High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production.

To increase the knowledge of the recombinant cyprosin production process in Saccharomyces cerevisiae cultures, it is relevant to implement efficient bioprocess monitoring techniques. The present work focuses on the implementation of a mid-infrared (MIR) spectroscopy-based tool for monitoring the recombinant culture in a rapid, economic, and high-throughput (using a microplate system) mode. Multivariate data analysis on the MIR spectra of culture samples was conducted. Principal component analysis (PCA) enabled capturing the general metabolic status of the yeast cells, as replicated samples appear grouped together in the score plot and groups of culture samples according to the main growth phase can be clearly distinguished. The PCA-loading vectors also revealed spectral regions, and the corresponding chemical functional groups and biomolecules that mostly contributed for the cell biomolecular fingerprint associated with the culture growth phase. These data were corroborated by the analysis of the samples' second derivative spectra. Partial least square (PLS) regression models built based on the MIR spectra showed high predictive ability for estimating the bioprocess critical variables: biomass (R 2 = 0.99, RMSEP 2.8%); cyprosin activity (R 2 = 0.98, RMSEP 3.9%); glucose (R 2 = 0.93, RMSECV 7.2%); galactose (R 2 = 0.97, RMSEP 4.6%); ethanol (R 2 = 0.97, RMSEP 5.3%); and acetate (R 2 = 0.95, RMSEP 7.0%). In conclusion, high-throughput MIR spectroscopy and multivariate data analysis were effective in identifying the main growth phases and specific cyprosin production phases along the yeast culture as well as in quantifying the critical variables of the process. This knowledge will promote future process optimization and control the recombinant cyprosin bioprocess according to Quality by Design framework.

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.