Flux response of glycolysis and storage metabolism during rapid feast/famine conditions in Penicillium chrysogenum using dynamic super(13)C labeling

The scale-up of fermentation processes frequently leads to a reduced productivity compared to small-scale screening experiments. Large-scale mixing limitations that lead to gradients in substrate and oxygen availability could influence the microorganism performance. Here, the impact of substrate gradients on a penicillin G producing Penicillium chrysogenum cultivation was analyzed using an intermittent glucose feeding regime. The intermittent feeding led to fluctuations in the extracellular glucose concentration between 400 mu M down to 6.5 mu M at the end of the cycle. The intracellular metabolite concentrations responded strongly and showed up to 100-fold changes. The intracellular flux changes were estimated on the basis of dynamic super(13)C mass isotopomer measurements during three cycles of feast and famine using a novel hybrid modeling approach. The flux estimations indicated a high turnover of internal and external storage metabolites in P. chrysogenum under feast/famine conditions. The synthesis and degradation of storage requires cellular energy (ATP and UTP) in competition with other cellular functions including product formation. Especially, 38% of the incoming glucose was recycled once in storage metabolism. This result indicated that storage turnover is increased under dynamic cultivation conditions and contributes to the observed decrease in productivity compared to reference steady-state conditions. The performance of microorganisms is reduced under large-scale bioprocess conditions and thus culture dynamics have to be taken into account for optimal design of the cultivation process. In this study, authors analyze the impact of substrate gradients on penicillin G-producing Penicillium chrysogenum using an intermittent glucose feeding regime. The approach used in this study suggests that vivo kinetic models will become increasingly available and that these models can be applied to improve the performance of organisms in large-scale cultures.