Metabolic flux analysis at ultra short time scale: isotopically non-stationary 13C labeling experiments.
A novel approach to (13)C metabolic flux analysis (MFA) is presented using cytosolic metabolite pool sizes and their (13)C labeling data from an isotopically non-stationary (13)C labeling experiment (INST-CLE). The procedure is demonstrated with an E. coli wild type strain grown at fed batch conditions. The intra cellular labeling dynamics are excited by a sudden step increase of the (13)C portion in the substrate feed. Due to unchanged saturation of the substrate uptake system, the metabolic fluxes remain constant during the following sampling time period of only 16s, in which 20 samples are taken by an automated rapid sampling device immediately stopping metabolism by methanol quenching. Subsequent cell disruptive sample preparation and LC-MS/MS enabled simultaneous determination of pool sizes and mass isotopomers of intra cellular metabolites requiring detection limits in the nM range. Based on this data the new computational flux analysis tool 13CFLUX/INST is used to determine the intra cellular fluxes based on a complex carbon labeling network model. The measured data is in good agreement with the model predictions, thus proving the applicability of the new isotopically non-stationary (13)C metabolic flux analysis (INST-(13)C-MFA) concept. Moreover, it is shown that significant new information with respect to flux identifiability, non-measurable pool sizes, data consistency, or large storage pools can be taken from the novel kind of experimental data. This offers new insight into the biological operation of the metabolic network in vivo.
Nöh
Katharina
K
Institute of Biotechnology, Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany. k.noeh@fz-juelich.de
Grönke
Karsten
K
Luo
Bing
B
Takors
Ralf
R
Oldiges
Marco
M
Wiechert
Wolfgang
W
D019149:Bioreactors; D002244:Carbon / Q000378:metabolism*; D002247:Carbon Isotopes / Q000378:metabolism; D002853:Chromatography, Liquid; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000378:metabolism*; D005285:Fermentation / Q000502:physiology*; D053858:Metabolic Networks and Pathways / Q000502:physiology; D008954:Models, Biological; D053719:Tandem Mass Spectrometry
D002247:Carbon Isotopes; D002244:Carbon