Optimization of culture conditions for penicilazaphilone C production by a marine-derived fungus Penicillium sclerotiorum M-22.

ABSTRACT

The aim of this study was to optimize the culture conditions of a marine-derived fungus Penicillium sclerotiorum M-22 for the production of penicilazaphilone C (PAC), a novel azaphilonidal derivative exhibiting broad cytotoxic and antibacterial effects. By single factor experiments, the effects to the production of PAC of aged seawater concentration, initial pH values, fermentation time, carbon sources, nitrogen sources and inorganic salt sources were investigated individually. Response surface methodology (RSM) analysis was adopted to investigate the interactions between variables and determine the optimal values for maximum PAC production. Evaluation of the experimental results signified that the optimum conditions for maximum production of PAC (19·85 mg l-1 ) in 250 ml Erlenmeyer flask were fermentation time 24·83 days, pH of 7·00, corn meal concentration of 10·72 g l-1 , yeast extract concentration of 4·58 g l-1 , crude sea salt concentration of 20·59 g l-1 . Production under optimized conditions increased to 1·344-fold comparing to its production prior to optimization. The higher PAC production and the penicilazaphilone C -producing marine fungus would be provide a promising alterative approach for industrial and commercial applications. SIGNIFICANCE AND IMPACT OF THE STUDY Penicilazaphilone C (PAC) was a novel azaphilonidal derivative which had exhibited selective cytotoxicity and antibacterial activity. To further enhance production of PAC by optimizing fermentation conditions of Penicillium sclerotiorum M-22 would provide a promising alterative approach for industrial and commercial applications. We used the single factor test to determine the key factors which influence the PAC production. Then through the Response surface methodology and Box-Behnken design to determine the best fermentation condition for maximum production of PAC. Through these experimental designs and analysis will help us improve experimental efficiency and save time and materials.