Metabolomics

, 14:36 | Cite as

Characterization of GMO or glyphosate effects on the composition of maize grain and maize-based diet for rat feeding

  • Stéphane Bernillon
  • Mickaël Maucourt
  • Catherine Deborde
  • Sylvain Chéreau
  • Daniel Jacob
  • Nathalie Priymenko
  • Bérengère Laporte
  • Xavier Coumoul
  • Bernard Salles
  • Peter M. Rogowsky
  • Florence Richard-Forget
  • Annick Moing
Original Article
Part of the following topical collections:
  1. Feeding a healthier world: metabolomics for food and nutrition

Abstract

Introduction

In addition to classical targeted biochemical analyses, metabolomic analyses seem pertinent to reveal expected as well as unexpected compositional differences between plant genetically modified organisms (GMO) and non-GMO samples. Data previously published in the existing literature led to divergent conclusions on the effect of maize transgenes on grain compositional changes and feeding effects. Therefore, a new study examining field-grown harvested products and feeds derived from them remains useful.

Objectives

Our aim was to use a metabolomics approach to characterize grain and grain-based diet compositional changes for two GMO events, one involving Bacillus thuringiensis toxin to provide insect resistance and the other one conferring herbicide tolerance by detoxification of glyphosate. We also investigated the potential compositional modifications induced by the use of a glyphosate-based herbicide on the transgenic line conferring glyphosate tolerance.

Results

The majority of statistically significant differences in grain composition, evidenced by the use of 1H-NMR profiling of polar extracts and LC-ESI-QTOF-MS profiling of semi-polar extracts, could be attributed to the combined effect of genotype and environment. In comparison, transgene and glyphosate effects remained limited in grain for the compound families studied. Some but not all compositional changes observed in grain were also detected in grain-based diets formulated for rats.

Conclusion

Only part of the data previously published in the existing literature on maize grains of plants with the same GMO events could be reproduced in our experiment. All spectra have been deposited in a repository freely accessible to the public. Our grain and diet characterization opened the way for an in depth study of the effects of these diets on rat health.

Keywords

Maize Metabolomics GMO Grain Rat diet 

Abbreviations

CPMG

Carr–Purcell–Meiboom–Gill

DW

Dry weight

LC-ESI-QTOF-MS

Liquid chromatography electrospray-ionization time-of-flight mass spectrometry

NMR

Nuclear magnetic resonance

PCA

Principal component analysis

Notes

Acknowledgements

We thank Drs Pablo Steinberg, Ralf Wilhelm and Joachim Schiemann (G-TwYST, EC project) for having shared the maize production and preliminary targeted analyses of the grains, and Dr Maria Pla (IRTA Mas Badia Field Station) and the farmers involved in maize culture, harvest and drying for providing the grain samples cultivated in Spain. We are grateful to the members of the scientific council of RiskOGM program for their follow-up and advice.

Compliance with ethical standards

Conflict of interest

Conflicts of interest of the principal investigators are declared on the public RiskOGM programme website (http://recherche-riskogm.fr/en/page/partners-pdis).

Ethical approval

The GMO plant samples followed dedicated laboratory procedures concerning their identification and destruction. Although this article is related to a project involving animals (Study approved by French Ethical Committee CETEA), it does not contain any study with animals performed by any of the authors.

Supplementary material

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Supplementary material 1 (PDF 392 KB)
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References

