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

IF 3.6 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Journal, genetic engineering & biotechnology Pub Date : 2023-11-24 DOI:10.1186/s43141-023-00618-2
Zuhour Hussein Wardah, Hiral G Chaudhari, Vimalkumar Prajapati, Gopalkumar G Raol
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Abstract

Background: Actinomycetes 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).

Results: Out 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.

Conclusions: The 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.

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应用统计方法优化假灰链霉菌zh20固态发酵条件下l -谷氨酰胺酶产率。
背景:放线菌是各种化学结构(如酶)的优良微生物来源,大多数用于制药和工业产品。放线菌是酶的首选来源,因为它们具有生产细胞外酶的高能力。l -谷氨酰胺酶已被证明其在癌症治疗中的重要作用,以及在食品工业中的经济作用。本研究旨在筛选强效的l -谷氨酰胺酶产生菌,并利用固态发酵(SSF)下的一因子一次法(OFAT)和统计方法优化生产培养基,以获得最大的酶产量。结果:从根际土壤分离的20株放线菌中,有5株产生胞外l -谷氨酰胺酶。筛选出1株最强毒力菌株,经16S rRNA鉴定为假灰链霉菌ZHG20。在SSF条件下进行生产和优化,经OFAT法优化后,酶产率提高至884.61 U/gds。此外,采用统计策略、响应面法(RSM)和中心复合设计(CCD)对重要介质成分的水平进行优化(p)。结论:本研究揭示了利用OFAT和RSM-CCD对各种理化参数的优化。结果表明,采用统计学方法可有效提高S. pseudogriseolus zh20的胞外l -谷氨酰胺酶的产量,在pH 7.0、33°C条件下,将两种固体底物(麦麸和芝麻油饼)混合培养6天,l -谷氨酰胺酶活性达到1297.87 U/gds,比未优化培养基的l -谷氨酰胺酶活性提高3.21倍。
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