Metabolic flux analysis in a nonstationary system: Fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol

Abstract

SUMMARYMetabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of E. coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism. KeywordsInstationary fluxes; 13C flux analysis; elementary metabolite units (EMU); gas chromatography mass spectrometry (GC/MS); statistical analysis

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

“…We next considered the metabolic model of the 1, 3-propanediol (PDO) producing Escherichia coli strain including 74 metabolites, 75 reactions, and 4,806 isotopomers (Antoniewicz et al, 2007c). All fluxes are numbered and metabolites are named in agreement with the nomenclature scheme used in Antoniewicz et al (2007c).…”

“…All fluxes are numbered and metabolites are named in agreement with the nomenclature scheme used in Antoniewicz et al (2007c). As in the original article, we did not account for the symmetry of glycerol to allow for a fair comparison of the obtained results.…”

“…As in the original article, we did not account for the symmetry of glycerol to allow for a fair comparison of the obtained results. In Antoniewicz et al (2007c), eight external fluxes ðv 66-72 ; v 75 Þ, four IDVs (Gluc, Cit, Glyc, and CO 2 ), and partial measurements from 12 amino acids (23 distinct fragment MDVs) were measured to obtain the best-fit flux distribution in the model. According to the relative costs of Table I, this original measurement set has a total relative cost of 58.…”

“…absent in the original article (Antoniewicz et al, 2007c), which helps resolve the PDO biosynthesis pathway. We tested the effectiveness of I PDO measurement by adding it to the original measurement set and solving TestUniq for various substrate labeling patterns (cf.…”

“…In addition, a Figure 5. Network representation of the partial measurements for the medium-size E. coli network (Antoniewicz et al, 2007c). Panel (A) shows the biosynthesis of each amino acid whose fragment MDVs were measured.…”