Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

Yanfei Ren; Tao Wang; Yufa Peng; Bin Xia; Li-Jia Qu

doi:10.1016/S1673-8527(08)60154-X

Volume 36 Issue 10

Oct. 2009

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Article Navigation > Journal of Genetics and Genomics > 2009 > 36(10): 621-628

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Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting

doi: 10.1016/S1673-8527(08)60154-X

Yanfei Ren^{a, #},
Tao Wang^{b, #},
Yufa Peng^c,
Bin Xia^b,
Li-Jia Qu^a

a
National Key Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, China
b
Beijing NMR Center, College of Life Sciences, Peking University, Beijing 100871, China
c
Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China

More Information

Corresponding author: E-mail address: binxia@pku.edu.cn (Bin Xia); E-mail address: qulj@pku.edu.cn (Li-Jia Qu)
Received Date: 2009-06-05
Accepted Date: 2009-09-16
Rev Recd Date: 2009-09-08

Available Online: 2009-10-17

Publish Date: 2009-10-20

Abstract

Abstract

We have recently reported the construction of an nuclear magnetic resonance (NMR)-based metabonomics study platform, Automics. To examine the application of Automics in transgenic plants, we performed metabolic fingerprinting analysis, i.e., ¹H NMR spectroscopy and multivariate analysis, on wild-type and transgenicArabidopsis. We found that it was possible to distinguish wild-type from four transgenic plants by PLS-DA following application of orthogonal signal correction (OSC). Scores plot following OSC clearly demonstrates significant variation between the transgenic and non-transgenic groups, suggesting that the metabolic changes among wild-type and transgenic lines are possibly associated with transgenic event. We also found that the major contributing metabolites were some specific amino acids (i.e., threonine and alanine), which could correspond to the insertion of the selective marker BAR gene in the transgenic plants. Our data suggests that NMR-based metabonomics is an efficient method to distinguish fingerprinting difference between wild-type and transgenic plants, and can potentially be applied in the bio-safety assessment of transgenic plants.
- metabonomics,
- nuclear magnetic resonance,
- principal component analysis,
- orthogonal signal correction

These authors contributed equally to this work.

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References(20)

References

[1]	Bailey, N.J.C., Oven, M., Holmes, E. et al. Phytochemistry, 62 (2003),pp. 851-858
[2]	Bligny, R., Douce, R. NMR and plant metabolism Curr. Opin. Plant Biol., 4 (2001),pp. 191-196
[3]	Cellini, F., Chesson, A., Colquhoun, I. et al. Unintended effects and their detection in genetically modified crops Food Chem. Toxicol., 42 (2004),pp. 1089-1125
[4]	Charlton, A., Allnutt, T., Holmes, S. et al. NMR profiling of transgenic peas Plant Biotechnol. J., 2 (2004),pp. 27-35
[5]	Charlton, A.J. NMR profiling of transgenic peas Abstracts of Papers of the American Chemical Society, 229 (2005),p. U83
[6]	Choi, Y.H., Kim, H.K., Linthorst, H.J.M. et al. J. Nat. Prod., 69 (2006),pp. 742-748
[7]	Conner, A.J., Jacobs, J.M.E. Genetic engineering of crops as potential source of genetic hazard in the human diet Mutat. Res., 443 (1999),pp. 223-234
[8]	Krishnan, P., Kruger, N.J., Ratcliffe, R.G. Metabolite fingerprinting and profiling in plants using NMR J. Exp. Bot., 56 (2005),pp. 255-265
[9]	Kuiper, H.A., Kleter, G.A., Noteborn, H.P.J.M. et al. Assessment of the food safety issues related to genetically modified foods Plant J., 27 (2001),pp. 503-528
[10]	Le Gall, G., DuPont, M.S., Mellon, F.A. et al. J. Agric. Food Chem., 51 (2003),pp. 2438-2446
[11]	Liang, Y.S., Choi, Y.H., Kim, H.K. et al. Phytochemistry, 67 (2006),pp. 2503-2511
[12]	Mesnard, F., Ratcliffe, R.G. NMR analysis of plant nitrogen metabolism Photosyn. Res., 83 (2005),pp. 163-180
[13]	Noteborn, H.P.J.M., Lommen, A., van der Jagt, R.C. et al. Chemical fingerprinting for the evaluation of unintended secondary metabolic changes in transgenic food crops J. Biotechnol., 77 (2000),pp. 103-114
[14]	Ott, K.H., Aranibar, N., Singh, B.J. et al. Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts Phytochemistry, 62 (2003),pp. 971-985
[15]	Pedersen, H.T., Bro, R., Engelsen, S.B. SLICING—A novel approach for unique deconvolution of NMR relaxation decays Magn. Reson. Food Sci., 262 (2001),pp. 202-209
[16]	Ruebelt, M.C., Leimgruber, N.K., Lipp, M. et al. Application of two-dimensional gel electrophoresis to interrogate alterations in the proteome of genetically modified crops. 1. Assessing analytical validation J. Agric. Food Chem., 54 (2006),pp. 2154-2161
[17]	Tian, C., Chikayama, E., Tsuboi, Y. et al. J. Biol. Chem., 282 (2007),pp. 18532-18541
[18]	Walker, T.S., Bais, H.P., Halligan, K.M. et al. J. Agric. Food Chem., 51 (2003),pp. 2548-2554
[19]	Wang, T., Shao, K., Chu, Q.Y. et al. Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis BMC Bioinformatics, 10 (2009),pp. 83-98
[20]	Ward, J.L., Harris, C., Lewis, J. et al. Phytochemistry, 62 (2003),pp. 949-957