Statistical optimization of cold-active chitinase production by mutagenized cells of multi-enzyme producing Bacillus cereus GA6

Abstract

In the present study, four cold-adapted bacterial isolates were screened for multiple-enzyme production at low temperature (15°C). The most potent isolate, Bacillus cereus GA6 (HQ832575), was subjected to mutation by UV radiation to obtain a mutant strain with elevated enzyme production. The mutant strain, designated as CUVGA6, with higher chitinase activity at low temperature was selected for enzyme production optimization using factorial design and response-surface methodology (RSM). Two statistically significant parameters (colloidal chitin and KH 2 PO 4 ) for response were selected (p value = 0.008 and 0.004, respectively) along with pH and temperature and utilized to optimize the process. Central composite design of RSM was used to optimize the levels of key ingredients for the best yield of chitinase. Maximum chitinase production was predicted to be 428.57 U/ml for a 4.4-fold increase in medium containing 2 % colloidal chitin, 6.0 g/L K 2 HPO 4 and pH 9.0 at 25°C when incubated for 7 days in submerged fermentation. ANOVA of CCD suggested that the quadratic interaction effect of K 2 HPO 4 with chitin, temperature and pH has high impact on the production of chitinase (p value = 0.007, 0.002, 0.035, respectively), although its linear effect was not significant as observed. The closeness of optimized values (R 2 = 82.28 %) to experimental values (R 2 = 80.13 %) proved the validity of statistical model. Thus, multi-enzyme producing cold-adapted mutant B. cereus GA6 (CUVGA6) could be exploited for the production of chitinase which is of industrial significance.

The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production

“…The mathematical models employed in DoE define the process under study [91]. Screening designs such as Plackett Burman Design are based on a first order model [92] as shown in Equation (1). normalY=sans-serifβ0+Σsans-serifβiXi where Y is the response, β0 is the model intercept, βi is the linear coefficient and Xi is the level of the independent variables.…”

“…These may be scatter plots, histograms, bar charts and Pareto charts that allow the researcher to identify the distribution of the data and statistical significance of the variables tested [85]. Different screening analysis methods have been used in the field of protein production [77,92,112,164]. Figure 5 illustrates a typical DoE data analysis and interpretation route from data visualisation, through experiment validation to conclusion.…”

Optimization of hexavalent chromium removal from aqueous solutions using Eichhornia crassipes-derived adsorbents through a response surface methodology

“…In addition, the application of statistical techniques such as experimental design can help optimize the process of adsorbing Cr(VI) and reduce the limitations of the one factor at a time (OFAT) approach. Statistical optimisation enables a decrease in the overall number of experiments, which allows for a quicker screening of a large number of parameters in a shorter period of time [18][19][20].…”

Enhancement of β-1,3-1,4-Glucanase Production by Marine halomonas meridiana ES021 via Statistical Optimization and Cell Immobilization

“…With 3D illustration on x, y and z axis, the response surface methodology (RSM) is applied as a statistical and mathematical optimization method that can be used to optimize the most three significant factors selected by Plackett-Burman design (Myers et al, 2004). Medium optimization through statistical experimental designs has received a lot of attention in recent years, and there have been a lot of published studies about how to utilize them in enzymes production (Felse & Panda, 1999;Roohi & Kuddus, 2015).…”