Comprehensive assessment of measurement uncertainty in 13C-based metabolic flux experiments

• DOI: 10.1007/s00216-018-1017-7
• Publication Date: 2018-04-13T00:22:54Z
• Authors: Teresa Mairinger; Wolfhard Wegscheider; David Alejandro Peña; Matthias G. Steiger; Gunda Koellensperger; Jürgen Zanghellini; Stephan Hann
• Keywords: 0301 basic medicine; 104002 Analytische Chemie; Carbon Isotopes; Measurement uncertainty; Uncertainty; Isotope interference correction; Metabolic flux analysis; Models, Biological; Metabolic Flux Analysis; Pichia; 03 medical and health sciences; 104002 Analytical chemistry; Metabolic Engineering; Metabolome; Metabolomics; Computer Simulation; Isotopologue analysis; Metabolic engineering; Monte Carlo Method; Metabolic Networks and Pathways; Research Paper

In the field of metabolic engineering 13C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. Graphical abstract ᅟ.