  1. Bakan, B., Melcion, D., Richard-Molard, D., & Cahagnier, B. (2002). Fungal growth and Fusarium mycotoxin content in isogenic traditional maize and genetically modified maize grown in France and Spain. Journal of Agricultural and Food Chemistry, 50(4), 728–731.  https://doi.org/10.1021/jf0108258.CrossRefPubMedGoogle Scholar
  2. Baker, J. M., Hawkins, N. D., Ward, J. L., Lovegrove, A., Napier, J. A., Shewry, P. R., et al. (2006). A metabolomic study of substantial equivalence of field-grown genetically modified wheat. Plant Biotechnology Journal, 4(4), 381–392.  https://doi.org/10.1111/j.1467-7652.2006.00197.x.CrossRefPubMedGoogle Scholar
  3. Balmer, D., Papajewski, D. V., Planchamp, C., Glauser, G., & Mauch-Mani, B. (2013). Induced resistance in maize is based on organ-specific defence responses. The Plant Journal, 74(2), 213–225.  https://doi.org/10.1111/tpj.12114.CrossRefPubMedGoogle Scholar
  4. Baniasadi, H., Vlahakis, C., Hazebroek, J., Zhong, C., & Asiago, V. (2014). Effect of environment and genotype on commercial maize hybrids using LC/MS-based metabolomics. Journal of Agricultural and Food Chemistry, 62(6), 1412–1422.  https://doi.org/10.1021/jf404702g.CrossRefPubMedGoogle Scholar
  5. Bassard, J.-E., Ullmann, P., Bernier, F., & Werck-Reichhart, D. (2010). Phenolamides: Bridging polyamines to the phenolic metabolism. Phytochemistry, 71(16), 1808–1824.  https://doi.org/10.1016/j.phytochem.2010.08.003.CrossRefPubMedGoogle Scholar
  6. Benevenuto, R. F., Agapito-Tenfen, S. Z., Vilperte, V., Wikmark, O.-G., van Rensburg, P. J., & Nodari, R. O. (2017). Molecular responses of genetically modified maize to abiotic stresses as determined through proteomic and metabolomic analyses. PLoS ONE, 12(2), e0173069.  https://doi.org/10.1371/journal.pone.0173069.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Biais, B., Allwood, J. W., Deborde, C., Xu, Y., Maucourt, M., Beauvoit, B., et al. (2009). 1H NMR, GC-EI-TOFMS, and data set correlation for fruit metabolomics: Application to spatial metabolite analysis in melon. Analytical Chemistry, 81(8), 2884–2894.  https://doi.org/10.1021/ac9001996.CrossRefPubMedGoogle Scholar
  8. Bowers, E., Hellmich, R., & Munkvold, G. (2014). Comparison of fumonisin contamination using HPLC and ELISA methods in Bt and near-isogenic maize hybrids infested with european corn borer or western bean cutworm. Journal of Agricultural and Food Chemistry, 62(27), 6463–6472.  https://doi.org/10.1021/jf5011897.CrossRefPubMedGoogle Scholar
  9. Catchpole, G. S., Beckmann, M., Enot, D. P., Mondhe, M., Zywicki, B., Taylor, J., et al. (2005). Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proceedings of the National Academy of Sciences of the United States of America, 102(40), 14458–14462.  https://doi.org/10.1073/pnas.0503955102.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Chen, M., Rao, R. S. P., Zhang, Y., Zhong, C., & Thelen, J. J. (2016). Metabolite variation in hybrid corn grain from a large-scale multisite study. The Crop Journal, 4(3), 177–187.  https://doi.org/10.1016/j.cj.2016.03.004.CrossRefGoogle Scholar
  11. Cuhra, M., Traavik, T., Dando, M., Primicerio, R., Holderbaum, D. F., & Bøhn, T. (2015). Glyphosate-residues in roundup-ready soybean impair Daphnia magna life-cycle. Journal of Agricultural Chemistry and Environment, 4(01), 24–36.  https://doi.org/10.4236/jacen.2015.41003.CrossRefGoogle Scholar
  12. Domingo, J. L. (2016). Safety assessment of GM plants: An updated review of the scientific literature. Food and Chemical Toxicology, 95, 12–18.  https://doi.org/10.1016/j.fct.2016.06.013.CrossRefPubMedGoogle Scholar
  13. Domingo, J. L., & Giné Bordonaba, J. (2011). A literature review on the safety assessment of genetically modified plants. Environment International, 37(4), 734–742.  https://doi.org/10.1016/j.envint.2011.01.003.CrossRefPubMedGoogle Scholar
