@article{NaglerNaegeleGillietal.2018,
  author    = {Nagler, Matthias and N{\"a}gele, Thomas and Gilli, Christian and Fragner, Lena and Korte, Arthur and Platzer, Alexander and Farlow, Ashley and Nordborg, Magnus and Weckwerth, Wolfram},
  title     = {Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field},
  series = {Frontiers in Plant Science},
  volume    = {9},
  journal   = {Frontiers in Plant Science},
  number    = {1556},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2018.01556},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189560},
  year      = {2018},
  abstract  = {Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.},
  language  = {en}
}
@article{AppelScholzMuelleretal.2015,
  author    = {Appel, Mirjam and Scholz, Claus-J{\"u}rgen and M{\"u}ller, Tobias and Dittrich, Marcus and K{\"o}nig, Christian and Bockstaller, Marie and Oguz, Tuba and Khalili, Afshin and Antwi-Adjei, Emmanuel and Schauer, Tamas and Margulies, Carla and Tanimoto, Hiromu and Yarali, Ayse},
  title     = {Genome-Wide Association Analyses Point to Candidate Genes for Electric Shock Avoidance in Drosophila melanogaster},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {5},
  doi       = {10.1371/journal.pone.0126986},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-152006},
  pages     = {e0126986},
  year      = {2015},
  abstract  = {Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/or sequences covaried with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance- associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hairlike organs distributed across the fly's body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms.},
  language  = {en}
}