@article{SchwahndeSouzaFernieetal.2014,
  author    = {Schwahn, Kevin and de Souza, Leonardo Perez and Fernie, Alisdair R. and Tohge, Takayuki},
  title     = {Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade},
  series = {Journal of integrative plant biology},
  volume    = {56},
  journal   = {Journal of integrative plant biology},
  number    = {9},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1672-9072},
  doi       = {10.1111/jipb.12274},
  pages     = {864 -- 875},
  year      = {2014},
  abstract  = {Steroidal glycoalkaloids (SGAs) are nitrogen-containing secondary metabolites of the Solanum species, which are known to have large chemical and bioactive diversity in nature. While recent effort and development on LC/MS techniques for SGA profiling have elucidated the main pathways of SGA metabolism in tomato, the problem of peak annotation still remains due to the vast diversity of chemical structure and similar on overlapping of chemical formula. Here we provide a case study of peak classification and annotation approach by integration of species and tissue specificities of SGA accumulation for provision of comprehensive pathways of SGA biosynthesis. In order to elucidate natural diversity of SGA biosynthesis, a total of 169 putative SGAs found in eight tomato accessions (Solanum lycopersicum, S. pimpinellifolium, S. cheesmaniae, S. chmielewskii, S. neorickii, S. peruvianum, S. habrochaites, S. pennellii) and four tissue types were used for correlation analysis. The results obtained in this study contribute annotation and classification of SGAs as well as detecting putative novel biosynthetic branch points. As such this represents a novel strategy for peak annotation for plant secondary metabolites.},
  language  = {en}
}
@article{LissoAltmannMuessig2006,
  author    = {Lisso, Janina and Altmann, Thomas and M{\"u}ssig, Carsten},
  title     = {Metabolic changes in fruits of the tomato d(x) mutant},
  series = {Phytochemistry : an international journal of plant biochemistry},
  volume    = {67},
  journal   = {Phytochemistry : an international journal of plant biochemistry},
  number    = {20},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0031-9422},
  doi       = {10.1016/j.phytochem.2006.07.008},
  pages     = {2232 -- 2238},
  year      = {2006},
  language  = {en}
}
@article{RohrmannTohgeAlbaetal.2011,
  author    = {Rohrmann, Johannes and Tohge, Takayuki and Alba, Rob and Osorio, Sonia and Caldana, Camila and McQuinn, Ryan and Arvidsson, Samuel Janne and van der Merwe, Margaretha J. and Riano-Pachon, Diego Mauricio and M{\"u}ller-R{\"o}ber, Bernd and Fei, Zhangjun and Nesi, Adriano Nunes and Giovannoni, James J. and Fernie, Alisdair R.},
  title     = {Combined transcription factor profiling, microarray analysis and metabolite profiling reveals the transcriptional control of metabolic shifts occurring during tomato fruit development},
  series = {The plant journal},
  volume    = {68},
  journal   = {The plant journal},
  number    = {6},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0960-7412},
  doi       = {10.1111/j.1365-313X.2011.04750.x},
  pages     = {999 -- 1013},
  year      = {2011},
  abstract  = {Maturation of fleshy fruits such as tomato (Solanum lycopersicum) is subject to tight genetic control. Here we describe the development of a quantitative real-time PCR platform that allows accurate quantification of the expression level of approximately 1000 tomato transcription factors. In addition to utilizing this novel approach, we performed cDNA microarray analysis and metabolite profiling of primary and secondary metabolites using GC-MS and LC-MS, respectively. We applied these platforms to pericarp material harvested throughout fruit development, studying both wild-type Solanum lycopersicum cv. Ailsa Craig and the hp1 mutant. This mutant is functionally deficient in the tomato homologue of the negative regulator of the light signal transduction gene DDB1 from Arabidopsis, and is furthermore characterized by dramatically increased pigment and phenolic contents. We choose this particular mutant as it had previously been shown to have dramatic alterations in the content of several important fruit metabolites but relatively little impact on other ripening phenotypes. The combined dataset was mined in order to identify metabolites that were under the control of these transcription factors, and, where possible, the respective transcriptional regulation underlying this control. The results are discussed in terms of both programmed fruit ripening and development and the transcriptional and metabolic shifts that occur in parallel during these processes.},
  language  = {en}
}