Browsing by Author "Sampaio, Pedro"
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- Antimicrobial evaluation of the Cynara cardunculus extract in Helicobacter pylori cells using mid-infrared spectroscopy and chemometric methodsPublication . Sampaio, Pedro; Calado, CecíliaThe treatment effectiveness of gastric diseases caused by the bacteria Helicobacter pylori is failing due to high resistance to some antibiotics. Consequently, it is urgent to develop an accurate methodology to screen new antimicrobial agents. Methods and results: A preliminary assay, using both therapeutic-based antibiotics (clarithromycin and metronidazole), was conducted to optimize experimental conditions in terms of the sensibility of the Fourier-transform mid-infrared (MIR-FTIR) spectroscopy associated with chemometric methods. Principal component analysis was applied to understand how the Cynara extract concentration acts differentially against H. pylori bacteria. The partial least squares model, characterized by R2 = 0.98, and root mean square error cross-validation, 0.011, was developed for the spectral regions (3600-2500 cm-1 and 2000-698 cm-1 ). Conclusions: MIR-FTIR spectroscopy associated with chemometric methods can be considered a suitable approach to discover and analyse the promissory antimicrobial agents based on the biomolecular changes observed according to the Cynara extract. Significance and impact of the study: MIR-FTIR spectroscopy and chemometric methods allowed to register the biomolecular changes due to the potential antimicrobial drugs at reduced concentrations comparatively to the conventional assay based on an agar-dilution method, being considered a useful approach to develop a platform to discover new bioactive molecules, allowing to reduce time and costs related to the exploratory step.
- Characterization of gastric cells infection by diverse Helicobacter pylori strains through Fourier-transform infrared spectroscopyPublication . Marques, Vanda; Ribeiro Da Cunha, Bernardo; Couto, Andreia; Sampaio, Pedro; Fonseca, Luís P. P.; Aleixo, Sandra; Calado, CecíliaThe infection of Helicobacter pylori, covering 50% of the world-population, leads to diverse gastric diseases as ulcers and cancer along the life-time of the human host. To promote the discovery of biomarkers of bacterial infection, in the present work, Fourier-transform infrared spectra were acquired from adenocarcinoma gastric cells, incubated with H. pylori strains presenting different genotypes concerning the virulent factors cytotoxin associated gene A and vacuolating cytotoxin A. Defined absorbance ratios were evaluated by diverse methods of statistical inference, according to the fulfillment of the tests assumptions. It was possible to define from the gastric cells, diverse absorbance ratios enabling to discriminate: i) The infection; ii) the bacteria genotype; and iii) the gastric disease of the patients from which the bacteria were isolated. These biomarkers could fasten the knowledge of the complex infection process while promoting a platform for a new diagnostic method, rapid but also specific and sensitive towards the diagnosis of both infection and bacterial virulence.
- Classification of recombinant Saccharomyces cerevisiae cells using PLS-DA modelling based on MIR spectroscopyPublication . Sampaio, Pedro; Calado, CecíliaTowards the optimization and control of bioprocesses, as the culture step of recombinant microorganisms, it is crucial to develop high-throughput monitoring techniques enabling the acquisition of a large data sets of information, as the ones based on mid infrared (MIR) spectroscopy. To maximize the knowledge associated to this this new large-scale data, it is also relevant to apply machine learning techniques, as Partial Least-Squares Discriminant Analysis (PLS-DA). In the present work, Principal Component Analysis followed by PLS-DA were applied to discriminate different growth phases of recombinant Saccharomyces cerevisiae along the production of an heterologous protein, conducted in bioreactor and monitored by high-throughput MIR spectroscopy. It was possible to derive PLS-DA models enabling to discriminate, from the MIR spectra, the yeast cells according to its metabolic status associated to the culture growth phase with an accuracy, sensitivity, and specificity between 83% and 100%. The optimised PLS-DA presented very low calibration errors, of 97% and 100% based on a cross-validation and an independent data-set, respectively. In conclusion, it was possible to build a PLS-DA model discriminating the cells metabolic status that will promote the knowledge of the bioprocess and future better control and optimization procedures.
- Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin productionPublication . Sampaio, Pedro; Calado, CecíliaInfrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.
- Comparison of partial least squares-discriminant analysis and soft independent modeling of class analogy methods for classification of Saccharomyces cerevisiae cells based on mid-infrared spectroscopyPublication . Sampaio, Pedro; Calado, CecíliaSaccharomyces cerevisiae is a widely studied and highly utilized eukaryotic organism, ideally suited to high throughput metabolic analysis, being a powerful model for understanding basic cell biology. This study compares the models developed by two supervised methods, such as the partial least squares-discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA), using mid-infrared (MIR) spectra registered during the growth of S. cerevisiae in bioreactor. The spectra were analyzed using the principal component analysis (PCA), with resolution in five different classes, which were well defined in terms of their biochemical parameters. The SIMCA model showed a significant fitting, 99%, validation, 98%, and prediction parameters, 97%, comparatively with PLS-DA model. Regarding accuracy, sensitivity, and specificity parameters, a value between 83% and 100% was achieved for both methods, but the SIMCA method showed significant specificity and sensitivity values, 98%-100%, representing a suitable classification tool of yeast cells. According to these results, the MIR spectra associated with chemometric tools can be considered a valued strategy for a classification and detailed analysis for an accurate control, allowing to predict the evolution of the corrected process in advance, avoiding losses of time and costs associated with new fermentations, identifying a significant number of samples in any biotechnological process.
- Enhancing bioactive compound classification through the synergy of fourier-transform infrared spectroscopy and advanced machine learning methodsPublication . Sampaio, Pedro; Calado, CecíliaBacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds’ impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.
- 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.
- 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.
- 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.
- Modelling, monitoring and control of plasmid bioproduction in Escherichia coli culturesPublication . Lopes, Marta B.; Scholtz, Teresa; Silva, Daniel; Santos, Inês; Silva, Tito; Sampaio, Pedro; Couto, Andreia; Lopes, Vitor V.; Calado, CecíliaAn integrated approach for modelling, monitoring and control the plasmid bioproduction in Escherichia coli cultures is presented. In a first stage, by the implementation of a kinetic model for E. coli cultures, a better bioprocess understanding was reached, concerning the availability of nutrients and products along the bioprocess, and their effects on the plasmid production. Results presented may provide significant help for future modelling and monitoring implementation. In a second stage, FTIR spectroscopy coupled with chemometrics, namely PLS regression, shows its potential as a high-throughput technique for simultaneously estimating the key variables involved in the plasmid production process by E. coli cultures run under distinct conditions. Finally, owing to online monitoring and process control, an NIR fibre optic probe and chemometrics provided promising results concerning the control of biomass and carbon sources in E. coli cultures.