@phdthesis{Arend2024,
  author    = {Arend, Marius},
  title     = {Comparing genome-scale models of protein-constrained metabolism in heterotrophic and photosynthetic microorganisms},
  doi       = {10.25932/publishup-65147},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-651470},
  school      = {Universit{\"a}t Potsdam},
  pages     = {150},
  year      = {2024},
  abstract  = {Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.},
  language  = {en}
}
@phdthesis{Winck2011,
  author    = {Winck, Flavia Vischi},
  title     = {Nuclear proteomics and transcription factor profiling in Chlamydomonas reinhardtii},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-53909},
  school      = {Universit{\"a}t Potsdam},
  year      = {2011},
  abstract  = {The transcriptional regulation of the cellular mechanisms involves many different components and different levels of control which together contribute to fine tune the response of cells to different environmental stimuli. In some responses, diverse signaling pathways can be controlled simultaneously. One of the most important cellular processes that seem to possess multiple levels of regulation is photosynthesis. A model organism for studying photosynthesis-related processes is the unicellular green algae Chlamydomonas reinhardtii, due to advantages related to culturing, genetic manipulation and availability of genome sequence. In the present study, we were interested in understanding the regulatory mechanisms underlying photosynthesis-related processes. To achieve this goal different molecular approaches were followed. In order to indentify protein transcriptional regulators we optimized a method for isolation of nuclei and performed nuclear proteome analysis using shotgun proteomics. This analysis permitted us to improve the genome annotation previously published and to discover conserved and enriched protein motifs among the nuclear proteins. In another approach, a quantitative RT-PCR platform was established for the analysis of gene expression of predicted transcription factor (TF) and other transcriptional regulator (TR) coding genes by transcript profiling. The gene expression profiles for more than one hundred genes were monitored in time series experiments under conditions of changes in light intensity (200 µE m-2 s-1 to 700 µE m-2 s-1), and changes in concentration of carbon dioxide (5\% CO2 to 0.04\% CO2). The results indicate that many TF and TR genes are regulated in both environmental conditions and groups of co-regulated genes were found. Our findings also suggest that some genes can be common intermediates of light and carbon responsive regulatory pathways. These approaches together gave us new insights about the regulation of photosynthesis and revealed new candidate regulatory genes, helping to decipher the gene regulatory networks in Chlamydomonas. Further experimental studies are necessary to clarify the function of the candidate regulatory genes and to elucidate how cells coordinately regulate the assimilation of carbon and light responses.},
  language  = {en}
}
@article{GietlerNykielOrzechowskietal.2016,
  author    = {Gietler, Marta and Nykiel, Malgorzata and Orzechowski, Slawomir and Zagdanska, Barbara and Fettke, J{\"o}rg},
  title     = {Proteomic analysis of S-nitrosylated and S-glutathionylated proteins in wheat seedlings with different dehydration tolerances},
  series = {Plant physiology and biochemistry : an official journal of the Federation of European Societies of Plant Physiology},
  volume    = {108},
  journal   = {Plant physiology and biochemistry : an official journal of the Federation of European Societies of Plant Physiology},
  publisher = {Elsevier},
  address   = {Paris},
  issn      = {0981-9428},
  doi       = {10.1016/j.plaphy.2016.08.017},
  pages     = {507 -- 518},
  year      = {2016},
  abstract  = {A loss of dehydration tolerance in wheat seedlings on the fifth day following imbibition is associated with a disturbance in cellular redox homeostasis, as documented by a shift of the reduced/oxidized glutathione ratio to a more oxidized state and a significant increase in the ratio of protein thiols to the total thiol group content. Therefore, the identification and characterization of redox-sensitive proteins are important steps toward understanding the molecular mechanisms of the loss of dehydration tolerance. In the present study, proteins that were differentially expressed between fully turgid (control), dehydrated tolerant (four-day-old) and dehydrated sensitive (six-day-old) wheat seedlings were analysed. Protein spots having at least a significant (p < 0.05) two-fold change in protein abundance were selected by Delta2D as differentially expressed, identified by MALDI-TOF and LC-MS/MS, and classified according to their function. The observed changes in the proteomic patterns of the differentially S-nitrosylated and S-glutathionylated proteins were highly specific in dehydration-tolerant and-sensitive wheat seedlings. The metabolic function of these proteins indicates that dehydration tolerance is mainly related to nucleic acids, protein metabolism, and energy metabolism. It has been proven that leaf-specific thionins BTH6 and DB4, chloroplastic 50S ribosomal protein L16, phospholipase A1-II delta, and chloroplastic thioredoxin M2 are both S-nitrosylated and S-glutathionylated upon water deficiency. Our results revealed the existence of interplay between S-nitrosylation and S-glutathionylation, two redox-regulated protein posttranslational modifications that could enhance plant defence mechanisms and/or facilitate the acclimation of plants to unfavourable environmental conditions. (C) 2016 Elsevier Masson SAS. All rights reserved.},
  language  = {en}
}
@article{ArendZimmerXuetal.2023,
  author    = {Arend, Marius and Zimmer, David and Xu, Rudan and Sommer, Frederik and M{\"u}hlhaus, Timo and Nikoloski, Zoran},
  title     = {Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale},
  series = {Nature Communications},
  volume    = {14},
  journal   = {Nature Communications},
  number    = {1},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-023-40498-1},
  pages     = {9},
  year      = {2023},
  abstract  = {Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.},
  language  = {en}
}