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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 accessions was grown in near-optimal C and N supply, restricted C supply and restricted N 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. PLS regression from metabolic traits allowed prediction of biomass within and, slightly more weakly, across conditions (cross-validated Pearson correlations in the range 0.27-0.58 and 0.21-0.51; p-values in the range <0.001-<0.13, and <0.001-<0.023, respectively). Metabolic traits that correlate with growth or have a high weighting in the PLS 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.