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Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques
Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques
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Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques
Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques

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Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques
Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques
Journal Article

Enhancement of the production of L-glutaminase, an anticancer enzyme, from Aeromonas veronii by adaptive and induced mutation techniques

2017
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Overview
Microbial anti-cancer enzymes have been proven to be effective and economical agents for cancer treatment. Aeromonas veronii has been identified as a microorganism with the potential to produce L-glutaminase, an anticancer agent effective against acute lymphocytic leukaemia. In this study, a selective medium of Aeromonas veronii was used to culture the microorganism. Strain improvement was done by adaptive and induced mutational techniques. A selective minimal agar media was incorporated for the growth of the strain which further supports adaptive mutation. Strains were also UV-irradiated and successively treated with N-methyl-N'-nitro-N-nitrosoguanidine to find a resilient strain capable of producing L-glutaminase efficiently. The Plackett-Burman design and central composite designs were used to screen and optimize additional carbon and nitrogen sources. Adaptive mutation resulted in promising yield improvements compared to native strain (P<0.001). The mean yield of 30 treated colonies from the induced mutation was significantly increased compared to the non-induced strain (P< 0.001). The economically feasible statistical designs were found to reinforce each other in order to maximize the yield of the enzyme. The interactions of nutrient factors were understood from the 3D response surface plots. The model was found to be a perfect fit in terms of maximizing enzyme yield, with the productivity improving at every stage to a fourfold output of enzyme (591.11 ±7.97 IU/mL) compared to the native strain (135±3.51 IU/mL).