Metabolic analysis of Schizochytrium sp. mutants with high EPA content achieved with ARTP mutagenesis screening.

Eicosapentaenoic acid (EPA) belonged to the ω-3 series of polyunsaturated fatty acids and had physiological functions lipid as regulating blood lipid and preventing cardiovascular diseases. Schizochytrium sp. was considered to be a potential industrial fermentation strain of EPA because of its fast growth, high oil content, and simple fatty acid composition. However, Schizochytrium sp. produced EPA with low production efficiency and a long synthesis path. This research aims to improve the yield of EPA in Schizochytrium sp. by ARTP mutagenesis and to reveal the mechanism of high-yield EPA through transcriptome analysis. ARTP mutagenesis screening yielded the mutant M12 that whereas the productivity of EPA increased 108% reaching 0.48 g/L, the total fatty acid concentration was 13.82 g/L with an increase of 13.7%. The transcriptomics revealed 2995 differentially expressed genes were identified between M12 and the wild-type strain and transcripts involved in carbohydrate, amino acid, energy, and lipid metabolism were up-regulated. Among them, the hexokinase (HK) and the phosphofructokinase genes (PFK), which can catalyze pyruvate to acetyl-CoA, were increased 2.23-fold and 1.78-fold. Glucose-6-phosphate dehydrogenase (G6PD) and glutamate dehydrogenase (GLDH), which can both generate NADPH, were increased by 1.67-fold and 3.11-fold. Furthermore, in the EPA synthesis module, the expression of 3-oxoacyl-[acyl-carrier protein] reductase(fabG) and carbonyl reductase 4 / 3-oxoacyl-[acyl-carrier protein] reductase beta subunit(CBR4), also up-regulated 1.11-fold and 2.67-fold. These may lead to increases in cell growth. The results provide an important reference for further research on promoting fatty acid and EPA accumulation in Schizochytrium sp.

Improving acetoin production through construction of a genome-scale metabolic model.

Acetoin was widely used in food, medicine, and other industries, because of its unique fragrance. Bacillus amyloliquefaciens was recognized as a safe strain and a promising acetoin producer in fermentation. However, due to the complexity of its metabolic network, it had not been fully utilized. Therefore, a genome-scale metabolic network model (iJYQ746) of B. amyloliquefaciens was constructed in this study, containing 746 genes, 1736 reactions, and 1611 metabolites. The results showed that Mg2+, Mn2+, and Fe2+ have inhibitory effects on acetoin. When the stirring speed was 400 rpm, the maximum titer was 49.8 g L-1. Minimization of metabolic adjustments (MOMA) was used to identify potential metabolic modification targets 2-oxoglutarate aminotransferase (serC, EC 2.6.1.52) and glucose-6-phosphate isomerase (pgi, EC 5.3.1.9). These targets could effectively accumulate acetoin by increasing pyruvate content, and the acetoin synthesis rate was increased by 610% and 10%, respectively. This provides a theoretical basis for metabolic engineering to reasonably transform B. amyloliquefaciens and produce acetoin.

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks.

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.