Application of statistical methodology for the optimization of l-glutaminase enzyme production from Streptomyces pseudogriseolus ZHG20 under solid-state fermentation

Abstract

BackgroundActinomycetes are excellent microbial sources for various chemical structures like enzymes, most of which are used in pharmaceutical and industrial products. Actinomycetes are preferred sources of enzymes due to their high ability to produce extracellular enzymes. l-glutaminase has proven its essential role as a pharmaceutical agent in cancer therapy and an economic agent in the food industry. The current study aimed to screen the potent l-glutaminase producer and optimize the production media for maximum enzyme yield using one factor at a time (OFAT) approach and statistical approaches under solid-state fermentation (SSF). ResultsOut of 20 actinomycetes strains isolated from rhizosphere soil, 5 isolates produced extracellular l-glutaminase. One isolate was chosen as the most potent strain, and identified as Streptomyces pseudogriseolus ZHG20 based on 16S rRNA. The production and optimization process were carried out under SSF, after optimization using OFAT method, the enzyme production increased up to 884.61 U/gds. Further, statistical strategy, response surface methodology (RSM), and central composite design (CCD) were employed for the level optimization of significant media component (p < 0.05), i.e., wheat bran, sesame oil cake, and corn steep liquor which are leading to increase 3.21-fold l-glutaminase production as compared to unoptimized media. ConclusionsThe presented investigation reveals the optimization of various physicochemical parameters using OFAT and RSM-CCD. Statistical approaches proved to be an effective method for increasing the yield of extracellular l-glutaminase from S. pseudogriseolus ZHG20 where l-glutaminase activity increased up to 1297.87 U/gds which is 3.21-fold higher than the unoptimized medium using a mixture of two solid substrates (wheat bran and sesame oil cake) incubated at pH 7.0 for 6 days at 33 °C.