@article{LisecRoemischMarglNikoloskietal.2011,
  author    = {Lisec, Jan and R{\"o}misch-Margl, Lilla and Nikoloski, Zoran and Piepho, Hans-Peter and Giavalisco, Patrick and Selbig, Joachim and Gierl, Alfons and Willmitzer, Lothar},
  title     = {Corn hybrids display lower metabolite variability and complex metabolite inheritance patterns},
  series = {The plant journal},
  volume    = {68},
  journal   = {The plant journal},
  number    = {2},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0960-7412},
  doi       = {10.1111/j.1365-313X.2011.04689.x},
  pages     = {326 -- 336},
  year      = {2011},
  abstract  = {We conducted a comparative analysis of the root metabolome of six parental maize inbred lines and their 14 corresponding hybrids showing fresh weight heterosis. We demonstrated that the metabolic profiles not only exhibit distinct features for each hybrid line compared with its parental lines, but also separate reciprocal hybrids. Reconstructed metabolic networks, based on robust correlations between metabolic profiles, display a higher network density in most hybrids as compared with the corresponding inbred lines. With respect to metabolite level inheritance, additive, dominant and overdominant patterns are observed with no specific overrepresentation. Despite the observed complexity of the inheritance pattern, for the majority of metabolites the variance observed in all 14 hybrids is lower compared with inbred lines. Deviations of metabolite levels from the average levels of the hybrids correlate negatively with biomass, which could be applied for developing predictors of hybrid performance based on characteristics of metabolite patterns.},
  language  = {en}
}
@article{HavermannGhirardoSchnitzleretal.2022,
  author    = {Havermann, Felix and Ghirardo, Andrea and Schnitzler, J{\"o}rg-Peter and Nendel, Claas and Hoffmann, Mathias and Kraus, David and Grote, R{\"u}diger},
  title     = {Modeling intra- and interannual variability of BVOC emissions from maize, oil-seed rape, and ryegrass},
  series = {Journal of advances in modeling earth systems},
  volume    = {14},
  journal   = {Journal of advances in modeling earth systems},
  number    = {3},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {1942-2466},
  doi       = {10.1029/2021MS002683},
  pages     = {22},
  year      = {2022},
  abstract  = {Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction-mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.},
  language  = {en}
}
@article{deAbreueLimaLiWenetal.2018,
  author    = {de Abreu e Lima, Francisco Anastacio and Li, Kun and Wen, Weiwei and Yan, Jianbing and Nikoloski, Zoran and Willmitzer, Lothar and Brotman, Yariv},
  title     = {Unraveling lipid metabolism in maize with time-resolved multi-omics data},
  series = {The plant journal},
  volume    = {93},
  journal   = {The plant journal},
  number    = {6},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0960-7412},
  doi       = {10.1111/tpj.13833},
  pages     = {1102 -- 1115},
  year      = {2018},
  abstract  = {Maize is the cereal crop with the highest production worldwide, and its oil is a key energy resource. Improving the quantity and quality of maize oil requires a better understanding of lipid metabolism. To predict the function of maize genes involved in lipid biosynthesis, we assembled transcriptomic and lipidomic data sets from leaves of B73 and the high-oil line By804 in two distinct time-series experiments. The integrative analysis based on high-dimensional regularized regression yielded lipid-transcript associations indirectly validated by Gene Ontology and promoter motif enrichment analyses. The co-localization of lipid-transcript associations using the genetic mapping of lipid traits in leaves and seedlings of a B73 x By804 recombinant inbred line population uncovered 323 genes involved in the metabolism of phospholipids, galactolipids, sulfolipids and glycerolipids. The resulting association network further supported the involvement of 50 gene candidates in modulating levels of representatives from multiple acyl-lipid classes. Therefore, the proposed approach provides high-confidence candidates for experimental testing in maize and model plant species.},
  language  = {en}
}