Metabolic Profiling Allows Comprehensive Phenotyping of Genetically or Environmentally Modified Plant Systems
Author(s) -
Ute Roessner,
Alexander Luedemann,
Doreen Brust,
Oliver Fiehn,
Thomas Linke,
Lothar Willmitzer,
Alisdair R. Fernie
Publication year - 2001
Publication title -
the plant cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.324
H-Index - 341
eISSN - 1532-298X
pISSN - 1040-4651
DOI - 10.1105/tpc.13.1.11
Subject(s) - profiling (computer programming) , biology , computational biology , metabolomics , genotype , microbiology and biotechnology , biological system , bioinformatics , genetics , computer science , gene , operating system
Metabolic profiling using gas chromatography-mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with data-mining tools as a technique for the comprehensive characterization of a plant genotype.
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