Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation

Abstract

BackgroundMapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation.ResultsIntracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism.ConclusionsThe CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.

Compartment‐specific metabolomics for CHO reveals that ATP pools in mitochondria are much lower than in cytosol

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A roadmap for interpreting 13 C metabolite labeling patterns from cells

“…Integrating dynamic labeling data into metabolic models has mainly been applied to microbial systems, although other systems have been recently investigated as well [34,39,86–89]. For a meaningful direct interpretation of dynamic labeling patterns a suitable time resolution ( i.e.…”

Untargeted metabolomics of neuronal cell culture: A model system for the toxicity testing of insecticide chemical exposure

“…The analysis of extracellular metabolites from this model may provide a clearer picture of the state of degraded cells that have released their internal metabolites into the surrounding medium. This could further uncover a living cell's metabolic state (Aurich et al, ; Nicolae, Wahrheit, Bahnemann, Zeng, & Heinzle, ) in the moments immediately preceding cell death, and comparison of this to intracellular metabolites of remaining viable cells may help to interpret where the ‘points of no return’ might be for the metabolic state of a cell in response to toxins.…”