novel and significant information on the impact of varietal development on biochemical variability. Proposed applications of omics in food and feed safety assessments will need to consider that GM is not a major source of metabolite variability and that trait development in crops will, of necessity, be associated with biochemical variation.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Baxter, I., & Dilkes, B. P. (2012). Elemental profiles reflect plant adaptations to the environment. Science, 336, 1661–1663.

    Article  CAS  PubMed  Google Scholar 

  2. Berman, K. H., Harrigan, G. G., Nemeth, M. A., Oliveira, W. S., Berger, G. U., & Tagliaferro, F. S. (2011). Compositional equivalence of insect-protected glyphosate-tolerant soybean MON 87701 × MON 89788 to conventional soybean extends across different world regions and multiple growing seasons. Journal of Agricultural and Food Chemistry, 59, 11643–11651.

    Article  CAS  PubMed  Google Scholar 

  3. Bylesjö, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E., & Trygg, J. (2006). OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. Journal of Chemometrics, 20, 341–351.

    Article  Google Scholar 

  4. Clemente, T. E., & Cahoon, E. B. (2009). Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiology, 151, 1030–1040.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Davies, H. (2010). A role for “omics” technologies in food safety assessment. Food Control, 21, 1601–1610.

    Article  Google Scholar 

  6. Gutierrez-Gonzalez, J. J., Wu, X., Gillman, J. D., Lee, J.-D., Zhong, R., Yu, O., et al. (2010). Intricate environment-modulated genetic networks control isoflavone accumulation in soybean seeds. BMC Plant Biology, 10, 105.

    Article  PubMed Central  PubMed  Google Scholar 

  7. Harrigan, G. G., Culler, A. H., Culler, M., Breeze, M. L., Berman, K. H., Halls, S. C., et al. (2013). Investigation of biochemical diversity in a soybean lineage representing 35 years of breeding. Journal of Agricultural and Food Chemistry, 61, 10807–10815.

    Article  CAS  PubMed  Google Scholar 

  8. Harrigan, G. G., Lundry, D., Drury, S., Berman, K., Riordan, S. G., Nemeth, M. A., et al. (2010). Natural variation in crop composition and the impact of transgenesis. Nature Biotechnology, 28, 402–404.

    Article  CAS  PubMed  Google Scholar 

  9. Hothorn, L. A., & Oberdoerfer, R. (2006). Statistical analysis used in the nutritional assessment of novel food using the proof of safety. Regulatory Toxicology and Pharmacology, 44, 125–135.

    Article  PubMed  Google Scholar 

  10. Jonsson, P., Johansson, A. I., Gullberg, J., Trygg, J., Jiye, A., Grung, B., et al. (2005). High-throughput data analysis for detecting and identifying differences between samples in GC/MS-based metabolomic analyses. Analytical Chemistry, 77, 5635–5642.

    Article  CAS  PubMed  Google Scholar 

  11. Kassem, M. A., Meksem, K., Iqbal, M. J., Njiti, V. N., Banz, W. J., Winters, T. A., et al. (2004). Definition of soybean genomic regions that control seed phytoestrogen amounts. Journal of Biomedicine and Biotechnology, 4, 52–60.

    Article  Google Scholar 

  12. Kimbara, J., Yoshida, M., Ito, H., Kitagawa, M., Takada, W., Hayashi, K., et al. (2013). Inhibition of CUTIN DEFICIENT 2 causes defects in cuticle function and structure and metabolite changes in tomato fruit. Plant Cell Physiology, 54, 1535–1548.

    Article  CAS  PubMed  Google Scholar 

  13. Kusano, M., Fukushima, A., Kobayashi, M., Hayashi, N., Jonsson, P., Moritz, T., 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 

  14. Kusano, M., Redestig, H., Hirai, T., Oikawa, A., Matsuda, F., Fukushima, A., et al. (2011). Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment. PLoS ONE, 6, e16989.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Kusano, M., & Saito, K. (2012). Role of metabolomics in crop improvement. Journal of Plant Biochemistry and Biotechnology, 21, S24–S31.

    Article  Google Scholar 

  16. Lam, H. M., Xu, X., Liu, X., Chen, W., Yang, G., Wong, F.-L., et al. (2010). Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nature Genetics, 42, 1053–1059.

