Abstract
β-glucosidases are a class of enzyme that are widely distributed in the living world, with examples noted in plants, fungi, animals and bacteria. They offer both hydrolysis and synthesis capacity for a wide range of biotechnological processes. However, the availability of native, or the production of recombinant β-glucosidases, is currently a bottleneck in the widespread industrial application of this enzyme. In this present work, the production of recombinant β-glucosidase from Streptomyces griseus was optimised using a Design of Experiments strategy, comprising a two-stage, multi-model design. Three screening models were comparatively employed: Fractional Factorial, Plackett-Burman and Definitive Screening Design. Four variables (temperature, incubation time, tryptone, and OD600 nm) were experimentally identified as having statistically significant effects on the production of S.griseus recombinant β-glucosidase in E. coli BL21 (DE3). The four most influential variables were subsequently used to optimise recombinant β-glucosidase production, employing Central Composite Design under Response Surface Methodology. Optimal levels were identified as: OD600 nm, 0.55; temperature, 26 °C; incubation time, 12 h; and tryptone, 15 g/L. This yielded a 2.62-fold increase in recombinant β-glucosidase production, in comparison to the pre-optimised process. Affinity chromatography resulted in homogeneous, purified β-glucosidase that was characterised in terms of pH stability, metal ion compatibility and kinetic rates for p-nitrophenyl-β-D-glucopyranoside (pNPG) and cellobiose catalysis.
Highlights
Recombinant protein expression has traditionally been an empirical process that required running a large number of experiments to explore many influencing variables [1]
The expression of recombinant β-glucosidase, similar to other recombinant proteins, is influenced by the expression host strain, and by expression conditions and media composition [2,3]. β-glucosidase catalyses the hydrolysis of β-1,4-glycosidic bonds, and its industrial applications are well documented [4,5]
In an attempt to address this issue of poor production yields, this study applied a statistical approach, Design of Experiments (DoE), to enhance the production of a recombinant β-glucosidase
Summary
Recombinant protein expression has traditionally been an empirical process that required running a large number of experiments to explore many influencing variables (e.g., expression vectors, hosts, expression conditions and media compositions) [1]. The expression of recombinant β-glucosidase, similar to other recombinant proteins, is influenced by the expression host strain, and by expression conditions and media composition [2,3]. Low yields of this enzyme have been a bottleneck for industrial applications, such as saccharification for biofuels production, and enzymatic synthesis of alkyl-glycosides and oligosaccharides, where large enzyme concentrations are needed [6,7]. In an attempt to address this issue of poor production yields, this study applied a statistical approach, Design of Experiments (DoE), to enhance the production of a recombinant β-glucosidase. Enhanced production of recombinant β-glucosidase, following DoE, has been detailed for recombinant β-glucosidases from a variety of sources, such as Pichia pastoris [3], A. niger
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