Two‐dimensional fluorescence as soft sensor in the monitoring of biotransformation performed by yeast

Soft sensors are powerful tools for bioprocess monitoring due to their ability to perform online, noninvasive measurement, and possibility of detection of multiple components in cultivation media, which in turn can provide tools for the quantification of more than one metabolite/substrate/product in real time. In this work, soft sensor based on excitation‐emission fluorescence is for the first time applied for the monitoring of biotransformation production of 2‐phenylethanol (2‐PE) by yeast strains. Main process parameters—such as optical density, glucose, and 2‐PE concentrations—were determined with high accuracy and precision by fluorescence fingerprinting coupled with partial least squares regression. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:299–307, 2017     

Expeditious prediction of post-mortem changes in frozen fish meat using three-dimensional fluorescence fingerprints

The present study was conducted to characterize fluorophores in the fish body using three-dimensional fluorescence fingerprints (3D-FFs) and to utilize these 3D-FFs obtained from frozen horse mackerel (Trachurus japonicus) fillets to predict early post-mortem changes. Alive fish were sacrificed instantly, preserved in ice until 2 days, and then filleted, vacuum packed, and frozen. Subsequently, 3D-FFs of the frozen fillets were acquired using F-7000 aided with a fiber probe. Post-mortem freshness changes were tracked by measuring adenylate energy charge (AEC) values and nicotinamide adenine dinucleotide (NAD and NADH) content. Partial least squares regression models for predicting AEC values and NADH content in frozen fish meat showed good fittings, with R² of 0.90 and 0.85, by utilizing eight and five excitation wavelengths, respectively, based on their fluorescence features acquired from standard fluorophores. This novel approach of 3D-FFs could be utilized as an efficient technique for at-line monitoring of frozen fish quality.     

Noninvasive tool for optical online monitoring of individual biomass concentrations in a defined coculture

Cocultures bear great potential in the conversion of complex substrates and process intensification, as well as, in the formation of unique components only available due to inter-species interactions. Dynamic data of coculture composition is necessary for understanding and optimizing coculture systems. However, most standard online determined parameters measure the sum of all species in the reactor system. The kinetic behavior of the individual species remains unknown. Up to now, different offline methods are available to determine the culture composition, as well as the online measurement of fluorescence of genetically modified organisms. To avoid any genetic modification, a noninvasive online monitoring tool based on the scattered light spectrum was developed for microtiter plate cultivations. To demonstrate the potential, a coculture consisting of the bacterium Lactococcus lactis and the yeast Kluyveromyces marxianus was cultivated. Via partial least squares regression of scattered light spectra, the online determination of the individual biomass concentrations without further sampling and analyses is possible. The results were successfully validated by a Coulter counter-analysis, taking advantage of the different cell sizes of both organisms. The findings prove the applicability of the new method to follow in detail the dynamics of a coculture.     

Quantification of protein mixture in chromatographic separation using multi‐wavelength UV spectra

In therapeutic protein production, the protein purification with chromatographic processes is of high importance in separating the qualified proteins from the impurities for consistent product quality. Therefore, to aid real‐time monitoring of the protein purification processes, various kinds of methodologies have been proposed until now. However, the majority of them still rely on the use of a single ultraviolet (UV) absorbance or the utilization of expensive and time‐consuming instruments, thus requiring a simple, fast, and cost‐effective methodology for protein quantification. In this study, the feasibility of using multiwavelength UV spectroscopy was investigated as an alternative tool for the real‐time monitoring of the protein mixtures in protein purification. To this end, three different proteins were selected as a model system for the protein mixture, and the multivariate UV spectra were analyzed to construct the reliable quantification models for different proteins of interest. By using various chemometrics tools, such as partial least squares (PLS), the validity of estimating the protein concentration from the UV spectra of the mixture samples was rigorously analyzed with their prediction performance, and the results indicated that the multiwavelength UV spectra had sufficient sensitivity and accuracy to estimate the protein concentrations in mixture, demonstrating its usefulness for the rapid quantification of the protein mixtures in protein purification. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:664–671, 2013     

