Statistical and evolutionary optimization for enhanced production of an antileukemic enzyme, L-asparaginase, in a protease-deficient Bacillus aryabhattai ITBHU02 isolated from the soil contaminated with hospital waste

Abstract

Over the past few decades, L-asparaginase has emerged as an excellent anti-neoplastic agent. In present study, a new strain ITBHU02, isolated from soil site near degrading hospital waste, was investigated for the production of extracellular L-asparaginase. Further, it was renamed as Bacillus aryabhattai ITBHU02 based on its phenotypical features, biochemical characteristics, fatty acid methyl ester (FAME) profile and phylogenetic similarity of 16S rDNA sequences. The strain was found protease-deficient and its optimal growth occurred at 37 °C and pH 7.5. The strain was capable of producing enzyme L-asparaginase with maximum specific activity of 3.02±0.3 Umg-1 protein, when grown in un-optimized medium composition and physical parameters. In order to improve the production of L-asparaginase by the isolate, response surface methodology (RSM) and genetic algorithm (GA) based techniques were implemented. The data achieved through the statistical design matrix were used for regression analysis and analysis of variance studies. Furthermore, GA was implemented utilizing polynomial regression equation as a fitness function. Maximum average L-asparaginase productivity of 6.35 Umg-1 was found at GA optimized concentrations of 4.07, 0.82, 4.91, and 5.2 gL-1 for KH2PO4, MgSO4.7H2O, L-asparagine, and glucose respectively. The GA optimized yield of the enzyme was 7.8% higher in comparison to the yield obtained through RSM based optimization.