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Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97433

Sulpice,  R.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97450

Trenkamp,  S.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97424

Steinfath,  M.
BioinformaticsCIG, Infrastructure Groups and Service Units, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97455

Usadel,  B.
Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97167

Gibon,  Y.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97346

Pyl,  E. T.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons98180

Tschoep,  H.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97426

Steinhauser,  M.-C.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97178

Guenther,  M.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97199

Hoehne,  M.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97050

Altmann,  T.
Developmental Physiology and Genomics, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97147

Fernie,  A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97427

Stitt,  M.
System Regulation, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

Sulpice, R., Trenkamp, S., Steinfath, M., Usadel, B., Gibon, Y., Witucka-Wall, H., et al. (2010). Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions. The Plant Cell, 22(8), 2872-2893. doi:10.1105/tpc.110.076653.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-22F3-C
Abstract
Natural genetic diversity provides a powerful resource to investigate how networks respond to multiple simultaneous changes. In this work, we profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass. Most enzyme activities change in a highly coordinated manner, especially those in the Calvin-Benson cycle. Metabolites show coordinated changes in defined sectors of metabolism. Little connectivity was observed between maximum enzyme activities and metabolites, even after applying multivariate analysis methods. Measurements of posttranscriptional regulation will be required to relate these two functional levels. Individual enzyme activities correlate only weakly with biomass. However, when they are used to estimate protein abundances, and the latter are summed and expressed as a fraction of total protein, a significant positive correlation to biomass is observed. The correlation is additive to that obtained between starch and biomass. Thus, biomass is predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use.