Advances in on-line monitoring and control of mammalian cell cultures: Supporting the PAT initiative

In recent years, much attention has been directed towards the development of global methods for on-line process monitoring, especially since the Food and Drug Administration (FDA) launched the Process Analytical Technology (PAT) guidance, stimulating biopharmaceutical companies to update their monitoring tools to ensure a pre-defined final product quality. The ideal technologies for biopharmaceutical processes should operate in situ, be non-invasive and generate on-line information about multiple key bioprocess and/or metabolic variables. A wide range of spectroscopic techniques based on in situ probes have already been tested in mammalian cell cultures, such as near infrared (NIR), mid infrared (MIR), 2D fluorescence and dielectric capacitance spectroscopy; similarly, the electronic nose technique based on chemical array sensors has been tested for in situ off-gas analysis of mammalian cell cultures. All these methods provide series of spectra, from which meaningful information must be extracted. In this sense, data mining techniques such as principal components regression (PCR), partial least squares (PLS) or artificial neural networks (ANN) have been applied to handle the dense flow of data generated from the real-time process analyzers. Furthermore, the implementation of feedback control methods would help to improve process performance and ultimately ensure reproducibility. This review discusses the suitability of several spectroscopic techniques coupled with chemometric methods for improved monitoring and control of mammalian cell processes.     

Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement,using a tunable emission monochromator

Online fluorescence monitoring has become a key technology in modern bioprocess development, as it provides in-depth process knowledge at comparably low costs. In particular, the technology is widely established for high-throughput microbioreactor cultivation systems, due to its noninvasive character. For microtiter plates, previously also multi-wavelength 2D fluorescence monitoring was developed. To overcome an observed limitation of fluorescence sensitivity, this study presents a modified spectroscopic setup, including a tunable emission monochromator. The new optical component enables the separation of the scattered and fluorescent light measurements, which allows for the adjustment of integration times of the charge-coupled device detector. The resulting increased fluorescence sensitivity positively affected the performance of principal component analysis for spectral data of Escherichia coli batch cultivation experiments with varying sorbitol concentration supplementation. In direct comparison with spectral data recorded at short integration times, more biologically consistent signal dynamics were calculated. Furthermore, during partial least square regression for E. coli cultivation experiments with varying glucose concentrations, improved modeling performance was observed. Especially, for the growth-uncoupled acetate concentration, a considerable improvement of the root-mean-square error from 0.25 to 0.17 g/L was achieved. In conclusion, the modified setup represents another important step in advancing 2D fluorescence monitoring in microtiter plates.     

Counter effect of sucrose on ethanol-induced aggregation of protein

The present paper is an attempt to study the mechanism of ethanol induced aggregation of chicken egg albumin and to stabilize the protein against ethanol induced aggregation. The protein aggregation was determined by monitoring the light scattering of protein aggregates spectrophotometrically. The protein undergoes certain structural changes in water-ethanol solution and the degree of aggregation was found to be linearly depending upon the concentration of alcohol used. The intrinsic fluorescence study showed a large blue shift in the λ(max) (16 nm) in the presence of 50% ethanol. The ANS fluorescence intensity was found to be gradually increasing with increasing concentration of ethanol. This indicates an increase in the hydrophobic cluster on the protein surface and as a result the hydrophobic interaction is the major driving force for the aggregate formation. Addition of sucrose significantly reduced the ethanol-induced protein aggregation. In presence of 50% sucrose the ethanol the aggregation was reduced to 5%. The study reveals that addition of sucrose brings out changes in the solvent distribution and prevents the structural changes in protein which lead the aggregation.     

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R²) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.     

Partial inverse regression

In regression with a vector of quantitative predictors, sufficientdimension reduction methods can effectively reduce the predictordimension, while preserving full regression information andassuming no parametric model. However, all current reductionmethods require the sample size n to be greater than the numberof predictors p. It is well known that partial least squarescan deal with problems with n < p. We first establisha link between partial least squares and sufficient dimensionreduction. Motivated by this link, we then propose a new dimensionreduction method, entitled partial inverse regression. We showthat its sample estimator is consistent, and that its performanceis similar to or superior to partial least squares when n < p,especially when the regression model is nonlinear or heteroscedastic.An example involving the spectroscopy analysis of biscuit doughis also given.     

