title: Impact of the carbon and nitrogen supply on relationships and connectivity between metabolism and biomass in a broad panel of Arabidopsis accessions.
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 <0.001-<0.13 and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the partial least squares regression were mainly condition specific and often related to the resource that restricts growth under that condition. Linear mixed-model analysis using the combined metabolic traits from all growth conditions as an input indicated that inclusion of random effects for the conditions improves predictions of biomass. Thus, robust prediction of biomass across a range of conditions requires condition-specific measurement of metabolic traits to take account of environment-dependent changes of the underlying networks.
authors.affiliation: Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
authors.forename: Ronan
authors.initials: R
authors.lastname: Sulpice
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nikoloski
authors.forename: Hendrik
authors.initials: H
authors.lastname: Tschoep
authors.forename: Carla
authors.initials: C
authors.lastname: Antonio
authors.forename: Sabrina
authors.initials: S
authors.lastname: Kleessen
authors.forename: Abdelhalim
authors.initials: A
authors.lastname: Larhlimi
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Ishihara
authors.forename: Yves
authors.initials: Y
authors.lastname: Gibon
authors.forename: Alisdair R
authors.initials: AR
authors.lastname: Fernie
authors.forename: Mark
authors.initials: M
authors.lastname: Stitt
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000254:growth & development / Q000378:metabolism / Q000502:physiology*; D018533:Biomass; D002244:Carbon / Q000378:metabolism*; D004777:Environment; D005838:Genotype; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D009584:Nitrogen / Q000378:metabolism*; D010641:Phenotype; D012044:Regression Analysis
chemical_list: D002244:Carbon; D009584:Nitrogen