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.
Faculty of Engineering, Lusophone University of Humanities and Technology, Campo Grande, 376, 1749-019, Lisbon, Portugal. pnsampaio@gmail.com.
Pedro N
PN
Sampaio
Faculty of Engineering, Catholic University of Portugal, Avenida Otávio Pato, 2635-631, Rio de Mouro, Portugal.
Kevin C
KC
Sales
Faculty of Engineering, Catholic University of Portugal, Avenida Otávio Pato, 2635-631, Rio de Mouro, Portugal.
Filipa O
FO
Rosa
Faculty of Engineering, Catholic University of Portugal, Avenida Otávio Pato, 2635-631, Rio de Mouro, Portugal.|Institute of Telecommunications, Technical Higher Institute University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon, Portugal.|ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Conselheiro Emídio Navarro Street, 1959-007, Lisbon, Portugal.
Marta B
MB
Lopes
ISEL-Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Conselheiro Emídio Navarro Street, 1959-007, Lisbon, Portugal.
Cecília R C
CR
Calado
D016282:Aspartic Acid Endopeptidases / Q000096:biosynthesis*; D018533:Biomass; D001709:Biotechnology / Q000379:methods; D000431:Ethanol / Q000378:metabolism; D005690:Galactose; D005947:Glucose / Q000032:analysis; D016018:Least-Squares Analysis; D025341:Principal Component Analysis; D011994:Recombinant Proteins / Q000096:biosynthesis*; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae; D013055:Spectrophotometry, Infrared; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*; D013696:Temperature
D011994:Recombinant Proteins; D000431:Ethanol; D016282:Aspartic Acid Endopeptidases; C087364:cyprosin; D005947:Glucose; D005690:Galactose
Cultivation; High-throughput analysis; Mid-infrared spectroscopy; Partial least square regression; Principal components analysis; Recombinant cyprosin