Differential metabolic networks unravel the effects of silent plant phenotypes

Wolfram Weckwerth, Marcelo Ehlers Loureiro, Kathrin Wenzel, Oliver Fiehn

Research output: Contribution to journalArticle

284 Scopus citations

Abstract

Current efforts aim to functionally characterize each gene in model plants. Frequently, however, no morphological or biochemical phenotype can be ascribed for antisense or knock-out plant genotypes. This is especially the case when gene suppression or knockout is targeted to isoenzymes or gene families. Consequently, pleiotropic effects and gene redundancy are responsible for phenotype resistance. Here, techniques are presented to detect unexpected pleiotropic changes in such instances despite very subtle changes in overall metabolism. The method consists of the relative quantitation of >1,000 compounds by GC/time-of-flight MS, followed by classical statistics and multivariate clustering. Complementary to these tools, metabolic networks are constructed from pair-wise analysis of linear metabolic correlations. The topology of such networks reflects the underlying regulatory pathway structure. A differential analysis of network connectivity was applied for a silent potato plant line suppressed in expression of sucrose synthase isoform II. Metabolic alterations could be assigned to carbohydrate and amino acid metabolism even if no difference in average metabolite levels was found.

Original languageEnglish (US)
Pages (from-to)7809-7814
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume101
Issue number20
DOIs
StatePublished - May 18 2004
Externally publishedYes

Keywords

  • Data mining
  • Functional genomics
  • Metabolomics
  • Metabonomics
  • Regulatory networks

ASJC Scopus subject areas

  • Genetics
  • General

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