SYSTEMS BIOLOGY AND METABOLIC ENGINEERING OF ARTHROSPIRA CELL FACTORIES

AbstractArthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented

Metabolic Regulation of Sugar Assimilation for Lipid Production in Aspergillus oryzae BCC7051 through Comparative Transcriptome Perspective

Microbial lipid production with cost effectiveness is a prerequisite for the oleochemical sector. In this work, genome-wide transcriptional responses on the utilization of xylose and glucose in oleaginous Aspergillus oryzae were studied with relation to growth and lipid phenotypic traits. Comparative analysis of the active growth (t1) and lipid-accumulating (t2) stages showed that the C5 cultures efficiently consumed carbon sources for biomass and lipid production comparable to the C6 cultures. By pairwise comparison, 599 and 917 differentially expressed genes (DEGs) were identified in the t1 and t2 groups, respectively, in which the consensus DEGs were categorized into polysaccharide-degrading enzymes, membrane transports, and cellular processes. A discrimination in transcriptional responses of DEGs set was also found in various metabolic genes, mostly in carbohydrate, amino acid, lipid, cofactors, and vitamin metabolisms. Although central carbohydrate metabolism was shared among the C5 and C6 cultures, the metabolic functions in acetyl-CoA and NADPH generation, and biosynthesis of terpenoid backbone, fatty acid, sterol, and amino acids were allocated for leveraging biomass and lipid production through at least transcriptional control. This study revealed robust metabolic networks in the oleaginicity of A. oryzae governing glucose/xylose flux toward lipid biosynthesis that provides meaningful hints for further process developments of microbial lipid production using cellulosic sugar feedstocks

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs

Genome Characterization of Oleaginous Aspergillus oryzae BCC7051: A Potential Fungal-Based Platform for Lipid Production

The selected robust fungus, Aspergillus oryzae strain BCC7051 is of interest for biotechnological production of lipid-derived products due to its capability to accumulate high amount of intracellular lipids using various sugars and agro-industrial substrates. Here, we report the genome sequence of the oleaginous A. oryzae BCC7051. The obtained reads were de novo assembled into 25 scaffolds spanning of 38,550,958\ua0bps with predicted 11,456 protein-coding genes. By synteny mapping, a large rearrangement was found in two scaffolds of A. oryzae BCC7051 as compared to the reference RIB40 strain. The genetic relationship between BCC7051 and other strains of A. oryzae in terms of aflatoxin production was investigated, indicating that the A. oryzae BCC7051 was categorized into group 2 nonaflatoxin-producing strain. Moreover, a comparative analysis of the structural genes focusing on the involvement in lipid metabolism among oleaginous yeast and fungi revealed the presence of multiple isoforms of metabolic enzymes responsible for fatty acid synthesis in BCC7051. The alternative routes of acetyl-CoA generation as oleaginous features and malate/citrate/pyruvate shuttle were also identified in this A. oryzae strain. The genome sequence generated in this work is a dedicated resource for expanding genome-wide study of microbial lipids at systems level, and developing the fungal-based platform for production of diversified lipids with commercial relevance