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Manuscript title Development of quantitative metabolomics for Pichia pastoris.
PubMed ID 22448155
Journal Metabolomics
Year 2012
Authors Marc Carnicer, Andre B. Canelas, Angela ten Pierick, Zhen Zeng, Jan van Dam, Joan Albiol, Pau Ferrer, Joseph J. Heijnen, Walter van Gulik
Affiliations Department of Chemical Engineering, Universitat Autonoma de Barcelona, 08193 Bellaterra (Cerdanyola del Valle`s), Spain
Keywords Pichia pastoris, Metabolite quantification, Quenching, Chemostat
Full text article Downloadarticle Carnicer_2012.pdf
Project name not specified

Experiment Description info

Organism Pichia pastoris
Strain X-33
Data type metabolites at steady-state
Data units µmol/gDCW
Execution date not specified

Experimental Details info

Temperature (0C) 25
pH 5.0
Carbon source glucose,
Culture mode chemostat
Process condition aerobic
Dilution rate (h-1) 0.1
Working volume (L) 4.0
Biomass concentration (g/L) ≈ 4.5 g/l
Medium composition

Derived from previously described media [1]. The composition of the batch medium was: 8 g/l glycerol, 0.9 g/l citric acid monohydrate, 12.6 g/l (NH4)2HPO4, 0.5 g/l MgSO4 7H20, 1.5 g/l KH2PO4, 0.02 g/l CaCl2 2H20, 5 ml/l trace salt solution, 2 ml/l Biotin solution (0.2 g/l). The composition of the chemostat medium was: 8.80 g/l glucose monohydrate, 0.92 g/l citric acid monohydrate, 2 g/l (NH4)2HPO4, 0.3 g/l MgSO4 7H20, 1.4 g/l KH2PO4, 0.01 g/l CaCl2 2H20, 0.5 ml/l trace salt solution, 0.3 ml/l Biotin (0.2 g/l). The trace salts solution was the same as described previously [1].

General protocol information Sampling method: samples for intracellular metabolite analysis were taken using a dedicated rapid-sampling setup [2].

Quenching procedure: approximately, 0.63 ± 0.01 g of broth was rapidly withdrawn and immediately injected in 5 ml of precooled quenching solution.

Extraction technique: hot ethanol

Sample analyzing method: GC-MS, LC-ESI-MS

Methods description - Notes

Sampling: The tubes were quickly mixed by vortexing and introduced in the filtration unit after weighting the tube [3]. All sampling tubes were weighted before and after the sampling procedure in order to determine the exact amount of sample taken. Briefly, the cell suspensions were filtered with membrane disk filters (Pall Corporation, East Hills, NY, USA, 47 mm diameter, 0.45 lm pore size) using a vacuum pump. A washing step was performed to remove as much extracellular metabolites as possible. Samples from the culture filtrate (CF) and the complete culture broth (WB) were withdrawn and further processed as described earlier [4]. Metabolite analysis: Metabolite quantification was carried out with LC–ESI–MS/MS and GC–MS based isotope dilution mass spectrometry (IDMS) [5]. Each sample was analyzed in duplicate. The amount of each metabolite was quantified in different sample fractions, that is, in whole broth (WB), quenched/
washed cells (QC), culture filtrate (CF) and quenching + washing liquid (QWS). The actual intracellular metabolite levels were estimated from the difference between the levels measured in whole broth (WB) and culture filtrate (CF). Protocol B was considered as the optimum quenching procedure for quantification of the intracellular metabolites in P. pastoris. Moreover, in order to evaluate the applicability of direct measurement using quenching protocol B, the obtained results were compared with the results obtained using with the differential method. A comparison of the determined intracellular metabolite levels between the direct measurement (QC) and the differential method (WB–CF) was performed for the five different variations of the cold methanol quenching protocols, to determine for which condition metabolite leakage from P. pastoris cells was minimal. ----------------------------------References--------------------------------------
[1] Baumann, K., Maurer, M., Dragosits, M., Cos, O., Ferrer, P., & Mattanovich, D. (2008). Biotechnology and Bioengineering, 100, 177–183. http://doi.org/b7hjxb
[2] Lange, H. C., Eman, M., Zuijlen, G., van Visser, D., van Dam, J. C., Frank, J., et al. (2001). Biotechnology and Bioengineering, 75, 406–415. http://doi.org/d6wqfv
[3] Douma, R. D., Jonge, L. P., de Jonker, C. T. H., Seifar, R. M., Heijnen, J. J., & van Gulik, W. M. (2010a). Biotechnology and Bioengineering, 107, 105–115. http://doi.org/cdphfq
[4] Canelas, A. B., Ras, C., ten Pierick, A., van Dam, J. C., Heijnen, J. J., & van Gulik, W. M. (2008). Metabolomics, 4, 226–239. http://doi.org/dtqddf
[5] Canelas, A. B., ten Pierick, A., Ras, C., Seifar, R. M., van Dam, J. C., van Gulik, W. M., et al. (2009). Analytical Chemistry, 81, 7379–7389. http://doi.org/djkxtk

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Entered by Administrator KiMoSysFirst name: Administrator
Affiliation: INESC-ID/IST
Homepage: http://kdbio.inesc-id.pt/kimosys
Interests: mathematical modeling, accessible data, use of data

Created 2018-08-01 15:36:45 UTC

Updated 2018-08-01 15:36:45 UTC

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Status (reviewed) 2018-08-01 15:37:19 UTC




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