Statistical enhancement of lipase extracellular production by Bacillus stratosphericus PSP8 in a batch submerged fermentation process.

AIM

The aim of this study was to isolate and identify lipolytic bacteria. Perform a statistical stepwise physicochemical optimization for maximum production of extracellular lipase and its validation in a bioreactor.

METHODS AND RESULTS

Several lipolytic bacteria were isolated from petroleum hydrocarbon-polluted soil. The strain expressing the highest lipase activity (47 U ml ) was genetically identified as Gram-positive Bacillus stratosphericus PSP8 (NCBI GenBank accession no. MH120423). The response surface methodology (RSM)-central composite face centre (CCF) design of experiments was performed based on the preselected levels of the studied parameters obtained from the performed one-factor-at-a-time sequential experiments. A second-order polynomial model was predicted and improved the lipase production by approximately 1·6-fold. Preliminary scaling up of the validated optimized process was carried out in a batch 10-l stirred tank bioreactor, applying the optimum predicted operating conditions; pH 6·98, 34·8°C, 2·2 × 10 cells per ml, 200 rev min , 4·82 g l tributyrine concentration, 1% sucrose and 0·1% yeast extract. This yielded 89 U ml at the late log phase of bacterial growth (48 h). Logistic kinetic model effectively characterized the submerged fermentation process, and the maximum specific growth and lipase production rates were estimated to be 0·338 and 0·164 h respectively.

CONCLUSIONS

The mesophilic and neutrophilic B. stratosphericus PSP8 isolated from petroleum hydrocarbon-contaminated soil is a proper source of lipase. The closeness of the predicted response with that of the experimental value and the enhancement of lipase productivity in fermenter scale by approximately 1·9-fold, showed that statistically optimized design can be used in order to improve the lipase production to meet the increasing demand.

SIGNIFICANCE AND IMPACT OF THE STUDY

The RSM-CCF statistical optimization is useful for optimizing a large number of variables and studying their interactive effects on extracellular lipase production.