Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods.

Bacterial 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.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts.

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

Antimicrobial evaluation of the Cynara cardunculus extract in Helicobacter pylori cells using mid-infrared spectroscopy and chemometric methods.

The 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.

Comparative analysis of different transformed Saccharomyces cerevisiae strains based on high-throughput Fourier transform infrared spectroscopy.

This study shows the application of the Fourier transform mid-infrared spectroscopy (FT-MIR) associated with high-throughput technology to study the biochemical fingerprints of different Saccharomyces cerevisiae strains transformed with the same expression system along the similar cultivation in bioreactor. The phenotype, as well as the cellular metabolism and recombinant cyprosin biosynthesis, were determined. The differences observed were confirmed by conventional cyprosin activity protocol, and the metabolic evolution was analyzed using high-performance liquid chromatography technique. The spectral analysis based on chemometrics tools, such as the principal component analysis, is a useful methodology for the phenotypes characterization as well as the specific metabolic states along the cultivations according to the clusters created. The ratio bands of spectra also represented a useful tool to evaluate the metabolic and biochemical differences between both expression systems, allowing to have an additional parameter to the biomolecular comparison. Therefore, high-throughput FT-MIR spectroscopy associated with multivariate data analysis represent a valuable strategy for extracting significant specific biomolecular information along the cultivation, providing a complete bioprocess analysis, once it detects slight molecular changes which it will be useful for screening and optimization process in the biotechnological or pharmaceutical industry.

Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis.

Escherichia 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 rapid, 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. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:285-298, 2017.

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 (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 (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.

In situ near infrared spectroscopy monitoring of cyprosin production by recombinant Saccharomyces cerevisiae strains.

Near 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 R(2)=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 R(2)=0.94 and a RMSEP=167 Um l(-1) were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0-2509 Um l(-1)), whereas a R(2)=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 Um g(-1)). For the carbon sources glucose and galactose, a R(2)=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 R(2)=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 R(2)=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.

Production and characterization of recombinant cyprosin B in Saccharomyces cerevisiae (W303-1A) strain.

The Saccharomyces cerevisiae W303-1A strain transformed with a centromeric plasmid containing CYPRO11, which codifies the aspartic protease cyprosin B, was grown in a 3 l bioreactor under aerobic conditions. Expression of cyprosin B is directly dependent on the concentration of galactose used as the inducer and carbon source in 1% yeast extract, 2% bactopeptone, and 4% galactose in culture medium. For 4% of galactose, 209 mg.l(-1) total protein, and 1036 U.ml(-1) recombinant cyprosin B activity were obtained from 6.1 g dcw.l(-1) biomass. The recombinant cyprosin B, purified by two consecutive anion-exchange chromatographies (diethyl amino-ethyl [DEAE]-Sepharose and Q-Sepharose XK-16 columns), shows a specific activity of 62 x 10(3) U.mg(-1), corresponding to a purification degree of 12.5-fold and a recovery yield of 25.6% relative to that in fermentation broth. The proteolytic activity of recombinant cyprosin B is optimal at 42 degrees C and pH 4.5. The recombinant cyprosin B activity is 95% inhibited by pepstatin A, which confirms its aspartic protease nature. The pure recombinant cyprosin B is composed of two subunits, one with 14 and the other with 32 kDa. It exhibits clotting activity, similar to that of the natural enzyme from Cynara cardunculus flowers. The results reported here show that recombinant cyprosin B, the first clotting protease of plant origin produced in a bioreactor, can now be produced in large scale and may constitute a new and efficient alternative to enzymes of animal or fungal origin that are widely used in cheese making.