Fluxome analysis using GC-MS

Abstract

Fluxome analysis aims at the quantitative analysis of in vivo carbon fluxes in metabolic networks, i. e. intracellular activities of enzymes and pathways. It allows investigating the effects of genetic or environmental modifications and thus precisely provides a global perspective on the integrated genetic and metabolic regulation within the intact metabolic network. The experimental and computational approaches developed in this area have revealed fascinating insights into metabolic properties of various biological systems. Most of the comprehensive approaches for metabolic flux studies today involve isotopic tracer studies and GC-MS for measurement of the labeling pattern of metabolites. Initially developed and applied mainly in the field of biomedicine these GC-MS based metabolic flux approaches have been substantially extended and optimized during recent years and today display a key technology in metabolic physiology and biotechnology.

Figure 1

Strategy for ¹³C metabolic flux analysis including the experimental part with the tracer study and the GC-MS labelling analysis and the computational part with the simulation of the labelling data via an isotopomer model representing the investigated metabolic network. The flux estimation is based on minimizing the deviation (δ) between the measured and the simulated labelling data.

Figure 2

Overview on different GC-MS instrumentation types. The most frequently used combination in the area of metabolic flux studies by isotope labelling is highlighted.

Figure 3

Total ion current (TIC) spectrum of a sample with TBDMS-derivatized metabolites. The separation of the totally 28 compounds is performed on a HP5-MS column (60 m, 250 μm inner diameter, Hewlett-Packard, Avondale, PA).

Figure 4

Schematic view of the ion source based on electron impact ionization and the quadrupole mass filter typically found in a GC-MS instrument.

Figure 5

Mass spectrum of TBDMS₃-alanine derived by electron impact ionization in GC-MS analysis: Naturally labeled alanine with a mass isotopomer distribution resulting from the nautrally occurring isotopes (A), alanine from the cell protein of S. cerevisiae cultivated on [1-¹³C] glucose (B), alanine from the cell protein of lysine producing C. glutamium cultivated on [1-¹³C] glucose (C). The monoisotopic mass of the molecular ion, which itself is not detected, is 317. The structures of valuable ion clusters for labeling analysis in metabolic flux studies (m/z 260, m/z 232) are additionally.

Figure 6

Experimental protocols for sampling and processing of amino acids in yeast and bacterial cell extracts, culture supernatant and biomass (protein) hydrolysate for GC-MS analysis.

Figure 7

Quantification of the flux partitioning between pentose phosphate pathway (PPP) and glycolysis: Carbon transfer from [1-¹³C] glucose in the underlying metabolic reactions (A), Influence of a variation of the relative flux into the PPP on the relative abundance of non labelled (M0) and single labelled (M1) pyruvate as determined by simulation with an isotopomer model (B).

Figure 8

Influence of the life-time of the electron multiplier (A) and of isotope discrimination effects during the GC separation (B) on GC-MS labelling analysis. The effect of the electron multiplier is exemplified for the ratio between the single and the non labelled mass isotopomer fraction of naturally labelled TBDMS₂-alanine whereby the dashed line represents the theoretical value and the experimental values result from measurement using an electron multiplier with extended life time and a new electron multiplier, respectively. The isotope discrimination effects are given for the different mass isotopomers of TBDMS₃-glutamate.

Figure 9

Relationship between the carbon skeleton of amino acids and the carbon skeleton of their metabolic precursors for the anabolic pathways in E. coli, S. cerevisiae, C. glutamicum and B. subtilis. The data are partly taken from [130].

Figure 10

Statistical analysis of metabolic fluxes using a Monte-Carlo approach exemplified for flux through major NADPH generating pathways, the pentose phosphate pathway (PPP) and the TCA cycle. The calculation is based on a previous flux study of different lysine producing strains of C. glutamicum [21] and represents 250 independent flux estimations with statistically varied experimental data for each of the five strains shown.