Pedro N. Sampaio

Production and characterization of recombinant cyprosin B in Saccharomyces cerevisiae (W303-1A) strain

The Saccharomyces cerevisiae W303-1A strain transformed with a centromeric plasmid containing CYPRO11, which codifies the aspartic protease cyprosin B, was grown in a 3 l bioreactor under aerobic conditions. Expression of cyprosin B is directly dependent on the concentration of galactose used as the inducer and carbon source in 1% yeast extract, 2% bactopeptone, and 4% galactose in culture medium. For 4% of galactose, 209 mg.l(-1) total protein, and 1036 U.ml(-1) recombinant cyprosin B activity were obtained from 6.1 g dcw.l(-1) biomass. The recombinant cyprosin B, purified by two consecutive anion-exchange chromatographies (diethyl amino-ethyl [DEAE]-Sepharose and Q-Sepharose XK-16 columns), shows a specific activity of 62 x 10(3) U.mg(-1), corresponding to a purification degree of 12.5-fold and a recovery yield of 25.6% relative to that in fermentation broth. The proteolytic activity of recombinant cyprosin B is optimal at 42 degrees C and pH 4.5. The recombinant cyprosin B activity is 95% inhibited by pepstatin A, which confirms its aspartic protease nature. The pure recombinant cyprosin B is composed of two subunits, one with 14 and the other with 32 kDa. It exhibits clotting activity, similar to that of the natural enzyme from Cynara cardunculus flowers. The results reported here show that recombinant cyprosin B, the first clotting protease of plant origin produced in a bioreactor, can now be produced in large scale and may constitute a new and efficient alternative to enzymes of animal or fungal origin that are widely used in cheese making.

In situ near infrared spectroscopy monitoring of cyprosin production by recombinant Saccharomyces cerevisiae strains

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=0.46 g dcw l(-1), representing an error of 4% for a calibration range between 0.44 and 13.75 g dcw l(-1). A R(2)=0.94 and a RMSEP=167 Um l(-1) were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0-2509 Um l(-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 Um g(-1)). For the carbon sources glucose and galactose, a R(2)=0.96 and a RMSECV of 1.26 and 0.55 g l(-1), respectively, were obtained, showing high predictive capabilities within the range of 0-20 g 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=0.06 g l (-1), which corresponds to a 6.1% error within the range of 0.41-1.23 g l(-1); for the ethanol, a high accuracy PLS model with a R(2)=0.97 and a RMSEP=1.08 g l(-1) was obtained, representing an error of 9% within the range of 0.18-21.76 g 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.

In Situ Near-Infrared (NIR) versus High-Throughput Mid-Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical Production

The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coli cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

Use of chemometrics in the selection of a Saccharomyces cerevisiae expression system for recombinant cyprosin B production

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.

Modelling, monitoring and control of plasmid bioproduction in Escherichia coli cultures

An integrated approach for modelling, monitoring and control the plasmid bioproduction in Escherichia coli cultures is presented. In a first stage, by the implementation of a kinetic model for E. coli cultures, a better bioprocess understanding was reached, concerning the availability of nutrients and products along the bioprocess, and their effects on the plasmid production. Results presented may provide significant help for future modelling and monitoring implementation. In a second stage, FTIR spectroscopy coupled with chemometrics, namely PLS regression, shows its potential as a high-throughput technique for simultaneously estimating the key variables involved in the plasmid production process by E. coli cultures run under distinct conditions. Finally, owing to online monitoring and process control, an NIR fibre optic probe and chemometrics provided promising results concerning the control of biomass and carbon sources in E. coli cultures.

Comparative analysis of different transformed Saccharomyces cerevisiae strains based on high-throughput Fourier transform infrared spectroscopy

This study shows the application of the Fourier transform mid-infrared spectroscopy (FT-MIR) associated with high-throughput technology to study the biochemical fingerprints of different Saccharomyces cerevisiae strains transformed with the same expression system along the similar cultivation in bioreactor. The phenotype, as well as the cellular metabolism and recombinant cyprosin biosynthesis, were determined. The differences observed were confirmed by conventional cyprosin activity protocol, and the metabolic evolution was analyzed using high-performance liquid chromatography technique. The spectral analysis based on chemometrics tools, such as the principal component analysis, is a useful methodology for the phenotypes characterization as well as the specific metabolic states along the cultivations according to the clusters created. The ratio bands of spectra also represented a useful tool to evaluate the metabolic and biochemical differences between both expression systems, allowing to have an additional parameter to the biomolecular comparison. Therefore, high-throughput FT-MIR spectroscopy associated with multivariate data analysis represent a valuable strategy for extracting significant specific biomolecular information along the cultivation, providing a complete bioprocess analysis, once it detects slight molecular changes which it will be useful for screening and optimization process in the biotechnological or pharmaceutical industry.

Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis

Escherichia coli is one of the most used host microorganism for the production of recombinant products, such as heterologous proteins and plasmids. However, genetic, physiological and environmental factors influence the plasmid replication and cloned gene expression in a highly complex way. To control and optimize the recombinant expression system performance, it is very important to understand this complexity. Therefore, the development of rapid, highly sensitive and economic analytical methodologies, which enable the simultaneous characterization of the heterologous product synthesis and physiologic cell behavior under a variety of culture conditions, is highly desirable. For that, the metabolic profile of recombinant E. coli cultures producing the pVAX‐lacZ plasmid model was analyzed by rapid, economic and high‐throughput Fourier Transform Mid‐Infrared (FT‐MIR) spectroscopy. The main goal of the present work is to show as the simultaneous multivariate data analysis by principal component analysis (PCA) and direct spectral analysis could represent a very interesting tool to monitor E. coli culture processes and acquire relevant information according to current quality regulatory guidelines. While PCA allowed capturing the energetic metabolic state of the cell, e.g. by identifying different C‐sources consumption phases, direct FT‐MIR spectral analysis allowed obtaining valuable biochemical and metabolic information along the cell culture, e.g. lipids, RNA, protein synthesis and turnover metabolism. The information achieved by spectral multivariate data and direct spectral analyses complement each other and may contribute to understand the complex interrelationships between the recombinant cell metabolism and the bioprocess environment towards more economic and robust processes design according to Quality by Design framework.