    Article  CAS  PubMed  Google Scholar 

  17. Meksem, K., Njiti, V. N., Banz, W. J., Iqbal, M. J., Kassem, M. M., Hyten, D. L., et al. (2001). Genomic regions that underlie soybean seed isoflavone content. Journal of Biomedicine and Biotechnology, 1, 38–44.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Mikel, M. A., Diers, B. W., Nelson, R. L., & Smith, H. H. (2010). Genetic diversity and agronomic improvement of North American soybean germplasm. Crop Science, 50, 1219–1229.

    Article  Google Scholar 

  19. Okazaki, Y., Kamide, Y., Hirai, M., & Saito, K. (2013). Plant lipidomics based on hydrophilic interaction chromatography coupled to ion trap time-of-flight mass spectrometry. Metabolomics, 9, 121–131.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Redestig, H., Fukushima, A., Stenlund, H., Moritz, T., Arita, M., Saito, K., et al. (2009). Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Analytical Chemistry, 81, 7974–7980.

    Article  CAS  PubMed  Google Scholar 

  21. Redestig, H., Kusano, M., Ebana, K., Kobayashi, M., Oikawa, A., Okazaki, Y., et al. (2011). Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics. BMC Systems Biology, 5, 176.

    Article  PubMed Central  PubMed  Google Scholar 

  22. Redestig, H., Kusano, M., Fukushima, A., Matsuda, F., Saito, K., & Arita, M. (2010). Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis. BMC Bioinformatics, 11, 214.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Ricroch, A. E. (2013). Assessment of GE food safety using ‘-omics’ techniques and long-term animal feeding studies. New Biotechnology, 30, 349–354.

    Article  CAS  PubMed  Google Scholar 

  24. Ricroch, A. E., Bergé, J. B., & Kuntz, M. (2011). Evaluation of genetically engineered crops using transcriptomic, proteomic, and metabolomic profiling techniques. Plant Physiology, 155, 1752–1761.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Rischer, H., & Oksman-Caldentey, K. M. (2006). Unintended effects in genetically modified crops: revealed by metabolomics? Trends in Biotechnology, 24, 102–104.

    Article  CAS  PubMed  Google Scholar 

  26. Rotundo, J. L., & Westgate, M. E. (2009). Meta-analysis of environmental effects on soybean seed composition. Field Crops Research, 110, 147–156.

    Article  Google Scholar 

  27. Sansone, S., Fan, T., Goodacre, R., Griffin, J. L., Hardy, N. W., Kaddurah-Daouk, R., et al. (2007). The metabolomics standards initiative. Nature Biotechnology, 25, 846.

    Article  CAS  PubMed  Google Scholar 

  28. Specht, J. E., Hume, D. J., & Kumudini, S. V. (1999). Soybean yield potential—A genetic and physiological perspective. Crop Science, 39, 1560–1570.

    Article  Google Scholar 

  29. Team, R. D. C. (2004). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

    Google Scholar 

  30. Thompson, J. A., & Nelson, R. L. (1998). Utilization of diverse germplasm for soybean yield improvement. Crop Science, 38, 1362–1368.

    Article  Google Scholar 

  31. Vedrina-Dragojevic, I., Balint, L., & Sebecic, B. (1997). Dynamics of the accumulation of thiamine during maturation of soybean seeds. Journal of Plant Physiology, 150, 437–439.

    Article  CAS  Google Scholar 

  32. Ziegler, G., Terauchi, A. M., Becker, A., Armstrong, P. R., Hudson, K. A., & Baxter, A. (2013). Ionomic screening of field-grown soybean identifies mutants with altered seed elemental composition. The Plant Genome, 6, 1–9.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are very grateful for the agronomic support provided by Matt Culler of Monsanto. Genetic fingerprint analyses were conducted within the Molecular Breeding Technology group at Monsanto. The excellent logistical support provided by James McCarter and his sample management team was also critical to the success of this experiment. We are indebted to Nordine Cheikh for his support and encouragement and to Mark Leibman and John Vicini for helpful comments on the manuscript. We also thank Tomoko Nishizawa, Makoto Kobayashi, Ryosuke Sasaki, and Koji Takano (RIKEN) for their technical assistance in the study. This research was also supported by the Japan Advanced Plant Science Network.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Ivan Baxter or Kazuki Saito or George G. Harrigan.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kusano, M., Baxter, I., Fukushima, A. et al. Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding. Metabolomics 11, 261–270 (2015). https://doi.org/10.1007/s11306-014-0702-6

Download citation

Keywords

  • Soybean (Glycine max) breeding
  • Food safety
  • Metabolomics
  • Ionomics