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General Information info

Manuscript title Sampling of intracellular metabolites for stationary and non-stationary 13C metabolic flux analysis in Escherichia coli
PubMed ID 25102204
Journal Analytical Biochemistry
Year 2014
Authors Pierre Millard, Stéphane Massou, Christoph wittmann, Jean-Charles Portais, Fabien Létisse
Affiliations Université de Toulouse, INSA, UPS, INP, LISBP, F-31077 Toulouse, France; INRA, UMR 792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France; CNRS, UMR 5504, F-31400 Toulouse, France
Keywords (13)C-labeling experiments, Isotopes, Isotopologue, Mass isotopomer, Metabolism, Sampling procedures
Full text article Downloadarticle Millard_2014.pdf
Project name not specified

Experiment Description info

Organism Escherichia coli
Strain K-12 MG1655
Data type flux measurements
Data units % substrate uptake
Execution date not specified

Experimental Details info

Temperature (0C) 37.0
pH 7.0
Carbon source glucose,
Culture mode batch
Process condition aerobic
Dilution rate (h-1)
Working volume (L) 0.150
Biomass concentration (g/L) not specified
Medium composition

Minimal synthetic medium: 5 mM KH2PO4, 10 mM Na2HPO4, 9 mM NaCl, 40 mM NH4Cl, 0.8 mM MgSO4, 0.1 mM CaCl2, 0.1 g/L thiamine, and 3 g/L glucose. Glucose and thiamine were sterilized by filtration (Minisart polyamide 0.2 μm, Sartorius, Göttingen, Germany), and other solutions were autoclaved separately.

General protocol information Flux analysis method: 13C constrained MFA

Platform: NMR, GC-MS, LC-MS

Methods description - Notes

Quantitative isotopic analyses
Analysis of extracellular metabolites by one-dimensional 1H NMR:

Aliquots (500 μl) of filtered broth (0.2 μm, Sartorius) were mixed with 100 μl of D2O and then analyzed with an Avance 500-MHz spectrometer (Bruker, Rheinstetten, Germany) equipped with a 5-mm z-gradient BBI probe at a temperature of 286 K. A sequence using presaturation (ZGPR) was used for water signal suppression, with a 30° pulse and a relaxation delay between scans of 20 s to ensure full signal recovery. A total of 64 scans were accumulated (64k data points with a spectral width of 10 ppm) after 8 dummy scans. From each spectrum, we quantified the four isotopomers of acetate and the 13C enrichment of the anomeric carbon of glucose. In the non-stationary experiment, 13C enrichment was quantified from samples collected just after the end of the experiment.

Analyses of proteinogenic amino acids by two-dimensional NMR:
NMR spectra of samples of proteinogenic amino acids were recorded with an Avance 500-MHz spectrometer (Bruker) equipped with a 5-mm z-gradient BBI probe at a temperature of 286 K. The specific enrichments were quantified using a ZQF–TOCSY (zero quantum filter–total correlation spectroscopy) sequence as described in [1]. For each 512 increments in the F1 dimension, 16k data points were acquired in the F2 dimension (8 dummy scans and 16 scans with a delay of 5 s between scans), with a spectral width of 10 ppm in each dimension. The positional isotopomers were quantified using an HSQC (heteronuclear single quantum correlation) sequence as described in [2]. For each 8k increments in the F1 (13C) dimension, 4k data points were acquired in the F2 (1H) dimension (32 dummy scans and 8 scans with a delay of 2 s between scans), with a spectral width of 150 and 10 ppm in the F1 and F2 dimensions, respectively. Spectra were processed using TopSpin 2 (Bruker) as described in [1] and [2].

Analyses of proteinogenic amino acids by GC–MS:
The isotopic pattern of t-butyldimethylsilyl derivatives of proteinogenic amino acids was quantified by GC–MS (HP 7890, inert MSD 57979C, Agilent, Waldbronn, Germany) as described previously [3]. All samples were first measured in scan mode [4]. The relative fractions of the isotopologues of interest were then determined in duplicate in selective ion monitoring (SIM) mode.

Analysis of intracellular metabolites by ion chromatography–tandem mass spectrometry:
After resuspension of cell extracts in 200 μl of MilliQ water, cell debris was removed by centrifugation at 10,000g for 10 min. Samples were analyzed with a Dionex ICS 2500 system coupled to a 4000 QTrap mass spectrometer as described in the “Quantitative metabolomics” section above. Isotopic clusters of molecular ions [M−H]− were quantified in the MRM mode where phosphate fragments and fragments with a loss of carboxylic group [M−H−CO2]− were the daughter ions of phosphorylated metabolites (see above) and organic acids (citrate [CIT] and malate [MAL]), respectively. MRM transitions were chosen according to Kiefer and coworkers [5]. The injection volume was 15 μl, originating from approximately 3 μg of biomass.

