Effect of Pichia pastoris host strain on the properties of recombinant Aspergillus niger endoglucanase, EglB

Aims@#The methylotrophic yeast Pichia pastoris is widely used to express foreign proteins fused to secretion signals. As the effect of the expression host on the final protein product is unclear, we compared the properties of an endoglucanase (eglB of Aspergillus niger) expressed in two different P. pastoris strains. @*Methodology and results@#Full-length cDNA encoding endoglucanase of A. niger strain ATCC10574 was isolated and expressed in P. pastoris X33 (the methanol utilisation plus phenotype, Mut+) and P. pastoris GS115 (slow methanol utilisation, MutS). EglB-GS115 showed the highest activity and stability at 60 °C while EglB-X33 was most active at 50 °C. EglB-X33 was active towards other substrates such as arabinogalactan, guar gum and locust bean gum besides its specific substrate, carboxymethyl cellulose (CMC). However, EglB-GS115 was only active on CMC. The affinity of EglB-X33 towards CMC (Km = 7.5 mg/mL and specific activity 658 U/mg) was higher than that of EglB-GS115 (Km = 11.57 mg/mL, specific activity 144 U/mg). @*Conclusion, significance and impact of study@#Although eglB was cloned in the same expression vector (pPICZαC), two different characteristics of enzymes were recovered from the supernatant of the different hosts. Thus, expression of recombinant enzyme in different P. pastoris strains greatly affects the physical structure and biochemical properties of the enzyme.

Short-chain fructo-oligosaccharides produced by enzymatic hydrolysis enhance the growth of probiotics isolated from cultured milk drinks

Aims@#Short-chain fructo-oligosaccharides (scFOSs) are good prebiotics that enhance the growth of probiotic bacteria. The aim of this study is to determine the effect of scFOSs produced by levan hydrolysis using recombinant endo-levanase from B. lehensis G1 on the growth of probiotics isolated from commercially cultured milk drinks. @*Methodology and results@#Two probiotic bacteria, Lactobacillus casei and L. rhamnosus, were isolated from commercially cultured milk drinks. ScFOSs were produced by levan hydrolysis using recombinant endo-levanase from B. lehensis G1. The scFOS and levan (control) were added independently to the growth medium, and the growth rates of the probiotic bacteria were determined. Results showed that the growth rate of L. casei decreased in the presence of levan compared with the control medium but increased by approximately 20% when supplemented with scFOS produced by Levblg1-N28S. Similarly, the growth rate of L. rhamnosus increased by approximately 20% when supplemented with scFOS produced by Levblg1 and Levblg1-N28S. @*Conclusion, significance and impact of study@#The scFOSs produced by the enzymatic hydrolysis of levan using a recombinant endo-levanase from B. lehensis G1 have significant potential as prebiotics because they were able to promote the growth of the probiotic bacteria.

Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.