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- In situ near infrared spectroscopy monitoring of cyprosin production by recombinant Saccharomyces cerevisiae strainsPublication . Sampaio, Pedro; Sales, Kevin C.; Rosa, Filipa O.; Lopes, Marta B.; Calado, CecíliaNear 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 R2 = 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 R2 = 0.94 and a RMSEP = 167 U ml−1 were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0–2509 U ml−1), whereas a R2 = 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 U mg−1). For the carbon sources glucose and galactose, a R2 = 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 R2 = 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 R2 = 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.
- Kinetic modeling of plasmid bioproduction in Escherichia coli DH5α cultures over different carbon-source compositionsPublication . Lopes, Marta B.; Martins, Gabriel; Calado, CecíliaThe need for the development of economic high plasmid production in Escherichia coli cultures is emerging, as a result of the latest advances in DNA vaccination and gene therapy. In order to contribute to achieve that, a model describing the kinetics involved in the bioproduction of plasmid by recombinant E. coli DH5α is presented, as an attempt to understand the complex and non-linear metabolic relationships and the plasmid production occurring in dynamic batch culture environments, run under different media compositions of glucose and glycerol, that result in distinct maximum biomass growths (between 8.2 and 12.8 g DCW/L) and specific plasmid productions (between 1.1 and 7.4 mg/g DCW). The model based on mass balance equations for biomass, glucose, glycerol, acetate and plasmid accurately described different culture behaviors, using either glucose or glycerol as carbon source, or mixtures of both. From the 17 parameters obtained after model simplification, the following 10 parameters were found to be independent of the carbon source composition: the substrate affinity constants, the inhibitory constants of biomass growth on glycerol by glucose, of biomass growth on acetate by glycerol and the global biomass growth by acetate, and the yields of biomass on acetate, acetate on glucose and glycerol, and plasmid on glucose. The parameters that depend on the culture composition, and that might explain the differences found between cultures, were: maximum specific growth rates on glucose, glycerol and acetate; biomass yield on glucose and glycerol; and plasmid yield on glycerol and acetate. Moreover, a crucial role of acetate in the plasmid production was revealed by the model, with most of plasmid production being associated to the acetate consumption. The model provides meaningful insight on the E. coli dynamic cell behavior concerning the plasmid bioproduction, which might lead to important guidelines for culture optimization and process scale-up and control.
- High-throughput FTIR-based bioprocess analysis of recombinant cyprosin productionPublication . Sampaio, Pedro; Sales, Kevin C.; Rosa, Filipa O.; B. Lopes, Marta; Calado, CecíliaTo 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.
- Does nonlinear modeling play a role in plasmid bioprocess monitoring using fourier transform infrared spectra?Publication . B. Lopes, Marta; Calado, Cecília; Figueiredo, Mario; Bioucas-Dias, JoseThe monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.
- Real-time plasmid monitoring of batch and fed-batch Escherichia coli cultures by NIR spectroscopyPublication . Lopes, Marta B.; Sales, Kevin C.; Lopes, Vítor V.; Calado, CecíliaThe development of in-situ monitoring techniques enabling the real-time acquisition of information concerning the key variables over different Escherichia coli cultivation conditions and strategies is a crucial step towards the optimization of a plasmid bioproduction process. This work shows the use of a Near-InfraRed (NIR) fiber optic probe immersed in the culture broth for the real-time acquisition of NIR spectra along different E. coli cultures conducted with mixtures of the carbon sources glucose and glycerol, and performed in both batch and fed-batch modes. Accurate partial least squares models based on the acquired spectral data over such different cultivation conditions were built, yielding a R 2 = 0.97 for biomass and plasmid productions and a RMSEP of 0.34 and 7.52, respectively; a R 2 of 0.93 and a RMSEP of 0.46 and 0.33 was obtained for glucose and glycerol, respectively; the acetate model produced a R 2 of 0.96 and a RMSEP of 0.32.
- Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysisPublication . Sales, Kevin C.; Rosa, Filipa; Ribeiro Da Cunha, Bernardo; Sampaio, Pedro; B. Lopes, Marta; Calado, CecíliaEscherichia 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 rap-id, 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.
- Determination of cell abundances and paralytic shellfish toxins in cultures of the dinoflagellate gymnodinium catenatum by fourier transform near infrared spectroscopyPublication . B. Lopes, Marta; Amorim, Ana; Calado, Cecília; Reis Costa, PedroHarmful algal blooms are responsible worldwide for the contamination of fishery resources, with potential impacts on seafood safety and public health. Most coastal countries rely on an intense monitoring program for the surveillance of toxic algae occurrence and shellfish contamination. The present study investigates the use of near infrared (NIR) spectroscopy for the rapid in situ determination of cell concentrations of toxic algae in seawater. The paralytic shellfish poisoning (PSP) toxin-producing dinoflagellate Gymnodinium catenatum was selected for this study. The spectral modeling by partial least squares (PLS) regression based on the recorded NIR spectra enabled the building of highly accurate (R2 = 0.92) models for cell abundance. The models also provided a good correlation between toxins measured by the conventional methods (high-performance liquid chromatography with fluorescence detection (HPLC-FLD)) and the levels predicted by the PLS/NIR models. This study represents the first necessary step in investigating the potential of application of NIR spectroscopy for algae bloom detection and alerting.
- In Situ Near-Infrared (NIR) Versus High-Throughput Mid- Infrared (MIR) Spectroscopy to Monitor Biopharmaceutical ProductionPublication . Sales, Kevin C.; Rosa, Filipa; Sampaio, Pedro N.; Fonseca, LuÍs P.; B. Lopes, Marta; Calado, CecíliaThe 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 coil 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.
- Assessing plasmid bioprocess reproducibility and C‐source uptake stage through multivariate analysis of offline and online dataPublication . B. Lopes, Marta; Calado, CecíliaBACKGROUND: A considerable effort has been put forth by the biopharmaceutical industry to guarantee high-quality products and patient safety. Ensuring the reproducibility of microbial cultures is essential to achieve such high standards. Reproducibility is usually assessed by offline, costly and time-consuming analyses of the final product, disregarding the culture behavior along time, which affects the final product. Online data provided in real time by the bioreactor unit control (e.g., temperature, pH, dissolved oxygen concentration), unlike offline data acquired along the culture, implies no extra cost or contamination risks, thus holding the promise of playing an important role in assessing culture reproducibility. RESULTS: Eleven batch recombinant Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ were analyzed by Multivariate Data Analysis (MVDA) of online and offline data. Similarities between the cultures were inspected by Principal Component Analysis (PCA) and the significance of the differences determined by Multivariate Analysis of Variance (MANOVA) based on offline data. Linear Discriminant Analysis (LDA) based on the online bioreactor data was also successful in predicting the cell growth stage at each time point regarding the C-source under consumption. CONCLUSION: Multivariate data analysis based on offline and online data has proved to be a valuable tool in assessing the cultures’ reproducibility and the metabolic stage regarding the C-source consumption phase at a given time. These results may certainly impact the biopharmaceutical industry, by drawing attention to the usefulness of combining MVDA and bioprocess data, particularly online data, never used for these purposes.