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Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions

Authors :
Carla António
Alisdair R. Fernie
Hirofumi Ishihara
Ronan Sulpice
Mark Stitt
Abdelhalim Larhlimi
Hendrik Tschoep
Sabrina Kleessen
Yves Gibon
Joachim Selbig
Zoran Nikoloski
Plant Metabolism
University of Cambridge [UK] (CAM)
Universität Potsdam
Max Planck Institute of Molecular Plant Physiology (MPI-MP)
Max-Planck-Gesellschaft
Laboratoire d'Informatique de Nantes Atlantique (LINA)
Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)
Biologie du fruit et pathologie (BFP)
Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1
Max-Planck-Institut für Molekulare Pflanzenphysiologie (MPI-MP)
Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)
Source :
Plant Physiology, Plant Physiology, American Society of Plant Biologists, 2013, 162 (1), pp.347-63. ⟨10.1104/pp.112.210104⟩, PLANT PHYSIOLOGY
Publication Year :
2013

Abstract

Natural genetic diversity provides a powerful tool to study the complex interrelationship between metabolism and growth. Profiling of metabolic traits combined with network-based and statistical analyses allow the comparison of conditions and identification of sets of traits that predict biomass. However, it often remains unclear why a particular set of metabolites is linked with biomass and to what extent the predictive model is applicable beyond a particular growth condition. A panel of 97 genetically diverse Arabidopsis (Arabidopsis thaliana) accessions was grown in near-optimal carbon and nitrogen supply, restricted carbon supply, and restricted nitrogen supply and analyzed for biomass and 54 metabolic traits. Correlation-based metabolic networks were generated from the genotype-dependent variation in each condition to reveal sets of metabolites that show coordinated changes across accessions. The networks were largely specific for a single growth condition. Partial least squares regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range of 0.27–0.58 and 0.21–0.51 and P values in the range of

Details

Language :
English
ISSN :
00320889 and 15322548
Database :
OpenAIRE
Journal :
Plant Physiology, Plant Physiology, American Society of Plant Biologists, 2013, 162 (1), pp.347-63. ⟨10.1104/pp.112.210104⟩, PLANT PHYSIOLOGY
Accession number :
edsair.doi.dedup.....cdbf117fd55a738a7b330c61387eb0bc