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KMID : 1094720090140060738
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Biotechnology and Bioprocess Engineering
2009 Volume.14 No. 6 p.738 ~ p.747
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Metabolic Profiles of Genetically Modified Potatoes using a Combination of Metabolite Fingerprinting and Multivariate Analysis
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Kim Hyun-Soon
Kim Suk-Weon
Park Young-Seok
Kwon Suk-Youn
Liu Jang-Ryol
Joung Hyo-Uk
Jeon Jae-Heung
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Abstract
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Comprehensive metabolite fingerprinting of transgenic potatoes that constitutively express human beta amyloid, curdlan synthase (CRDS), and glycogen synthase (glgA); and of wild-type potatoes was carried out using FT-IR and 1H NMR spectroscopy in combination with multivariate analyses. Comparison of metabolic patterns between transgenic and wild-type potatoes revealed that there were neither quantitative nor qualitative differences in metabolites between transgenic potatoes expressing human beta amyloid, CRDS or glgA, and non-transformed control potatoes. However, there were metabolic differences between two control potato lines - one that was fresh and the other stored. After 1 week of storage, comprehensive metabolite patterns were significantly modified. Although the differences between CRDS and glgA transgenic and control potato lines were small, PCA analysis of FT-IR and 1H NMR spectral data identified two distinct control lines. These results suggest that the comprehensive metabolite changes in control potato lines, which occurred after 1 week of storage, were greater than the differences between CRDS and glgA transgenic and wild-type potato lines. Thus, the combination of FT-IR and 1H NMR spectral data and multivariate analysis was valuable for the detection of comprehensive differences in metabolic profiles between transgenic and non-transformed control plants, even though peak-signal overlap prevented assignment of pure compounds. The combination of FT-IR and 1H NMR spectral data and multivariate analysis is a simple and rapid method for evaluation of the metabolic equivalence of GM crops.
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KEYWORD
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FT-IR spectroscopy, genetically modified potatoes, 1H NMR spectroscopy, multivariate analysis, principal component analysis (PCA)
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