Application of medium optimization tools for improving recombinant human interferon gamma production from Kluyveromyces lactis

Abstract

ABSTRACTThe present study is focused upon improving biomass of Kluyveromyces lactis cells expressing recombinant human interferon gamma (hIFN-γ), with the aim of augmenting hIFN-γ concentration using statistical and artificial intelligence approach. Optimization of medium components viz., lactose, yeast extract, and trace elements were performed with Box–Behnken design (BBD) and artificial neural network linked genetic algorithm (ANN-GA) for maximizing biomass of recombinant K. lactis (objective function). The studies resulted over 1.5-fold improvement in the biomass concentration in a medium composed of 80 g/L lactose, 10.353 g/L yeast extract, and 15 mL/L trace elements as compared with initial biomass value. In the same study hIFN-γ concentration reached 881 µg/L which was 2.28-fold higher as compared with initial hIFN-γ concentration obtained in unoptimized medium. Further the batch fermentation study displayed mixed growth associated kinetics with the maximum hIFN-γ production rate of 1.1 mg/L. BBD and ANN-GA, both optimization techniques predicted a higher lactose concentration was clearly beneficial for augmenting K. lactis biomass which in turn increased hIFN-γ concentration.