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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/13941

Title: Role of extracellular protease in nitrogen substrate management during antibiotic fermentation: a process model and experimental validation
Authors: BAPAT, PM
SINHA, A
WANGIKAR, PP
Keywords: RIFAMYCIN-B FERMENTATION
EXOCELLULAR PROTEASE
NEUROSPORA-CRASSA
MICROBIAL-GROWTH
SERINE PROTEASE
COMPLEX MEDIA
PHASE-SHIFTS
BIOSYNTHESIS
ACID
SPORULATION
Issue Date: 2011
Publisher: SPRINGER
Citation: APPLIED MICROBIOLOGY AND BIOTECHNOLOGY,91(4)1019-1028
Abstract: Kinetics of extracellular protease (ECP) production has typically been studied for processes that involve protease as a product. We argue that ECP is equally important in fermentations where protease is not a product of interest. Industrial fermentations typically use complex nitrogen substrates, which are proteolytically hydrolyzed to amino acids (AA) by ECP before assimilation. However, high AA concentrations may lead to nitrogen catabolite repression (NCR) of the products such as antibiotics. Thus, ECP plays a crucial role in managing the nitrogen substrate supply thereby affecting the antibiotic productivity. Here, we have studied the induction of ECP and its effect on the antibiotic productivity for a rifamycin B overproducer strain Amycolatopsis meditterranei S699. This organism produces ECP at the level of 14 U mL(-1) in complex media, which is sufficient for hydrolysis of proteins in the media but low compared to other ECP overproducers. We find ECP secretion to be repressed by ammonia, AA, and under conditions that support high growth rate. We propose a structured kinetic model which accounts for the kinetics of ECP secretion, amino acid availability, growth, and antibiotic production. In addition to the quantity, the timing of ECP induction was critical in achieving higher rifamycin productivity. We artificially created conditions that led to delayed protease secretion, which in turn led to premature termination of batch and lower productivity. The predictive value of the model can be useful in better management of the available nitrogen supply, minimization of NCR, and in the monitoring of fermentation batches.
URI: http://dx.doi.org/10.1007/s00253-011-3318-z
http://dspace.library.iitb.ac.in/jspui/handle/100/13941
ISSN: 0175-7598
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