TY - JOUR
T1 - High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production
AU - Sampaio, Pedro N.
AU - Sales, Kevin C.
AU - Rosa, Filipa O.
AU - Lopes, Marta B.
AU - Calado, Cecília R. C.
N1 - Funding Information:
We thank Dr. Filomena Calixto for providing the S. cerevisiae BJ1991 strain transformed with CYPRO11 gene. We also thank Professor António Mendonça from the University of Beira Interior by availability of laboratory equipment and facilities related to spectra acquisition. We also thank the Professor Fernando Baltazar Duarte (Faculty of Engineering, ULHT) for the support in MATLAB. M.B Lopes acknowledges financial support from the Portuguese Foundation for Science and Technology (SFRH/BPD/73758/2010). This work was supported by CLARO research project funded by ADI agency (Portugal). The present work was partly conducted in the Health Engineering Laboratory resulting from the Protocol between Universidade Católica Portuguesa and the Instituto Politécnico de Lisboa.
Publisher Copyright:
© 2016, Society for Industrial Microbiology and Biotechnology.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
KW - Cultivation
KW - High-throughput analysis
KW - Mid-infrared spectroscopy
KW - Partial least square regression
KW - Principal components analysis
KW - Recombinant cyprosin
UR - http://www.scopus.com/inward/record.url?scp=84994411606&partnerID=8YFLogxK
U2 - 10.1007/s10295-016-1865-0
DO - 10.1007/s10295-016-1865-0
M3 - Article
C2 - 27830421
AN - SCOPUS:84994411606
SN - 1367-5435
VL - 44
SP - 49
EP - 61
JO - Journal of Industrial Microbiology and Biotechnology
JF - Journal of Industrial Microbiology and Biotechnology
IS - 1
ER -