TY - JOUR
T1 - Comparative performance of different scale-down simulators of substrate gradients in Penicillium chrysogenum cultures
T2 - the need of a biological systems response analysis
AU - Wang, Guan
AU - Zhao, Junfei
AU - Haringa, Cees
AU - Tang, Wenjun
AU - Xia, Jianye
AU - Chu, Ju
AU - Zhuang, Yingping
AU - Zhang, Siliang
AU - Deshmukh, Amit T.
AU - van Gulik, Walter
AU - Heijnen, Joseph J.
AU - Noorman, Henk J.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - 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.
AB - 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.
UR - http://resolver.tudelft.nl/uuid:21c0153d-98a7-4132-8e27-ee0632180beb
UR - http://www.scopus.com/inward/record.url?scp=85045453343&partnerID=8YFLogxK
U2 - 10.1111/1751-7915.13046
DO - 10.1111/1751-7915.13046
M3 - Article
AN - SCOPUS:85045453343
SN - 1751-7907
VL - 11
SP - 486
EP - 497
JO - Microbial Biotechnology
JF - Microbial Biotechnology
IS - 3
ER -