  14. Duke, S. O., Rimando, A. M., Reddy, K. N., Cizdziel, J. V., Bellaloui, N., Shaw, D. R., et al. (2017). Lack of transgene and glyphosate effects on yield, and mineral and amino acid content of glyphosate-resistant soybean. Pest Management Science.  https://doi.org/10.1002/ps.4625.Google Scholar
  15. Fan, T. W. M. (1996). Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures. Progress in Nuclear Magnetic Resonance Spectroscopy, 28(2), 161–219.  https://doi.org/10.1016/0079-6565(95)01017-3.CrossRefGoogle Scholar
  16. Frank, T., Röhlig, R. M., Davies, H. V., Barros, E., & Engel, K.-H. (2012). Metabolite profiling of maize kernel—genetic modification versus environmental influence. Journal of Agricultural and Food Chemistry, 60(12), 3005–3012.  https://doi.org/10.1021/jf204167t.CrossRefPubMedGoogle Scholar
  17. Funke, T., Han, H., Healy-Fried, M. L., Fischer, M., & Schönbrunn, E. (2006). Molecular basis for the herbicide resistance of Roundup Ready crops. Proceedings of the National Academy of Sciences USA, 103(35), 13010–13015,  https://doi.org/10.1073/pnas.0603638103.CrossRefGoogle Scholar
  18. Graham, S. F., Hollis, J. H., Migaud, M., & Browne, R. A. (2009). Analysis of betaine and choline contents of aleurone, bran, and flour fractions of wheat (Triticum aestivum L.) using 1H nuclear magnetic resonance (NMR) spectroscopy. Journal of Agricultural and Food Chemistry, 57(5), 1948–1951.  https://doi.org/10.1021/jf802885m.CrossRefPubMedGoogle Scholar
  19. Hall, R. D. (2011). Plant metabolomics in a nutshell: Potential and future challenges. In R. D. Hall (Ed.), Biology of plant metabolomics (Vol. 43, pp. 1–24). Oxford: Wiley-Blackwell.Google Scholar
  20. Harrigan, G. G., Venkatesh, T. V., Leibman, M., Blankenship, J., Perez, T., Halls, S., et al. (2016). Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait. Metabolomics, 12(5), 82.  https://doi.org/10.1007/s11306-016-1017-6.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Hetherington, P. R., Reynolds, T. L., Marshall, G., & Kirkwood, R. C. (1999). The absorption, translocation and distribution of the herbicide glyphosate in maize expressing the CP-4 transgene. Journal of Experimental Botany, 50(339), 1567–1576.  https://doi.org/10.1093/jxb/50.339.1567.CrossRefGoogle Scholar
  22. Hilbeck, A., Binimelis, R., Defarge, N., Steinbrecher, R., Székács, A., Wickson, F., et al. (2015). No scientific consensus on GMO safety. Environmental Sciences Europe, 27(1), 4.  https://doi.org/10.1186/s12302-014-0034-1.CrossRefGoogle Scholar
  23. Jacob, D., Deborde, C., Lefebvre, M., Maucourt, M., & Moing, A. (2017). NMRProcFlow: A graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics, 13(4), 36.  https://doi.org/10.1007/s11306-017-1178-y.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Khakimov, B., Bak, S., & Engelsen, S. B. (2014). High-throughput cereal metabolomics: Current analytical technologies, challenges and perspectives. Journal of Cereal Science, 59(3), 393–418.  https://doi.org/10.1016/j.jcs.2013.10.002.CrossRefGoogle Scholar
  25. Kumar, V., Rani, A., Goyal, L., Dixit, A. K., Manjaya, J., Dev, J., et al. (2010). Sucrose and raffinose family oligosaccharides (RFOs) in soybean seeds as influenced by genotype and growing location. Journal of Agricultural and Food Chemistry, 58(8), 5081–5085.  https://doi.org/10.1021/jf903141s.CrossRefPubMedGoogle Scholar
  26. Le Gall, G., Colquhoun, I. J., Davis, A. L., Collins, G. J., & Verhoeyen, M. E. (2003). Metabolite profiling of tomato (Lycopersicon esculentum) using 1H NMR spectroscopy as a tool to detect potential unintended effects following a genetic modification. Journal of Agricultural and Food Chemistry, 51(9), 2447–2456.  https://doi.org/10.1021/jf0259967.CrossRefPubMedGoogle Scholar
