Power input effects on degeneration in prolonged penicillin chemostat cultures: A systems analysis at flux, residual glucose, metabolite, and transcript levels

Abstract

In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m 3 in more remote zones and 3.83 kW/m 3 in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent.To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (C s,out ). At the same time, the biomass specific glucose consumption rate (q s )did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low C s,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5-and 3.3-fold, respectively, and those of the pcbC gene

Comparative performance of different scale‐down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

“…In the reference chemostat cultures, the extracellular C s remains at the level of 2.6 μM over 250 h, and this agrees with the glucose uptake kinetics (Wang et al ., ), giving a high glucose affinity of about 5 μM, in reasonable accordance with the 7.8 μM as reported by de Jonge et al . ().…”

“…This system remains in the limitation regime. An explanation is that in this experiment there is an interference with the liquid circulation time of about 1–2 s (Wang et al ., ), which is close to the feed switch‐on time interval (3 s) of the 30 s IFR. The maximum feast glucose concentrations in 30 s, 3 min and 6 min IFR experiments are 30, 167 and 333 μM (Fig.…”

“…Further, Wang et al . () observed that a very low C s may induce the expression of the high‐affinity glucose transporter genes, and the related glucose sensing can trigger metabolic rearrangement, for example futile cycling, which leads to an extra ATP consumption and eventually results in the decrease of penicillin production. Therefore, in this study, the transcript levels of the glucose/hexose transporter genes have been determined.…”

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

“…As an example, as shown in Figure , our research group has developed and validated standard operating procedures (SOPs) for fast sampling, quenching, and efficient extraction of metabolites as well as preparation of U‐ 13 C metabolites as internal standards from P. chrysogenum to allow for global analysis of the metabolome under both steady‐state and nonstationary conditions (Wang, Chu, et al, ). This protocol has been exactly followed in our previous studies, which delivered invaluable information for in silico metabolic modeling and revealed regulatory mechanisms in response to genetic or environmental perturbations (Tang et al, ; Wang, Chu, et al, ; Wang, Chu, et al, ; Wang, Wu, et al, ; Wang, Zhao, et al, ; Wang, Zhao, et al, ).…”

“…Typically, by applying different feeding strategies (e.g., intermittent feeding, stochastic feeding; de Jonge et al, ; Delvigne, Destain, & Thonart, ; Delvigne, Lejeune, Destain, & Thonart, ; Wang, Zhao, et al, ) or scale‐down simulators (Neubauer & Junne, ; Wang et al, ), spatially nonhomogeneous environments encountered by cells in large‐scale fermentors can be mimicked at laboratory‐scale bioreactors. Recent studies have shown that before industrial scaling‐up, scaling down industrial‐scale fermentation processes can gain deep insights into the interaction between metabolic dynamics and hydrodynamics in industrial‐scale fermentation processes (Buchholz et al, ; Haringa et al, ; Lemoine et al, ; Lemoine, Maya Martiotanez‐Iturralde, Spann, Neubauer, & Junne, ; Loffler et al, ; Marba‐Ardebol, Bockisch, Neubauer, & Junne, ; Nieß, Löffler, Simen, & Takors, ; Simen et al, ; Spann et al, ; Wang, Wu, et al, ; Wang, Zhao, et al, ). However, it should be noted that a prerequisite for this kind of representative downscaling experiment is the knowledge of how conditions vary in large‐scale reactors.…”