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

Metabolomics

Volumn 11, Issue 2, 2015, Pages 261-270

  Kusano, Miyako ^a   Baxter, Ivan ^b   Fukushima, Atsushi ^a   Oikawa, Akira ^a   Okazaki, Yozo ^a   Nakabayashi, Ryo ^a   Bouvrette, Denise J ^c   Achard, Frederic ^c   Jakubowski, Andrew R ^c   Ballam, Joan M ^c   Phillips, Jonathan R ^c   Culler, Angela H ^c   Saito, Kazuki ^a,d   Harrigan, George G ^c  

^a Elements Chemistry Laboratory   (Japan)

^b DONALD DANFORTH PLANT SCIENCE CENTER   (United States)

^c MONSANTO COMPANY   (United States)

^d CHIBA UNIVERSITY   (Japan)

Author keywords

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

Indexed keywords

BETA ALANINE; GLUCONIC ACID; ISOFLAVONE; MOLYBDENUM; RUBIDIUM; SELENIUM; STRONTIUM;

ARTICLE; BIOCHEMICAL ANALYSIS; CHEMICAL ANALYSIS; CONTROLLED STUDY; CORRELATION ANALYSIS; DISCRIMINANT ANALYSIS; FOOD SAFETY; GAS CHROMATOGRAPHY; GENETIC ANALYSIS; GENETIC FINGERPRINT ANALYSIS; INFORMATION PROCESSING; LIQUID CHROMATOGRAPHY; MEASUREMENT ACCURACY; METABOLITE; METABOLOMICS; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; PHYSICAL CHEMISTRY; PLANT BREEDING; PLANT YIELD; SOYBEAN; SPECIES DIVERSITY; TIME OF FLIGHT MASS SPECTROMETRY;

EID: 84929170704     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-014-0702-6     Document Type: Article

References (32)

1. Baxter, I., & Dilkes, B. P. (2012). Elemental profiles reflect plant adaptations to the environment. Science,336, 1661–1663.
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.
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.
4. Clemente, T. E., & Cahoon, E. B. (2009). Soybean oil: genetic approaches for modification of functionality and total content. Plant Physiology,151, 1030–1040.
5. Davies, H. (2010). A role for “omics” technologies in food safety assessment. Food Control,21, 1601–1610.
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.
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.
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.
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.
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.
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.
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.
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.
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.
15. Kusano, M., & Saito, K. (2012). Role of metabolomics in crop improvement. Journal of Plant Biochemistry and Biotechnology,21, S24–S31.
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.
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.
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.
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.
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.
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.
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.
23. Ricroch, A. E. (2013). Assessment of GE food safety using ‘-omics’ techniques and long-term animal feeding studies. New Biotechnology,30, 349–354.
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.
25. Rischer, H., & Oksman-Caldentey, K. M. (2006). Unintended effects in genetically modified crops: revealed by metabolomics? Trends in Biotechnology,24, 102–104.
26. Rotundo, J. L., & Westgate, M. E. (2009). Meta-analysis of environmental effects on soybean seed composition. Field Crops Research,110, 147–156.
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.
28. Specht, J. E., Hume, D. J., & Kumudini, S. V. (1999). Soybean yield potential—A genetic and physiological perspective. Crop Science,39, 1560–1570.
29. Team, R. D. C. (2004). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
30. Thompson, J. A., & Nelson, R. L. (1998). Utilization of diverse germplasm for soybean yield improvement. Crop Science,38, 1362–1368.
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.
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.