  27. Leon, C., Rodriguez-Meizoso, I., Lucio, M., Garcia-Cañas, V., Ibañez, E., Schmitt-Kopplin, P., et al. (2009). Metabolomics of transgenic maize combining Fourier transform-ion cyclotron resonance-mass spectrometry, capillary electrophoresis-mass spectrometry and pressurized liquid extraction. Journal of Chromatography A, 1216(43), 7314–7323.  https://doi.org/10.1016/j.chroma.2009.04.092.CrossRefPubMedGoogle Scholar
  28. Levandi, T., Leon, C., Kaljurand, M., Garcia-Cañas, V., & Cifuentes, A. (2008). Capillary electrophoresis time-of-flight mass spectrometry for comparative metabolomics of transgenic versus conventional maize. Analytical Chemistry, 80(16), 6329–6335.  https://doi.org/10.1021/ac8006329.CrossRefPubMedGoogle Scholar
  29. Liu, Y., Zhang, Y., Liu, Y., Lu, W., & Wang, G. (2015). Metabolic effects of glyphosate on transgenic maize expressing a G2-EPSPS gene from Pseudomonas fluorescens. Journal of Plant Biochemistry and Biotechnology, 24(2), 233–241.  https://doi.org/10.1007/s13562-014-0263-9.CrossRefGoogle Scholar
  30. Manetti, C., Bianchetti, C., Casciani, L., Castro, C., Di Cocco, M. E., Miccheli, A., et al. (2006). A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids. Journal of Experimental Botany, 57(11), 2613–2625.  https://doi.org/10.1093/jxb/erl025.CrossRefPubMedGoogle Scholar
  31. Martin-Tanguy, J. (1985). The occurrence and possible function of hydroxycinnamoyl acid amides in plants. Plant Growth Regulation, 3(3), 381–399.  https://doi.org/10.1007/bf00117595.CrossRefGoogle Scholar
  32. Mesnage, R., Agapito-Tenfen, S. Z., Vilperte, V., Renney, G., Ward, M., Séralini, G.-E., et al. (2016). An integrated multi-omics analysis of the NK603 Roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process. Scientific Reports, 6, 37855.  https://doi.org/10.1038/srep37855.CrossRefPubMedPubMedCentralGoogle Scholar
  33. Moing, A., Maucourt, M., Renaud, C., Gaudillere, M., Brouquisse, R., Lebouteiller, B., et al. (2004). Quantitative metabolic profiling by 1-dimensional 1H-NMR analyses: Application to plant genetics and functional genomics. Functional Plant Biology, 31(9), 889–902.  https://doi.org/10.1071/FP04066.CrossRefGoogle Scholar
  34. Mounet, F., Lemaire-Chamley, M., Maucourt, M., Cabasson, C., Giraudel, J.-L., Deborde, C., et al. (2007). Quantitative metabolic profiles of tomato flesh and seeds during fruit development: Complementary analysis with ANN and PCA. Metabolomics, 3(3), 273–288.  https://doi.org/10.1007/s11306-007-0059-1.CrossRefGoogle Scholar
  35. Onkokesung, N., Gaquerel, E., Kotkar, H., Kaur, H., Baldwin, I. T., & Galis, I. (2012). MYB8 Controls inducible phenolamide levels by activating three novel hydroxycinnamoyl-coenzyme A:polyamine transferases in Nicotiana attenuata. Plant Physiology, 158(1), 389–407.  https://doi.org/10.1104/pp.111.187229.CrossRefPubMedGoogle Scholar
  36. Piccioni, F., Capitani, D., Zolla, L., & Mannina, L. (2009). NMR metabolic profiling of transgenic maize with the Cry1A (b) gene. Journal of Agricultural and Food Chemistry, 57(14), 6041–6049.CrossRefPubMedGoogle Scholar
  37. Ridley, W. P., Sidhu, R. S., Pyla, P. D., Nemeth, M. A., Breeze, M. L., & Astwood, J. D. (2002). Comparison of the nutritional profile of glyphosate-tolerant corn event NK603 with that of conventional corn (Zea mays L.). Journal of Agricultural and Food Chemistry, 50(25), 7235–7243.  https://doi.org/10.1021/jf0205662.CrossRefPubMedGoogle Scholar
  38. Schmidt, K., Döhring, J., Kohl, C., Pla, M., Kok, E. J., Glandorf, D. C. M., et al. (2016). Proposed criteria for the evaluation of the scientific quality of mandatory rat and mouse feeding trials with whole food/feed derived from genetically modified plants. Archives of toxicology, 90(9), 2287–2291.  https://doi.org/10.1007/s00204-016-1762-3.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Shen, X., Gong, X., Cai, Y., Guo, Y., Tu, J., Li, H., et al. (2016). Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics, 12(5), 89.  https://doi.org/10.1007/s11306-016-1026-5.CrossRefGoogle Scholar
