High quality $^{13}$C metabolic flux analysis using GC-MS

  • Hochqualitative $^{13}$C Stoffflussanalyse mittels GC-MS

Schmitz, Andreas; Blank, Lars Mathias (Thesis advisor); Oldiges, Marco (Thesis advisor)

1st ed.. - Aachen : Apprimus-Verlag (2018)
Book, Dissertation / PhD Thesis

In: Applied microbiology 10
Page(s)/Article-Nr.: 1 Online-Ressource (XVII, 140 Seiten) : Illustrationen, Diagramme

Dissertation, RWTH Aachen University, 2018


In order to reduce the demand of fossil resources such as crude oil or natural gas, biotechnological processes are increasingly applied for product manufacturing using biological hosts. To enhance the production efficiency, these biological hosts are generally optimized by means of metabolic engineering. Since good knowledge of intracellular processes in the selected biological hosts is mandatory prior metabolic engineering, these intracellular processes have to be investigated in advance by methods such as $^{13}$C-Metabolic Flux Analysis (MFA).The present thesis was focused on improving analytical techniques for the measurement of $^{13}$C-labeling pattern of metabolites. First, parameters influencing data quality of existing gaschromatography-mass spectrometry (GC-MS) based analytical techniques for the classic $^{13}$C-MFA, which is based on the analysis of proteinogenic amino acids, were analyzed. Scanmode, integration method and the amount of biomass analyzed emerged as the main parameters impacting the quality of the labeling data. On average, a 3.5-fold improvement of the data quality was achieved with measurements run in Single Ion Monitoring (SIM) mode. We set up a detailed protocol for sample preparation and GC-MS analysis to enable scientists new to this field to easily produce labeling data of high quality for $^{13}$C-MFA.$^{13}$C-MFA using labeling data of intracellular metabolites is becoming increasingly relevant in biotechnological research. It allows to perform $^{13}$C-tracer experiments at shorter time scales thereby reducing cost for the $^{13}$C-tracer but more importantly makes the analysis applicable for systems not maintaining stable metabolic steady states. Therefore, sample preparation and measurement methods for labeling determination of intracellular metabolites were implemented and further developed. Due to the high turnover rates of intracellular metabolites during growth, special attention was paid to the rapid termination of intracellular processes. Metabolite quenching with cold ethanol-saline solution was applied and showed best results for Pseudomonas putida strains. The metabolism was stopped immediately and intracellular metabolites were extracted subsequently using a cold methanol-chloroform-water solution. The developed methods were used for an in-depth analysis of the glucose uptake in Pseudomonas and for the examination of cyclic Entner-Doudoroff-Pathway fluxes. Compared to $^{13}$C-MFA with proteinogenic amino acids, the use of intracellular metabolites increased the information content and therefore additional fluxes could be resolved. Besides classical GC-MS approaches, the potential of GC-MS/MS analyses to increase the positional $^{13}$C-label information of the MS data by introducing a second fragmentation step was investigated as such positional information about $^{13}$C-isotopes can be exploited to resolve additional metabolic fluxes. For these GC-MS/MS measurements, cells were grown and harvested as for classical analysis of amino acids with GC-MS. Leucine and lysine were found to be promising amino acids in terms of acetyl-CoA labeling determination when being fragmented in tandem MS measurements. Labeling experiments with 1-$^{13}$C-glucose and U-$^{13}$C-glucose revealed differences in the labeling of cytosolic and mitochondrial acetyl-CoA indicating that there is no high exchange of these two compartmental pools. The methods presented in this study are highly sophisticated and are suited for determining the labeling of various metabolites. The information gained by application of these methods is suitable for use in $^{13}$C-MFA and thus enables a deeper insight into the metabolic activity in the cell.