Lichenase production by catabolite repression-resistant Bacillus subtilis mutants: optimization and formulation of an agro-industrial by-product medium

Fungal β-1,3-1,4-glucanases: production, proprieties and biotechnological applications

β-1,3-1,4-glucanases (or lichenases; EC 3.2.1.73) comprise one of the main enzymes used in industry during recent decades. These enzymes hydrolyze β-glucans containing β-1,3 and β-1,4 linkages, such as cereal β-glucans and lichenan. The β-1,3-1,4-glucanases are produced by a variety of bacteria, fungi, plants and animals. A large number of microbial β-1,3-1,4-glucanases have potential application in industrial processes, such as feed, food and detergent industries. The present review summarizes the available studies with respect to β-1,3-1,4-glucanases production conditions, enzyme biochemical properties and potential industrial application. © 2018 Society of Chemical Industry.

High-level production of β-1,3-1,4-glucanase by Rhizomucor miehei under solid-state fermentation and its potential application in the brewing industry

AIMS

To improve the β-1,3-1,4-glucanase production by Rhizomucor miehei under solid-state fermentation (SSF) for industrial application.

METHODS AND RESULTS

The fermentation conditions for β-1,3-1,4-glucanase production by R. miehei CAU432 under SSF were optimized using a 'one-factor-at-a-time' method. Under the optimized fermentation conditions, viz. oatmeal (0·45-0·9 mm) as sole carbon source, 5% (w/w) peptone as sole nitrogen source, initial moisture of 80% (w/w), initial culture pH of 5·0, incubation temperature of 50°C and incubation time of 6 days, the highest β-1,3-1,4-glucanase activity of 20,025 U g(-1) dry substrate was achieved, which represents the highest yield for β-1,3-1,4-glucanase production ever reported. The crude enzyme was extracted and purified to homogeneity with a purification fold of 4·6 and a recovery yield of 9·0%. The addition of the purified β-1,3-1,4-glucanase in mash obviously reduced its filtration time (24·6%) and viscosity (2·61%).

CONCLUSIONS

The optimal fermentation conditions for maximal β-1,3-1,4-glucanase production under SSF was obtained, and the enzyme was suitable for application in the malting process.

SIGNIFICANCE AND IMPACT OF THE STUDY

The high production yield and excellent capability of the enzyme may enable it great potential in industries, especially in brewing industry.

Purification and characterization of a novel alkaline β-1,3-1,4-glucanase (lichenase) from thermophilic fungus Malbranchea cinnamomea

A novel alkaline β-1,3-1,4-glucanase (McLic1) from a thermophilic fungus, Malbranchea cinnamomea, was purified and biochemically characterized. McLic1 was purified to homogeneity with a purification fold of 3.1 and a recovery yield of 3.7 %. The purified enzyme was most active at pH 10.0 and 55 °C, and exhibited a wide range of pH stability (pH 4.0–10.0). McLic1 displayed strict substrate specificity for barley β-glucan, oat β-glucan and lichenan, but did not show activity towards other tested polysaccharides and synthetic p-nitrophenyl derivates, suggesting that it is a specific β-1,3-1,4-glucanase. The K  m values for barley β-glucan, oat β-glucan and lichenan were determined to be 0.69, 1.11 and 0.63 mg mL−1, respectively. Moreover, the enzyme was stable in various non ionic surfactants, oxidizing agents and several commercial detergents. Thus, the alkaline β-1,3-1,4-glucanase may have potential in industrial applications, such as detergent, paper and pulp industries.

Experimental design for fermentation media development: Statistical design or global random search?

The diversity of combinatorial interactions of medium components with the metabolism of cells as well as the large number of medium constituents necessary for cellular growth and production do not permit satisfactory detailed modelling. For this reason, experimental search procedures in simultaneous shaking flask experiments are used to optimise fermentation media. As an alternative to the methods of statistical experimental design employed in this field for many decades, the use of stochastic search procedures has been evaluated recently, since these require neither the unimodality of the response surface nor limitations in the number of medium components under consideration. Genetic algorithms were selected due to their basic capability for efficient exploration of large variable spaces. Using a genetic algorithm, it has been experimentally verified, with the aid of process examples, that process improvements can be achieved both for microbial and enzymatic conversions and for cell cultures despite the large number of medium components under simultaneous consideration (about 10 or more). In exploring a new variable space, process improvements of more than 100% were generally achieved. For initial reaction conditions previously 'optimised' via standard procedures it has been possible in most cases to achieve a further improvement of 20-40% of the target quantity. Although the genetic algorithm can be very efficient for exploration of large variable spaces, it is improbable that a 'global optimum' can be precisely identified because of the relatively small number of shaking flask experiments usually performed. As a consequence, a combination of highly directed random searches to explore the n-dimensional variable space with the genetic algorithm and subsequent application of classical statistical experimental design is recommended for media development.