  40. Singh, S., Gamlath, S., & Wakeling, L. (2007). Nutritional aspects of food extrusion: A review. International Journal of Food Science & Technology, 42(8), 916–929.  https://doi.org/10.1111/j.1365-2621.2006.01309.x.CrossRefGoogle Scholar
  41. Skogerson, K., Harrigan, G. G., Reynolds, T. L., Halls, S. C., Ruebelt, M., Iandolino, A., et al. (2010). Impact of genetics and environment on the metabolite composition of maize grain. Journal of Agricultural and Food Chemistry, 58(6), 3600–3610.  https://doi.org/10.1021/jf903705y.CrossRefPubMedGoogle Scholar
  42. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787.  https://doi.org/10.1021/ac051437y.CrossRefPubMedGoogle Scholar
  43. t’Kindt, R., Morreel, K., Deforce, D., Boerjan, W., & Van Bocxlaer, J. (2009). Joint GC–MS and LC–MS platforms for comprehensive plant metabolomics: Repeatability and sample pre-treatment. Journal of Chromatography B, 877(29), 3572–3580.  https://doi.org/10.1016/j.jchromb.2009.08.041.CrossRefGoogle Scholar
  44. Tang, W., Hazebroek, J., Zhong, C., Harp, T., Vlahakis, C., Baumhover, B., et al. (2017). Effect of genetics, environment, and phenotype on the metabolome of maize hybrids using GC/MS and LC/MS. Journal of Agricultural and Food Chemistry, 65(25), 5215–5225.  https://doi.org/10.1021/acs.jafc.7b00456.CrossRefPubMedGoogle Scholar
  45. Venkatesh, T. V., Chassy, A. W., Fiehn, O., Flint-Garcia, S., Zeng, Q., Skogerson, K., et al. (2016). Metabolomic assessment of key maize resources: GC-MS and NMR Profiling of grain from B73 hybrids of the Nested Association Mapping (NAM) Founders and of geographically diverse landraces. Journal of Agricultural and Food Chemistry, 64(10), 2162–2172.  https://doi.org/10.1021/acs.jafc.5b04901.CrossRefPubMedGoogle Scholar
  46. Vinaixa, M., Samino, S., Saez, I., Duran, J., Guinovart, J. J., & Yanes, O. (2012). A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data. Metabolites, 2(4), 775–795.  https://doi.org/10.3390/metabo2040775.CrossRefPubMedPubMedCentralGoogle Scholar
  47. Watson, S. A. (2003). Description, development, structure and composition of the corn kernel. In P. J. White & L. A. Johnson (Eds.), Corn: Chemistry and technology, Second Edition (pp. 69–106). St Paul, MN: AACC.Google Scholar
  48. Wen, W., Li, D., Li, X., Gao, Y., Li, W., Li, H., et al. (2014). Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nature Communications, 5, 3438.  https://doi.org/10.1038/ncomms4438.PubMedPubMedCentralGoogle Scholar
  49. Wusirika, R., Bohn, M., Lai, J., & Kole, C. (Eds.). (2014). Genetics, genomics and breeding of maize. Boca Raton, FL: CRC Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Stéphane Bernillon
    • 1
    • 2
  • Mickaël Maucourt
    • 1
    • 2
  • Catherine Deborde
    • 1
    • 2
  • Sylvain Chéreau
    • 3
  • Daniel Jacob
    • 1
    • 2
  • Nathalie Priymenko
    • 4
  • Bérengère Laporte
    • 4
  • Xavier Coumoul
    • 5
  • Bernard Salles
    • 4
  • Peter M. Rogowsky
    • 6
  • Florence Richard-Forget
    • 3
  • Annick Moing
    • 1
    • 2
  1. 1.UMR1332 Biologie du Fruit et Pathologie, INRA, Univ. Bordeaux, Centre INRA de Nouvelle Aquitaine - BordeauxVillenave d’OrnonFrance
  2. 2.Plateforme Métabolome du Centre de Génomique Fonctionnelle Bordeaux, MetaboHUB, PHENOME, IBVM, Centre INRA de Nouvelle Aquitaine - BordeauxVillenave d’OrnonFrance
  3. 3.UR MycSA, INRA, Centre INRA de Nouvelle Aquitaine - BordeauxVillenave d’OrnonFrance
  4. 4.Toxalim (Research Centre in Food Toxicology)Université de Toulouse, INRA, ENVT, INP-Purpan, UPSToulouseFrance
  5. 5.UMRS1124, Toxicologie, Pharmacologie et Signalisation Cellulaire, INSERM, Univ. Paris DescartesParisFrance
  6. 6.Laboratoire Reproduction et Développement des PlantesUniv. Lyon, ENS de Lyon, UCB Lyon 1 CNRS, INRALyonFrance

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