Dataset Information

Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001.

ABSTRACT: Physical and chemical factors influencing the anti-leukemic l-asparaginase enzyme production by Bacillus subtilis VUVD001 were optimized using multi-stage optimization on the basis of preliminary experimental outcomes obtained by conventional one-factor-at-a-time approach using shake flasks. Process variables namely carbon, nitrogen sources, pH and temperature were taken into consideration during response surface methodology (RSM) optimization. The finest enzyme activity of 0.51 IUml-1 obtained by OFAT method was enhanced by 3.2 folds using RSM optimization. Artificial neural network (ANN) modelling and genetic algorithm (GA) based optimizations were further carried out to improve the enzyme drug yield. Results were also validated by conducting experiments at optimum conditions determined by RSM and GA optimization methods. The novel bacterium yielded in 2.88 IUml-1 of enzyme activity at optimum process variables determined by GA optimization, i.e., 0.5% glucose, 8.0% beef extract, 8.3 pH and 49.9 °C temperature. The study explored the optimized culture conditions for better yielding of anti-leukemic enzyme drug from a new bacterial source namely Bacillus subtilis VUVD001.

SUBMITTER: Erva RR

PROVIDER: S-EPMC5736497 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001.

Erva Rajeswara Reddy RR Venkateswarulu T C TC Pagala Bangaraiah B

3 Biotech 20171219 1

Physical and chemical factors influencing the anti-leukemic l-asparaginase enzyme production by Bacillus subtilis VUVD001 were optimized using multi-stage optimization on the basis of preliminary experimental outcomes obtained by conventional one-factor-at-a-time approach using shake flasks. Process variables namely carbon, nitrogen sources, pH and temperature were taken into consideration during response surface methodology (RSM) optimization. The finest enzyme activity of 0.51 IUml ...[more]

PMID: 29279817

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Project description:Bacillus subtilis is a soil-dwelling bacterium that can form biofilms, or communities of cells surrounded by a self-produced extracellular matrix. In biofilms, genetically identical cells often exhibit heterogeneous transcriptional phenotypes, so that subpopulations of cells carry out essential yet costly cellular processes that allow the entire population to thrive. Surprisingly, the extent of phenotypic heterogeneity and the relationships between subpopulations of cells within biofilms of even in well-studied bacterial systems like B. subtilis remains largely unknown. To determine relationships between these subpopulations of cells, we created 182 strains containing pairwise combinations of fluorescent transcriptional reporters for the expression state of 14 different genes associated with potential cellular subpopulations. We determined the spatial organization of the expression of these genes within biofilms using confocal microscopy, which revealed that many reporters localized to distinct areas of the biofilm, some of which were co-localized. We used flow cytometry to quantify reporter co-expression, which revealed that many cells "multi-task," simultaneously expressing two reporters. These data indicate that prior models describing B. subtilis cells as differentiating into specific cell types, each with a specific task or function, were oversimplified. Only a few subpopulations of cells, including surfactin and plipastatin producers, as well as sporulating and competent cells, appear to have distinct roles based on the set of genes examined here. These data will provide us with a framework with which to further study and make predictions about the roles of diverse cellular phenotypes in B. subtilis biofilms. IMPORTANCE Many microbes differentiate, expressing diverse phenotypes to ensure their survival in various environments. However, studies on phenotypic differentiation have typically examined only a few phenotypes at one time, thus limiting our knowledge about the extent of differentiation and phenotypic overlap in the population. We investigated the spatial organization and gene expression relationships for genes important in B. subtilis biofilms. In doing so, we mapped spatial gene expression patterns and expanded the number of cell populations described in the B. subtilis literature. It is likely that other bacteria also display complex differentiation patterns within their biofilms. Studying the extent of cellular differentiation in other microbes may be important when designing therapies for disease-causing bacteria, where studying only a single phenotype may be masking underlying phenotypic differentiation relevant to infection outcomes.

Dataset Information

Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001.

Publications

Multi level statistical optimization of l-asparaginase from <i>Bacillus subtilis VUVD001</i>.

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