@article{SprengerErbanSeddigetal.2017,
  author    = {Sprenger, Heike and Erban, Alexander and Seddig, Sylvia and Rudack, Katharina and Thalhammer, Anja and Le, Mai Q. and Walther, Dirk and Zuther, Ellen and Koehl, Karin I. and Kopka, Joachim and Hincha, Dirk K.},
  title     = {Metabolite and transcript markers for the prediction of potato drought tolerance},
  series = {Plant Biotechnology Journal},
  volume    = {16},
  journal   = {Plant Biotechnology Journal},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {1467-7644},
  doi       = {10.1111/pbi.12840},
  pages     = {939 -- 950},
  year      = {2017},
  abstract  = {Potato (Solanum tuberosum L.) is one of the most important food crops worldwide. Current potato varieties are highly susceptible to drought stress. In view of global climate change, selection of cultivars with improved drought tolerance and high yield potential is of paramount importance. Drought tolerance breeding of potato is currently based on direct selection according to yield and phenotypic traits and requires multiple trials under drought conditions. Marker-assisted selection (MAS) is cheaper, faster and reduces classification errors caused by noncontrolled environmental effects. We analysed 31 potato cultivars grown under optimal and reduced water supply in six independent field trials. Drought tolerance was determined as tuber starch yield. Leaf samples from young plants were screened for preselected transcript and nontargeted metabolite abundance using qRT-PCR and GC-MS profiling, respectively. Transcript marker candidates were selected from a published RNA-Seq data set. A Random Forest machine learning approach extracted metabolite and transcript markers for drought tolerance prediction with low error rates of 6\% and 9\%, respectively. Moreover, by combining transcript and metabolite markers, the prediction error was reduced to 4.3\%. Feature selection from Random Forest models allowed model minimization, yielding a minimal combination of only 20 metabolite and transcript markers that were successfully tested for their reproducibility in 16 independent agronomic field trials. We demonstrate that a minimum combination of transcript and metabolite markers sampled at early cultivation stages predicts potato yield stability under drought largely independent of seasonal and regional agronomic conditions.},
  language  = {en}
}
@article{ShouBremerRindfleischetal.2019,
  author    = {Shou, Keyun and Bremer, Anne and Rindfleisch, Tobias and Knox-Brown, Patrick and Hirai, Mitsuhiro and Rekas, Agata and Garvey, Christopher J. and Hincha, Dirk K. and Stadler, Andreas M. and Thalhammer, Anja},
  title     = {Conformational selection of the intrinsically disordered plant stress protein COR15A in response to solution osmolarity - an X-ray and light scattering study},
  series = {Physical chemistry, chemical physics : a journal of European Chemical Societies},
  volume    = {21},
  journal   = {Physical chemistry, chemical physics : a journal of European Chemical Societies},
  number    = {34},
  publisher = {Royal Society of Chemistry},
  address   = {Cambridge},
  issn      = {1463-9076},
  doi       = {10.1039/c9cp01768b},
  pages     = {18727 -- 18740},
  year      = {2019},
  abstract  = {The plant stress protein COR15A stabilizes chloroplast membranes during freezing. COR15A is an intrinsically disordered protein (IDP) in aqueous solution, but acquires an alpha-helical structure during dehydration or the increase of solution osmolarity. We have used small- and wide-angle X-ray scattering (SAXS/WAXS) combined with static and dynamic light scattering (SLS/DLS) to investigate the structural and hydrodynamic properties of COR15A in response to increasing solution osmolarity. Coarse-grained ensemble modelling allowed a structure-based interpretation of the SAXS data. Our results demonstrate that COR15A behaves as a biomacromolecule with polymer-like properties which strongly depend on solution osmolarity. Biomacromolecular self-assembly occurring at high solvent osmolarity is initiated by the occurrence of two specific structural subpopulations of the COR15A monomer. The osmolarity dependent structural selection mechanism is an elegant way for conformational regulation and assembly of COR15A. It highlights the importance of the polymer-like properties of IDPs for their associated biological function.},
  language  = {en}
}