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Transcript abundance on its own cannot be used to infer fluxes in central metabolism

Overview of attention for article published in Frontiers in Plant Science, November 2014
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Title
Transcript abundance on its own cannot be used to infer fluxes in central metabolism
Published in
Frontiers in Plant Science, November 2014
DOI 10.3389/fpls.2014.00668
Pubmed ID
Authors

Jörg Schwender, Christina König, Matthias Klapperstück, Nicolas Heinzel, Eberhard Munz, Inga Hebbelmann, Jordan O. Hay, Peter Denolf, Stefanie De Bodt, Henning Redestig, Evelyne Caestecker, Peter M. Jakob, Ljudmilla Borisjuk, Hardy Rolletschek

Abstract

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.

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Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United States 2 2%
Switzerland 1 1%
Portugal 1 1%
Singapore 1 1%
Israel 1 1%
Unknown 79 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 28%
Researcher 24 28%
Student > Master 7 8%
Student > Doctoral Student 5 6%
Other 4 5%
Other 10 11%
Unknown 13 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 46%
Biochemistry, Genetics and Molecular Biology 18 21%
Computer Science 3 3%
Social Sciences 3 3%
Engineering 3 3%
Other 6 7%
Unknown 14 16%