@article{SulpiceNikoloskiTschoepetal.2013,
  author    = {Sulpice, Ronan and Nikoloski, Zoran and Tschoep, Hendrik and Antonio, Carla and Kleessen, Sabrina and Larhlimi, Abdelhalim and Selbig, Joachim and Ishihara, Hirofumi and Gibon, Yves and Fernie, Alisdair R. and Stitt, Mark},
  title     = {Impact of the Carbon and Nitrogen Supply on Relationships and Connectivity between Metabolism and Biomass in a Broad Panel of Arabidopsis Accessions(1[W][OA])},
  series = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants},
  volume    = {162},
  journal   = {Plant physiology : an international journal devoted to physiology, biochemistry, cellular and molecular biology, biophysics and environmental biology of plants},
  number    = {1},
  publisher = {American Society of Plant Physiologists},
  address   = {Rockville},
  issn      = {0032-0889},
  doi       = {10.1104/pp.112.210104},
  pages     = {347 -- 363},
  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 <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.},
  language  = {en}
}
@article{ChildsWituckaWallGuentheretal.2010,
  author    = {Childs, Liam H. and Witucka-Wall, Hanna and Guenther, Torsten and Sulpice, Ronan and Korff, Maria V. and Stitt, Mark and Walther, Dirk and Schmid, Karl J. and Altmann, Thomas},
  title     = {Single feature polymorphism (SFP)-based selective sweep identification and association mapping of growth- related metabolic traits in Arabidopsis thaliana},
  issn      = {1471-2164},
  doi       = {10.1186/1471-2164-11-188},
  year      = {2010},
  abstract  = {Background: Natural accessions of Arabidopsis thaliana are characterized by a high level of phenotypic variation that can be used to investigate the extent and mode of selection on the primary metabolic traits. A collection of 54 A. thaliana natural accession-derived lines were subjected to deep genotyping through Single Feature Polymorphism (SFP) detection via genomic DNA hybridization to Arabidopsis Tiling 1.0 Arrays for the detection of selective sweeps, and identification of associations between sweep regions and growth-related metabolic traits. Results: A total of 1,072,557 high-quality SFPs were detected and indications for 3,943 deletions and 1,007 duplications were obtained. A significantly lower than expected SFP frequency was observed in protein-, rRNA-, and tRNA-coding regions and in non- repetitive intergenic regions, while pseudogenes, transposons, and non-coding RNA genes are enriched with SFPs. Gene families involved in plant defence or in signalling were identified as highly polymorphic, while several other families including transcription factors are depleted of SFPs. 198 significant associations between metabolic genes and 9 metabolic and growth-related phenotypic traits were detected with annotation hinting at the nature of the relationship. Five significant selective sweep regions were also detected of which one associated significantly with a metabolic trait. Conclusions: We generated a high density polymorphism map for 54 A. thaliana accessions that highlights the variability of resistance genes across geographic ranges and used it to identify selective sweeps and associations between metabolic genes and metabolic phenotypes. Several associations show a clear biological relationship, while many remain requiring further investigation.},
  language  = {en}
}
@article{SulpicePylIshiharaetal.2009,
  author    = {Sulpice, Ronan and Pyl, Eva-Theresa and Ishihara, Hirofumi and Trenkamp, Sandra and Steinfath, Matthias and Witucka-Wall, Hanna and Gibon, Yves and Usadel, Bj{\"o}rn and Poree, Fabien and Piques, Maria Conceicao and von Korff, Maria and Steinhauser, Marie Caroline and Keurentjes, Joost J. B. and Guenther, Manuela and Hoehne, Melanie and Selbig, Joachim and Fernie, Alisdair R. and Altmann, Thomas and Stitt, Mark},
  title     = {Starch as a major integrator in the regulation of plant growth},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.0903478106},
  year      = {2009},
  abstract  = {Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1- phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.},
  language  = {en}
}