@article{SchwarteWegnerHavensteinetal.2015,
  author    = {Schwarte, Sandra and Wegner, Fanny and Havenstein, Katja and Groth, Detlef and Steup, Martin and Tiedemann, Ralph},
  title     = {Sequence variation, differential expression, and divergent evolution in starch-related genes among accessions of Arabidopsis thaliana},
  series = {Plant molecular biology : an international journal of fundamental research and genetic engineering},
  volume    = {87},
  journal   = {Plant molecular biology : an international journal of fundamental research and genetic engineering},
  number    = {4-5},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0167-4412},
  doi       = {10.1007/s11103-015-0293-2},
  pages     = {489 -- 519},
  year      = {2015},
  abstract  = {Transitory starch metabolism is a nonlinear and highly regulated process. It originated very early in the evolution of chloroplast-containing cells and is largely based on a mosaic of genes derived from either the eukaryotic host cell or the prokaryotic endosymbiont. Initially located in the cytoplasm, starch metabolism was rewired into plastids in Chloroplastida. Relocation was accompanied by gene duplications that occurred in most starch-related gene families and resulted in subfunctionalization of the respective gene products. Starch-related isozymes were then evolutionary conserved by constraints such as internal starch structure, posttranslational protein import into plastids and interactions with other starch-related proteins. 25 starch-related genes in 26 accessions of Arabidopsis thaliana were sequenced to assess intraspecific diversity, phylogenetic relationships, and modes of selection. Furthermore, sequences derived from additional 80 accessions that are publicly available were analyzed. Diversity varies significantly among the starch-related genes. Starch synthases and phosphorylases exhibit highest nucleotide diversities, while pyrophosphatases and debranching enzymes are most conserved. The gene trees are most compatible with a scenario of extensive recombination, perhaps in a Pleistocene refugium. Most genes are under purifying selection, but disruptive selection was inferred for a few genes/substitutiones. To study transcript levels, leaves were harvested throughout the light period. By quantifying the transcript levels and by analyzing the sequence of the respective accessions, we were able to estimate whether transcript levels are mainly determined by genetic (i.e., accession dependent) or physiological (i.e., time dependent) parameters. We also identified polymorphic sites that putatively affect pattern or the level of transcripts.},
  language  = {en}
}
@article{SteinfathStrehmelPetersetal.2010,
  author    = {Steinfath, Matthias and Strehmel, Nadine and Peters, Rolf and Schauer, Nicolas and Groth, Detlef and Hummel, Jan and Steup, Martin and Selbig, Joachim and Kopka, Joachim and Geigenberger, Peter and Dongen, Joost T. van},
  title     = {Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach},
  issn      = {1467-7644},
  doi       = {10.1111/j.1467-7652.2010.00516.x},
  year      = {2010},
  abstract  = {Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.},
  language  = {en}
}