Two multivariate statistical methods, factor analysis (FA) and hierarchical cluster analysis (HCA), were applied to experimental data set to evaluate their usefulness in selecting the adequate expression system and optimal growth parameters for recombinant cyprosin B production. Using FA, the large data set was reduced to two factors representing 73.4% of variability. Factor 1, with 53.5% of variability, corresponds to recombinant cyprosin B expression and efficient secretion, while factor 2, accounting for 19.9% of variability, represents cell growth and physiological characteristics. FA and HCA allowed the establishment of correlations among different variables and the clusters obtained providing clear identification of the experimental parameters related to cyprosin B production, which results on more accurate scientific output and time saving when selection of an adequate expression system is concerned.
[Show abstract][Hide abstract] ABSTRACT: The generation of novel yeast cell factories for production of high-value industrial biotechnological products relies on three metabolic engineering principles: design, construction, and analysis. In the last two decades, strong efforts have been put on developing faster and more efficient strategies and/or technologies for each one of these principles. For design and construction, three major strategies are described in this review: (1) rational metabolic engineering; (2) inverse metabolic engineering; and (3) evolutionary strategies. Independent of the selected strategy, the process of designing yeast strains involves five decision points: (1) choice of product, (2) choice of chassis, (3) identification of target genes, (4) regulating the expression level of target genes, and (5) network balancing of the target genes. At the construction level, several molecular biology tools have been developed through the concept of synthetic biology and applied for the generation of novel, engineered yeast strains. For comprehensive and quantitative analysis of constructed strains, systems biology tools are commonly used and using a multi-omics approach. Key information about the biological system can be revealed, for example, identification of genetic regulatory mechanisms and competitive pathways, thereby assisting the in silico design of metabolic engineering strategies for improving strain performance. Examples on how systems and synthetic biology brought yeast metabolic engineering closer to industrial biotechnology are described in this review, and these examples should demonstrate the potential of a systems-level approach for fast and efficient generation of yeast cell factories.
FEMS Yeast Research 12/2011; 12(2):228-48. DOI:10.1111/j.1567-1364.2011.00779.x · 2.82 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Metabolic engineering, defined as the practice of manipulating cells’ genetic and regulatory processes for improving cellular performance, has been recently integrating systems and synthetic biology with other technologies, e.g. molecular biology, physiology, biochemistry, analysis science, bioinformatics and biochemical engineering. The aim is to surmount cells’ performance-related limitations, e.g. metabolic limitation of cellular processes, network rigidity and global regulation, in a comprehensive, rational and high-throughput manner. Using this multi-level/integrated approach, the (re)design and (re)construction of microbial systems for the development of novel products with significant impact on current global problems, e.g. depletion of energy resources and global warming, is becoming a reality.
In the last two decades, technological platforms for systems biology, e.g. genomics, transcriptomics, proteomics, metabolomics and fluxomics, and synthetic biology approaches, e.g. synthetic biological parts, devices and systems, have been implemented and efficiently used as tools for metabolic engineering of high-value bioproducts. One successful story on how metabolic engineering has been used to enhance the synthesis of microbial-based molecules is the production of biofuels. In this review, synthetic and systems biology technologies for yeast metabolic engineering will be described in detail. In addition, two case studies related with biofuel production (ethanol and 1-butanol) in yeast S. cerevisiae will be presented.
Systems Metabolic Engineering, the First Edition 01/2012: chapter 9: pages 271-327; Springer., ISBN: 978-94-007-4533-9 (Print) 978-94-007-4534-6 (Online)
[Show abstract][Hide abstract] ABSTRACT: Nowadays, the dairy industry is continuously looking for new and more efficient clotting enzymes to create innovative products. Cyprosin B is a plant aspartic protease characterized by clotting activity that was previously cloned in Saccharomyces cerevisiae BJ1991 strain. The production of recombinant cyprosin B by a batch and fed-batch culture was compared using glucose and galactose as carbon sources. The strategy for fed-batch cultivation involved two steps: in the first batch phase, the culture medium presented glucose 1 % (w/v) and galactose 0.5 % (w/v), while in the feed step the culture medium was constituted by 5 % (w/v) galactose with the aim to minimize the GAL7 promoter repression. Based on fed-batch, in comparison to batch growth, an increase in biomass (6.6-fold), protein concentration (59 %) and cyprosin B activity (91 %) was achieved. The recombinant cyprosin B was purified by a single hydrophobic chromatography, presenting a specific activity of 6 × 10(4) U·mg(-1), corresponding to a purification degree of 12.5-fold and a recovery yield of 16.4 %. The SDS-PAGE analysis showed that recovery procedure is suitable for achieving the purified recombinant cyprosin B. The results show that the recombinant cyprosin B production can be improved based on two distinct steps during the fed-batch, presenting that this strategy, associated with a simplified purification procedure, could be applied to large-scale production, constituting a new and efficient alternative for animal and fungal enzymes widely used in cheese making.
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