Use of chemometrics in the selection of a Saccharomyces cerevisiae expression system for recombinant cyprosin B production.
ABSTRACT 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.
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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 · 2.44 Impact Factor
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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.Bioprocess and Biosystems Engineering 06/2014; 37(12). DOI:10.1007/s00449-014-1229-y · 1.82 Impact Factor
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ABSTRACT: Near Infrared (NIR) spectroscopy was used to in situ monitoring the cultivation of two recombinant Saccharomyces cerevisiae strains producing heterologous cyprosin B. NIR spectroscopy is a fast and non-destructive technique, that by being based on overtones and combinations of molecular vibrations requires chemometrics tools, such as partial least squares (PLS) regression models, to extract quantitative information concerning the variables of interest from the spectral data. In the present work, good PLS calibration models based on specific regions of the NIR spectral data were built for estimating the critical variables of the cyprosin production process: biomass concentration, cyprosin activity, cyprosin specific activity, the carbon sources glucose and galactose concentration and the by-products acetic acid and ethanol concentration. The PLS models developed are valid for both recombinant S. cerevisiae strains, presenting distinct cyprosin production capacities, and therefore can be used, not only for the real-time control of both processes, but also in optimization protocols. The PLS model for biomass yielded a R(2) =0.98 and a RMSEP=0.46g dcw.l(-1), representing an error of 4% for a calibration range between 0.44 and13.75g dcw.l(-1). A R(2)=0.94 and a RMSEP=167 U.ml(-1) were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0-2509 U.ml(-1)), whereas a R(2)=0.93 and RMSEP=672 U.mg(-1) were obtained for the cyprosin specific activity, corresponding to an error of 7% of the experimental data range (0-11,690 U.mg(-1)). For the carbon sources glucose and galactose, a R(2)=0.96 and a RMSECV of 1.26 and 0.55g.l(-1), respectively, were obtained, showing high predictive capabilities within the range of 0-20g.l(-1). For the metabolites resulting from the cell growth, the PLS model for acetate was characterized by a R(2)=0.92 and a RMSEP=0.06g.l(-1), which corresponds to a 6.1% error within the range of 0.41-1.23g.l(-1); for the ethanol, a high accuracy PLS model with a R(2)=0.97 and a RMSEP=1.08g.l(-1) was obtained, representing an error of 9% within the range of 0.18-21.76g.l(-1). The present study shows that it is possible the in situ monitoring and prediction of the critical variables of the recombinant cyprosin B production process by NIR spectroscopy, which can be applied in process control in real-time and in optimization protocols. From the above, NIR spectroscopy appears as a valuable analytical tool for online monitoring of cultivation processes, in a fast, accurate and reproducible operation mode.Journal of Biotechnology 08/2014; 188. DOI:10.1016/j.jbiotec.2014.07.454 · 2.88 Impact Factor