Flux calculations in stationary state:
Flux calculations were performed with influx_s [6]. The metabolic network implemented in the FTBL model included all major reactions of the central carbon metabolism: glucose uptake, glycolysis (EMP), Entner–Doudoroff (ED), and pentose phosphate (PP) pathways, tricarboxylic acid (TCA) cycle, and acetate production and amino acid biosynthesis pathways. In total, the model was made up of 88 reactions for central carbon metabolism and 63 biosynthetic reactions for a total of 103 fluxes (73 unidirectional and 15 reversible reactions). Precursor requirements for biomass formation were determined according to the molecular composition of E. coli [7] and the measured growth rate. Metabolic fluxes were estimated by minimizing the variance-weighted sum of square residuals between the experimental and simulated isotopic data using the NLSIC algorithm implemented in influx_s.


Flux calculations in non-stationary state:
Non-stationary flux calculations were performed as described by Schaub and coworkers [8]. We focused this analysis on reactions upstream from the pyruvate. The metabolic network investigated here included reactions of EMP, ED, and PP pathways and output reactions of the central metabolic intermediaries toward biomass synthesis. This model contained a total of 37 fluxes (15 unidirectional and 11 reversible reactions). An iterative procedure was used to estimate metabolic fluxes. The propagation of 13C atoms through the network was simulated by solving a system of 692 differential isotopomer balance equations (implemented in Fortran) using the LSODA method in the R “deSolve” package. The presence of extracellular pools at natural abundance in the samples had a strong impact on the transient isotopic data monitored. We took label dilution into account in the calculation by summing the IDs of the extracellular metabolites (at natural abundance) and the simulated IDs of intracellular metabolites, both weighted by the fraction of each pool. Consequently, the fractions of extracellular metabolites relative to the total pools were additional parameters. Finally, the model contained 41 free parameters: the fraction of [13C]glucose in the substrate, 14 free fluxes, the concentration of 19 intracellular metabolites, and the fractions of 7 extracellular metabolites for which transient isotopic data were available. These parameters were estimated using the NLSIC algorithm by fitting simulated data to (i) time-course isotopic data of G6P, F6P, FBP, PEP, P5P, S7P, and 2/3PG, (ii) intracellular concentrations of G6P, F6P, FBP, 6PG, PEP, P5P, and 2/3PG, and (iii) extracellular fluxes. Concentrations of intracellular metabolites were constrained in a physiological range (0.01–10 mM), fractions of extracellular pools were constrained between 0 and 1, and exchange coefficients were constrained between 10−4 and 0.99. See schematic overview in Figure 1.
--------------------References---------------
[1] S. Massou, C. Nicolas, F. Letisse, J.-C. Portais, Metab. Eng., 9 (2007), pp. 252–257. http://doi.org/dz42ft
[2] S. Massou, C. Nicolas, F. Letisse, J.-C. Portais, Phytochemistry, 68 (2007), pp. 2330–2340. http://doi.org/frrtgv [3] P. Kiefer, E. Heinzle, O. Zelder, C. Wittmann, Appl. Environ. Microbiol., 70 (2004), pp. 229–239. http://doi.org/cg63zq
[4] C. Wittmann, Microb. Cell Fact., 6 (2007), p. 6. http://doi.org/cfph95
[5] P. Kiefer, C. Nicolas, F. Letisse, J.-C. Portais, Anal. Biochem., 360 (2007), pp. 182–188. http://doi.org/dws46h
[6] S. Sokol, P. Millard, J.-C. Portais, Bioinformatics, 28 (2012), pp. 687–693. http://doi.org/fzrf8p
[7] Ishii, et al. Science, 316 (2007), pp. 593–597. http://doi.org/cr2stm
[8] C. Wittmann, J.-C. Portais, M. Lämmerhofer, W. Weckwerth (Eds.), Metabolomics in Practice: Successful Strategies to Generate and Analyze Metabolic Data, John Wiley, Weinheim, Germany (2013), pp. 285–312. http://doi.org/3vg

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Submission and curation info

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 2015-04-20 14:43:51 UTC

Updated 2015-04-20 15:17:58 UTC

Version 2

Status (reviewed) 2015-04-20 15:12:15 UTC




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