ABSTRACT
Trefoil factor 3 (TFF3) is involved in cell adhesion, motility and apoptosis, regulates mucosal immunity and maintains the functional integrity of intestinal epithelia. The upregulation of TFF3 expression in the weaning rat intestine attracted our interest. The present study hypothesized that TFF3 may serve a role in preventing diarrhea in weaning piglets, which is an important consideration in the pig farming industry. Previous recombinant TFF3 protein expression yields obtained from Escherichia coli were too low and the bioactivity of the protein was poor. Hence, this expression system was unsuitable for industrial applications. The present study explored the production of recombinant sus scrofa TFF3 in a Brevibacillus choshinensis (B. choshinensis) expression system, aiming to enhance the expression level of bioactive protein. To achieve this, the sus scrofa TFF3-encoding gene fragment was fused into an E. coli-Brevibacillus shuttle vector pNCMO2. High levels of TFF3 (30 mg/l) were produced and secreted into the B. choshinensis culture medium in soluble form with a molecular mass of 13.6 kDa and high immunoreactivity in western blotting. Thus, Brevibacillus may be used to produce useful mucosal factors for biochemical analyses and mucosal protection, and in industrial applications to produce novel inhibitors of diarrhea.
ABSTRACT
Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.
Subject(s)
Bacteria/genetics , Genes, Bacterial/genetics , Machine Learning , Metagenomics/methods , Sequence Analysis, DNA/methods , Bacteria/classification , Databases, Genetic , Models, StatisticalABSTRACT
OBJECTIVE: To develop a new scattering spectra method for the determination of tetramethylpyrazine.