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

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 selecti...

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Bibliographic Details
Published in:PLoS ONE
Main Author: Aravinth Vijay Jesuraj S.; Moklesur Rahman Sarker Md.; Ming L.C.; Marylin Jeya Praya S.; Ravikumar M.; Wui W.T.
Format: Article
Language:English
Published: Public Library of Science 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031786262&doi=10.1371%2fjournal.pone.0181745&partnerID=40&md5=3ed1938fa7bc209cedb42db832b06c58
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Summary: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). Copyright: © 2017 Jesuraj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ISSN:19326203
DOI:10.1371/journal.pone.0181745