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.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Baxter, I., & Dilkes, B. P. (2012). Elemental profiles reflect plant adaptations to the environment. Science, 336, 1661–1663.
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.
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.
Clemente, T. E., & Cahoon, E. B. (2009). Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiology, 151, 1030–1040.
Davies, H. (2010). A role for “omics” technologies in food safety assessment. Food Control, 21, 1601–1610.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Kusano, M., & Saito, K. (2012). Role of metabolomics in crop improvement. Journal of Plant Biochemistry and Biotechnology, 21, S24–S31.
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.
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.
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.
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.
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.
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.
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.
Ricroch, A. E. (2013). Assessment of GE food safety using ‘-omics’ techniques and long-term animal feeding studies. New Biotechnology, 30, 349–354.
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.
Rischer, H., & Oksman-Caldentey, K. M. (2006). Unintended effects in genetically modified crops: revealed by metabolomics? Trends in Biotechnology, 24, 102–104.
Rotundo, J. L., & Westgate, M. E. (2009). Meta-analysis of environmental effects on soybean seed composition. Field Crops Research, 110, 147–156.
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.
Specht, J. E., Hume, D. J., & Kumudini, S. V. (1999). Soybean yield potential—A genetic and physiological perspective. Crop Science, 39, 1560–1570.
Team, R. D. C. (2004). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Thompson, J. A., & Nelson, R. L. (1998). Utilization of diverse germplasm for soybean yield improvement. Crop Science, 38, 1362–1368.
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.
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.
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.
Electronic supplementary material
About this article
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
- Soybean (Glycine max) breeding
- Food safety