DataEntryID 121
    General information
      Manuscript title: Metabolome, transcriptome and metabolic flux analysis of arabinose fermentation by engineered Saccharomyces cerevisiae.
      PubMed ID: http://www.ncbi.nlm.nih.gov/pubmed/20816840
      Journal: Metabolic Engineering
      Year: 2010
      Authors: H. Wouter Wisselink, Chiara Cipollina, Bart Oud, BarbaraCrimi, Joseph J. Heijnen, Jack T. Pronk, Antonius J.A.van Maris
      Affiliations: Department of Biotechnology, Delft University of Technology, Julianalaan 67, 2628 BC Delft, The Netherlands.
      Keywords: Saccharomyces cerevisiae, Evolutionary engineering, Arabinose, Transcriptomics, Metabolomics, Metabolic flux analysis
      Full text article: https://www.kimosys.org/rails/active_storage/blobs/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBdWNFIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--3eaf3d8aa6e111633d7061bb7d5febe4ccb4a451/Wisselink_2010.pdf
      Project name: not specified

    Experiment description
      Organism: Saccharomyces cerevisiae
      Strain: IMS0001 and IMS0002 
      Data type: flux measurements
      Data units: normalised to the specific glucose uptake rate
      Execution date: not specified

    Experimental details
      Temperature (°C): 30
      pH: 5.0
      Carbon source: glucose and arabinose
      Culture mode: chemostat
      Process condition: anaerobic
      Dilution rate (h⁻¹): 0.03
      Working volume: 1.0 L
      Biomass concentration (g/L): Biomass yield (g g−1) =  0.066±0.001 (IMS0001, Glucose), 0.072±0.003 (IMS0002, Glucose) and 0.075±0.001 (IMS0002, Arabinose) 
      Medium composition: Cultures were performed in MY supplemented with 0.01 g l−1 ergosterol and 0.42 g l−1 Tween 80 dissolved in ethanol, silicon antifoam, vitamin solution and trace elements [7], and 20 g l−1 glucose (MYG) or arabinose (MYA).
      General protocol information: Type analysis list: 13C constrained MFA; Platform list: LC-MS;
      Methods description: Intracellular metabolic fluxes were calculated through metabolic flux balancing using a compartmented stoichiometric model that was previously developed for describing aerobic growth on glucose [1,2] and modified to describe anaerobic growth on glucose and arabinose. Reactions required for production and excretion of organic acids and for transport of oleate and palmitoleate (required for lipid biosynthesis) and the fumarate reductase reaction (TCA reductive branch) were introduced [1, 3]. Four reactions necessary for arabinose utilization were added to the model: arabinose transport, arabinose isomerase, l-ribulokinase and l-ribulose-5-phosphate-4-epimerase. The assumed macromolecular biomass composition was 45% (w/w) protein, 40.7% (w/w) polysaccharides, 6.3% (w/w) RNA, 0.4% (w/w) DNA, 2.9% (w/w) lipid, 2.5% (w/w) metals, 1.2% (w/w) water, 0.8% (w/w) phosphate and 0.2% (w/w) sulfate [2, 4]. Measured protein contents of IMS0001 and IMS0002 strains agreed with the assumed value (data not shown).                                                                                                                                                                                                                                                                                                                                                     Dedicated software (SPAD it, Nijmegen, The Netherlands) was used for metabolic flux balancing, the theory and practice of which have been thoroughly described elsewhere (4, 5, 6) and will not be repeated here. For each growth condition, specific rates of growth, substrate consumption and carbon-dioxide, glycerol, acetate, pyruvate, lactate and succinate production were measured. For the conversion of fluxes into mmoles per Cmole of biomass, a biomass C-molar weight of 25.87 g mol−1 was used  [2, 4]. The number of measured rates was sufficient to result in an over-determined system, thus enabling data reconciliation. In all cases the degree of redundancy equaled 2.                                                                                                                                                                                                                                                                                                                                                                          --------------------------------------------References--------------------------------------- 
[1] P. Daran-Lapujade, M.L. Jansen, J.M. Daran, W. van Gulik, J.H. de Winde, J.T. Pronk. J. Biol. Chem., 279 (2004), pp. 9125-9138. http://doi.org/dhhj2k
[2] Lange, H.C., 2002. Quantitative physiology of S. cerevisiae using metabolic network analysis. Ph.D. Thesis, Delft University of Technology, Department of Biotechnology, the Netherlands.
[3] K. Enomoto, Y. Arikawa, H. Muratsubaki. FEMS Microbiol. Lett., 215 (2002), pp. 103-108. http://doi.org/c6x6g6
[4] T.L. Nissen, U. Schulze, J. Nielsen, J. Villadsen.Microbiology, 143 (1997), pp. 203-218. http://doi.org/dwvx2q
[5] Vallino, J.J., Stephanopoulos, G., 1990.  In: S.K. Skidar, M. Bier, P. Todd (Eds.), Frontiers in Bioprocess. CRC Press, Boca Raton, FL, pp. 205–219.
[6] W.M. van Gulik, J.J. Heijnen. Biotechnol. Bioeng., 48 (1995), pp. 681-698. http://doi.org/fsv4g6
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      Created: 2018-08-28 16:39:16 UTC
      Updated: 2020-04-24 16:10:37 UTC
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