High-throughput FTIR-based bioprocess analysis of recombinant cyprosin production

A portable dry film FTIR instrument for industrial food and bioprocess applications

The main objective of this study was to design, build, and test a compact, multi-well, portable dry film FTIR system for industrial food and bioprocess applications. The system features dry film sampling on a circular rotating disc comprising 31 wells, a design that was chosen to simplify potential automation and robotic sample handling at a later stage. Calibration models for average molecular weight (AMW, 200 samples) and collagen content (68 samples) were developed from the measurements of industrially produced protein hydrolysate samples in a controlled laboratory environment. Similarly, calibration models for the prediction of lactate content in samples from cultivation media (59 samples) were also developed. The portable dry film FTIR system showed reliable model characteristics which were benchmarked with a benchtop FTIR system. Subsequently, the portable dry film FTIR system was deployed in a bioprocessing plant, and protein hydrolysate samples were measured at-line in an industrial environment. This industrial testing involved building a calibration model for predicting AMW using 60 protein hydrolysate samples measured at-line using the portable dry film FTIR system and subsequent model validation using a test set of 26 samples. The industrial calibration in terms of coefficient of determination (R2 = 0.94), root mean square of cross-validation (RMSECV = 194 g mol-1), and root mean square of prediction (RMSEP = 162 g mol-1) demonstrated low prediction errors as compared to benchtop FTIR measurements, with no statistical difference between the calibration models of the two FTIR systems. This is to the authors' knowledge the first study for developing and employing a portable dry film FTIR system in the enzymatic protein hydrolysis industry for successful at-line measurements of protein hydrolysate samples. The study therefore suggests that the portable dry film FTIR instrument has huge potential for in/at-line applications in the food and bioprocessing industries.

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.

Multivariate analysis approach for assessing coated dry-cured ham flavor quality during long-term storage

This study aimed to investigate the effect of coatings on the quality of ripened dry-cured hams during long-term storage, especially the profile of volatile compounds. The coatings were made up of 33% palm oil, 16.5% water, 39.7% cassava starch, 6.8% corn starch, 1.6% mono- and diglycerides of fatty acids, 0.6% tert-butylhydroquinone (TBHQ), and 1.8% sodium carbonate. The results showed that the moisture content of the coated ham (48.93-49.59%) was higher than that of the noncoated ham (44.37%). The average peroxide value (POV) and b* value were lower in the coated hams than in the noncoated hams (5.52 and 8.99 meq/kg, respectively), and the sensory attributes of the coated hams had better overall acceptability scores. The changes in the contents of 39 volatile flavor compounds were evaluated through a multivariate statistical analysis, revealing that 20 identified compounds could be related to the decrease in fat pungent aroma, and most belonged to the long-chain benzene and carboxylic acid family. Meanwhile, 2-nonanone, nonanal, amyl alcohol, and 2-heptanone indicated that they could be used as markers to distinguish between the coated and noncoated groups.

A Chemometric Tool to Monitor and Predict Cell Viability in Filamentous Fungi Bioprocesses Using UV Chromatogram Fingerprints

Monitoring process variables in bioprocesses with complex expression systems, such as filamentous fungi, requires a vast number of offline methods or sophisticated inline sensors. In this respect, cell viability is a crucial process variable determining the overall process performance. Thus, fast and precise tools for identification of key process deviations or transitions are needed. However, such reliable monitoring tools are still scarce to date or require sophisticated equipment. In this study, we used the commonly available size exclusion chromatography (SEC) HPLC technique to capture impurity release information in Penicillium chrysogenum bioprocesses. We exploited the impurity release information contained in UV chromatograms as fingerprints for development of principal component analysis (PCA) models to descriptively analyze the process trends. Prediction models using well established approaches, such as partial least squares (PLS), orthogonal PLS (OPLS) and principal component regression (PCR), were made to predict the viability with model accuracies of 90% or higher. Furthermore, we demonstrated the platform applicability of our method by monitoring viability in a Trichoderma reesei process for cellulase production. We are convinced that this method will not only facilitate monitoring viability of complex bioprocesses but could also be used for enhanced process control with hybrid models in the future.

A Simple, Label-Free, and High-Throughput Method to Evaluate the Epigallocatechin-3-Gallate Impact in Plasma Molecular Profile

