@article{ApriyantoCompartZimmermannetal.2022,
  author    = {Apriyanto, Ardha and Compart, Julia and Zimmermann, Vincent and Alseekh, Saleh and Fernie, Alisdair and Fettke, J{\"o}rg},
  title     = {Indication that starch and sucrose are biomarkers for oil yield in oil palm (Elaeis guineensis Jacq.)},
  series = {Food chemistry},
  volume    = {393},
  journal   = {Food chemistry},
  publisher = {Elsevier},
  address   = {New York, NY [u.a.]},
  issn      = {0308-8146},
  doi       = {10.1016/j.foodchem.2022.133361},
  pages     = {11},
  year      = {2022},
  abstract  = {Oil palm (Elaeis guineensis Jacq.) is the most productive oil-producing crop per hectare of land. The oil that accumulates in the mesocarp tissue of the fruit is the highest observed among fruit-producing plants. A comparative analysis between high-, medium-, and low-yielding oil palms, particularly during fruit development, revealed unique characteristics. Metabolomics analysis was able to distinguish accumulation patterns defining of the various developmental stages and oil yield. Interestingly, high- and medium-yielding oil palms exhibited substantially increased sucrose levels compared to low-yielding palms. In addition, parameters such as starch granule morphology, granule size, total starch content, and starch chain length distribution (CLD) differed significantly among the oil yield categories with a clear correlation between oil yield and various starch parameters. These results provide new insights into carbohydrate and starch metabolism for biosynthesis of oil palm fruits, indicating that starch and sucrose can be used as novel, easy-to-analyze, and reliable biomarker for oil yield.},
  language  = {en}
}
@phdthesis{Jueppner2014,
  author    = {J{\"u}ppner, Jessica},
  title     = {Characterization of metabolomic dynamics in synchronized Chlamydomonas reinhardtii cell cultures and the impact of TOR inhibition on cell cycle, proliferation and growth},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-76923},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 153},
  year      = {2014},
  abstract  = {The adaptation of cell growth and proliferation to environmental changes is essential for the surviving of biological systems. The evolutionary conserved Ser/Thr protein kinase "Target of Rapamycin" (TOR) has emerged as a major signaling node that integrates the sensing of numerous growth signals to the coordinated regulation of cellular metabolism and growth. Although the TOR signaling pathway has been widely studied in heterotrophic organisms, the research on TOR in photosynthetic eukaryotes has been hampered by the reported land plant resistance to rapamycin. Thus, the finding that Chlamydomonas reinhardtii is sensitive to rapamycin, establish this unicellular green alga as a useful model system to investigate TOR signaling in photosynthetic eukaryotes. The observation that rapamycin does not fully arrest Chlamydomonas growth, which is different from observations made in other organisms, prompted us to investigate the regulatory function of TOR in Chlamydomonas in context of the cell cycle. Therefore, a growth system that allowed synchronously growth under widely unperturbed cultivation in a fermenter system was set up and the synchronized cells were characterized in detail. In a highly resolved kinetic study, the synchronized cells were analyzed for their changes in cytological parameters as cell number and size distribution and their starch content. Furthermore, we applied mass spectrometric analysis for profiling of primary and lipid metabolism. This system was then used to analyze the response dynamics of the Chlamydomonas metabolome and lipidome to TOR-inhibition by rapamycin The results show that TOR inhibition reduces cell growth, delays cell division and daughter cell release and results in a 50\% reduced cell number at the end of the cell cycle. Consistent with the growth phenotype we observed strong changes in carbon and nitrogen partitioning in the direction of rapid conversion into carbon and nitrogen storage through an accumulation of starch, triacylglycerol and arginine. Interestingly, it seems that the conversion of carbon into triacylglycerol occurred faster than into starch after TOR inhibition, which may indicate a more dominant role of TOR in the regulation of TAG biosynthesis than in the regulation of starch. This study clearly shows, for the first time, a complex picture of metabolic and lipidomic dynamically changes during the cell cycle of Chlamydomonas reinhardtii and furthermore reveals a complex regulation and adjustment of metabolite pools and lipid composition in response to TOR inhibition.},
  language  = {en}
}
@article{MorenoCurtidorAnnunziataGuptaetal.2020,
  author    = {Moreno Curtidor, Catalina and Annunziata, Maria Grazia and Gupta, Saurabh and Apelt, Federico and Richard, Sarah Isabel and Kragler, Friedrich and M{\"u}ller-R{\"o}ber, Bernd and Olas, Justyna Jadwiga},
  title     = {Physiological profiling of embryos and dormant seeds in two Arabidopsis accessions reveals a metabolic switch in carbon reserve accumulation},
  series = {Frontiers in plant science},
  volume    = {11},
  journal   = {Frontiers in plant science},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2020.588433},
  pages     = {14},
  year      = {2020},
  abstract  = {In flowering plants, sugars act as carbon sources providing energy for developing embryos and seeds. Although most studies focus on carbon metabolism in whole seeds, knowledge about how particular sugars contribute to the developmental transitions during embryogenesis is scarce. To develop a quantitative understanding of how carbon composition changes during embryo development, and to determine how sugar status contributes to final seed or embryo size, we performed metabolic profiling of hand-dissected embryos at late torpedo and mature stages, and dormant seeds, in two Arabidopsis thaliana accessions with medium [Columbia-0 (Col-0)] and large [Burren-0 (Bur-0)] seed sizes, respectively. Our results show that, in both accessions, metabolite profiles of embryos largely differ from those of dormant seeds. We found that developmental transitions from torpedo to mature embryos, and further to dormant seeds, are associated with major metabolic switches in carbon reserve accumulation. While glucose, sucrose, and starch predominantly accumulated during seed dormancy, fructose levels were strongly elevated in mature embryos. Interestingly, Bur-0 seeds contain larger mature embryos than Col-0 seeds. Fructose and starch were accumulated to significantly higher levels in mature Bur-0 than Col-0 embryos, suggesting that they contribute to the enlarged mature Bur-0 embryos. Furthermore, we found that Bur-0 embryos accumulated a higher level of sucrose compared to hexose sugars and that changes in sucrose metabolism are mediated by sucrose synthase (SUS), with SUS genes acting non-redundantly, and in a tissue-specific manner to utilize sucrose during late embryogenesis.},
  language  = {en}
}
@phdthesis{Schwahn2018,
  author    = {Schwahn, Kevin},
  title     = {Data driven approaches to infer the regulatory mechanism shaping and constraining levels of metabolites in metabolic networks},
  doi       = {10.25932/publishup-42324},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {109},
  year      = {2018},
  abstract  = {Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers. This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs. I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis. Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings. I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach. The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.},
  language  = {en}
}