Elucidation and modeling of the in-vivo kinetics of enzymes and membrane transporters associated with β-lactam and non-ribosomal peptide production in Penicillium chrysogenum

Even 80 years after the discovery of penicillin, it still holds 16% of total antibiotics market. This makes it crucial, from an economical point of view, to improve our understanding of the production organism Penicillium chrysogenum to maximize the penicillin production, as its theoretical yields are far from reached. With the advancement in analytical techniques and detailed knowledge of the metabolic pathways, enough information and tools are available to try to identify possible bottlenecks that limit the penicillin yield, and thus with known genome sequence there are possibilities to modify the strain by using metabolic engineering strategies. One of the aims of this study was to unravel the in vivo enzyme kinetic properties and identify possible bottlenecks in the penicillin biosynthesis pathway in Penicillium chrysogenum. To understand the mechanism of the enzymes/transporters under in vivo conditions and to estimate parameters, several different studies were carried out that included steady state and stimulus response experiments. The other aim of the study was to use Penicillium chrysogenum as a cell factory to produce non-ribosomal peptides. The strain used was an industrial high producing strain.BiotechnologyApplied Science

Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: Towards rational scale-down and design optimization

We assess the effect of substrate heterogeneity on the metabolic response of P. chrysogenum in industrial bioreactors via the coupling of a 9-pool metabolic model with Euler-Lagrange CFD simulations. In this work, we outline how this coupled hydrodynamic-metabolic modeling can be utilized in 5 steps. (1) A model response study with a fixed spatial extra-cellular glucose concentration gradient, which reveals a drop in penicillin production rate qp of 18–50% for the simulated reactor, depending on model setup. (2) CFD-based scale-down design, where we design a 1-vessel scale down simulator based on the organism lifelines. (3) Scale-down verification, numerically comparing the model response in the proposed scale-down simulator with large-scale CFD response. (4) Reactor design optimization, reducing the drop in penicillin production by a change of feed location. (5) Long-term fed-batch simulation, where we verify model predictions against experimental data, and discuss population heterogeneity. Overall, these steps present a coupled hydrodynamic-metabolic approach towards bioreactor evaluation, scale-down and optimization.ChemE/Transport PhenomenaOLD BT/Cell Systems EngineeringBT/Bioprocess Engineerin

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

In a 54 m3 large-scale penicillin fermentor, the cells experience substrate gradient cycles at the timescales of global mixing time about 20–40 s. Here, we used an intermittent feeding regime (IFR) and a two-compartment reactor (TCR) to mimic these substrate gradients at laboratory-scale continuous cultures. The IFR was applied to simulate substrate dynamics experienced by the cells at full scale at timescales of tens of seconds to minutes (30 s, 3 min and 6 min), while the TCR was designed to simulate substrate gradients at an applied mean residence time ((Formula presented.)) of 6 min. A biological systems analysis of the response of an industrial high-yielding P. chrysogenum strain has been performed in these continuous cultures. Compared to an undisturbed continuous feeding regime in a single reactor, the penicillin productivity (qPenG) was reduced in all scale-down simulators. The dynamic metabolomics data indicated that in the IFRs, the cells accumulated high levels of the central metabolites during the feast phase to actively cope with external substrate deprivation during the famine phase. In contrast, in the TCR system, the storage pool (e.g. mannitol and arabitol) constituted a large contribution of carbon supply in the non-feed compartment. Further, transcript analysis revealed that all scale-down simulators gave different expression levels of the glucose/hexose transporter genes and the penicillin gene clusters. The results showed that qPenG did not correlate well with exposure to the substrate regimes (excess, limitation and starvation), but there was a clear inverse relation between qPenG and the intracellular glucose level.ChemE/Transport PhenomenaOLD BT/Cell Systems EngineeringBT/Bioprocess Engineerin