Epigallocatechin-3-gallate (EGCG), the major catechin present in green tea, presents diverse appealing biological activities, such as antioxidative, anti-inflammatory, antimicrobial, and antiviral activities, among others. The present work evaluated the impact in the molecular profile of human plasma from daily consumption of 225 mg of EGCG for 90 days. Plasma from peripheral blood was collected from 30 healthy human volunteers and analyzed by high-throughput Fourier transform infrared spectroscopy. To capture the biochemical information while minimizing the interference of physical phenomena, several combinations of spectra pre-processing methods were evaluated by principal component analysis. The pre-processing method that led to the best class separation, that is, between the plasma spectral data collected at the beginning and after the 90 days, was a combination of atmospheric correction with a second derivative spectra. A hierarchical cluster analysis of second derivative spectra also highlighted the fact that plasma acquired before EGCG consumption presented a distinct molecular profile after the 90 days of EGCG consumption. It was also possible by partial least squares regression discriminant analysis to correctly predict all unlabeled plasma samples (not used for model construction) at both timeframes. We observed that the similarity in composition among the plasma samples was higher in samples collected after EGCG consumption when compared with the samples taken prior to EGCG consumption. Diverse negative peaks of the normalized second derivative spectra, associated with lipid and protein regions, were significantly affected (p < 0.001) by EGCG consumption, according to the impact of EGCG consumption on the patients’ blood, low density and high density lipoproteins ratio. In conclusion, a single bolus dose of 225 mg of EGCG, ingested throughout a period of 90 days, drastically affected plasma molecular composition in all participants, which raises awareness regarding prolonged human exposure to EGCG. Because the analysis was conducted in a high-throughput, label-free, and economic analysis, it could be applied to high-dimension molecular epidemiological studies to further promote the understanding of the effect of bio-compound consumption mode and frequency.

Novel near-infrared fluorescent probe for live cell imaging

Near infrared (NIR) fluorescent probes play a crucial role in biological system imaging. A novel NIR fluorescent probe, IR787, was designed in the present study. Compared with indocyanine green (ICG), IR787 showed lower background fluorescent interference and higher fluorescence enhancement. Fluorescence intensities were detected by a Cary Eclipse fluorescence spectrophotometer. The interference of intracellular ions (Cu²⁺, Ca²⁺, Mg²⁺ and Zn²⁺) on the measurement was negligible, which indicated a good photostability of IR787. MTT assay demonstrated that cell viability of human lung adenocarcinoma epithelial cell line A549 was not significantly affected by the use of the IR787 probe compared with the ICG probe. This result suggested that the IR787 probe was safe for in vitro cell imaging. In vitro NIR optical imaging experiments further revealed cellular uptake and strong intracellular NIR fluorescence of the IR787 probe in A549 cells. The excitation wavelength was 787 nm for IR787. Compared with the previously reported NIR fluorescent probe ICG, the IR787 NIR fluorescent probe had improved prospects for intracellular imaging. IR787 may play a pivotal role in the understanding cell biology, pharmacology and disease diagnosis.

A phenotypic screening bioassay for Escherichia coli stress and antibiotic responses based on Fourier-transform infrared (FTIR) spectroscopy and multivariate analysis

AIMS

To develop and optimize a Fourier-transform infrared spectroscopy (FTIRS) phenotypic screening bioassay for stress responses, regarding the effect of nutrient content, bacterial growth phase and stress agent exposure time.

METHODS AND RESULTS

A high-throughput FTIRS bioassay was developed to distinguish the stress responses of Escherichia coli to sodium hydroxide, hydrochloric acid, sodium chloride, sodium hypochlorite and ethanol. Principal component analysis and hierarchical clustering were used to quantify the effect of each parameter on bioassay performance, namely its reproducibility and metabolic resolution. Bioassay performance varied greatly, ranging from poor to very good. Spectra were partitioned into biologically relevant regions to evaluate their contributions to bioassay performance, but further improvements were not observed. Bioassay optimization was validated against empirical parameters, which confirmed a closer representation of known mechanisms on the antibiotic-induced stress responses.

CONCLUSIONS

The optimized bioassay used standard nutrient content, cells in the late-stationary growth phase and a one-shift exposure duration. Only the optimized bioassay adequately and reproducibly distinguished the E. coli stress and antibiotic responses. The absence of performance improvements using partitioned spectra indicated that stress responses are imprinted on the whole-spectra metabolic signature.

SIGNIFICANCE AND IMPACT OF THE STUDY

Highly optimized FTIRS bioassay parameters are vital in capturing whole-spectra metabolic signatures that can be used for satisfactory and reproducible phenotypic screening of stress and antibiotic responses.

Cardoon-based rennets for cheese production

The use of crude aqueous extracts of Cynara cardunculus flowers as coagulants in the production of high-quality sheep and goat cheeses-as are the cases of several Portuguese and Spanish cheese varieties with Protected Designation of Origin status-has been maintained since ancient times. The unique rheological attributes and sensory properties characteristic of these cheeses have always suggested that this plant coagulant (and, therefore, its isolated milk-clotting proteases) could be used as alternative rennet in the dairy industry, particularly suited for the production of sheep and goat cheeses. However, the lack of standardization of C. cardunculus crude flower extracts, whose quality and performance depends on numerous factors, has always hampered the application of this plant rennet in industrial production scales. To overcome these limitations, and to aim at developing more effective solutions with potential for scalability of production and commercial application, several strategies have been undertaken in more recent years to establish new cardoon-based rennets. This review provides an overview on these developments and on the currently available solutions, which range from producing standardized formulations of native cardoon enzymes, to the optimization of the heterologous production of cardosins and cyprosins to generate synthetic versions of these milk-clotting enzymes. Challenges and emerging opportunities are also discussed.

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Hyperspectral imaging covering the spectral range of 384-1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.