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

Cardoon-based rennets for cheese production

The use of crude aqueous extracts of Cynara cardunculus flowers as coagulants in the production of high-quality sheep and goat cheeses-as are the cases of several Portuguese and Spanish cheese varieties with Protected Designation of Origin status-has been maintained since ancient times. The unique rheological attributes and sensory properties characteristic of these cheeses have always suggested that this plant coagulant (and, therefore, its isolated milk-clotting proteases) could be used as alternative rennet in the dairy industry, particularly suited for the production of sheep and goat cheeses. However, the lack of standardization of C. cardunculus crude flower extracts, whose quality and performance depends on numerous factors, has always hampered the application of this plant rennet in industrial production scales. To overcome these limitations, and to aim at developing more effective solutions with potential for scalability of production and commercial application, several strategies have been undertaken in more recent years to establish new cardoon-based rennets. This review provides an overview on these developments and on the currently available solutions, which range from producing standardized formulations of native cardoon enzymes, to the optimization of the heterologous production of cardosins and cyprosins to generate synthetic versions of these milk-clotting enzymes. Challenges and emerging opportunities are also discussed.

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 (R2 = 0.99, RMSEP 2.8%); cyprosin activity (R2 = 0.98, RMSEP 3.9%); glucose (R2 = 0.93, RMSECV 7.2%); galactose (R2 = 0.97, RMSEP 4.6%); ethanol (R2 = 0.97, RMSEP 5.3%); and acetate (R2 = 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 (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 (R(2)) 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.