Fluorescence-based soft-sensor for monitoring beta-lactoglobulin and alpha-lactalbumin solubility during thermal aggregation

A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lactoglobulin (beta-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of alpha-LA and beta-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of beta-LG and alpha-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of alpha-LA and beta-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-wavelength fluorescence spectra collected under a variety of process conditions and provide a fairly accurate quantification of respective native-like proteins despite the significant overlapping between their emission profiles. It was also demonstrated that a PLS model can be used as a black-box prediction tool for estimating protein aggregation when combined with simple mass balances.     

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background

The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used.

Methods

Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content.

Results

In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip.

Conclusions

Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available.     

Using metabolic fingerprinting of plants for evaluating nitrogen deposition impacts on the landscape level

Nitrogen emissions and atmospheric deposition are globally significant with the potential to alter ecosystem nutrient balance, provoking changes in vegetation composition. Shifts in plant biochemistry are good indicators of nitrogen pollution and have been used to monitor vegetation health. Fourier transform‐infrared (FT‐IR) spectroscopy has previously been shown to be a rapid and relatively inexpensive method for evaluating leaf biochemistry. In the present study, FT‐IR spectra were collected from Galium saxatile samples taken from sites across the United Kingdom. Spectral changes in the tissue samples were correlated with a gradient of N deposition using partial least squares regression analysis. We show that FT‐IR analysis of G. saxatile leaf tissue is an effective way to evaluate nitrogen deposition across the entire UK landscape.     

Inline noninvasive Raman monitoring and feedback control of glucose concentration during ethanol fermentation

Raman spectroscopy as a process analytical technology tool was implemented for the monitoring and control of ethanol fermentation carried out with Saccharomyces cerevisiae. The need for the optimization of bioprocesses such as ethanol production, to increase product yield, enhanced the development of control strategies. The control system developed by the authors utilized noninvasive Raman measurements to avoid possible sterilization problems. Real-time data analysis was applied using partial least squares regression (PLS) method. With the aid of spectral pretreatment and multivariate data analysis, the monitoring of glucose and ethanol concentration was successful during yeast fermentation with the prediction error of 4.42 g/L for glucose and 2.40 g/L for ethanol. By Raman spectroscopy-based feedback control, the glucose concentration was maintained at 100 g/L by the automatic feeding of concentrated glucose solution. The control of glucose concentration during fed-batch fermentation resulted in increased ethanol production. Ethanol yield of 86% was achieved compared to the batch fermentation when 75 % yield was obtained. The results show that the use of Raman spectroscopy for the monitoring and control of yeast fermentation is a promising way to enhance process understanding and achieve consistently high production yield.     

Conformational transitions induced by in vitro macromolecular crowding lead to the amyloidogenesis of buffalo heart cystatin

The biological cells and extracellular matrix exhibit a highly crowded environment, called as macromolecular crowding. Crowding significantly influences protein structure and may lead to its aggregation. In the present study, buffalo heart cystatin (BHC), after purification from buffalo heart tissue, has been used as a model protein for studying effect of macromolecular crowding in the presence of high concentrations of bovine serum albumin (BSA), poly‐ethylene glycol‐1000 (PEG‐1000), and poly‐ethylene glycol‐4000 (PEG‐4000). Cystatins are thiol protease inhibitors and found to be involved in various important physiological processes. Functional inactivation of BHC was observed upon crowding, which varied as a function of concentration and molecular weight of crowding agents as well as incubation time. Structural changes of BHC at tertiary and secondary level were detected with the help of fluorescence and CD spectroscopy. CD analysis showed changes of α‐helix to β‐sheet, which could be due to aggregation. The ANS‐fluorescence study suggested the unfolding and presence of some partially folded intermediates. Increase in ThT‐fluorescence and absorption of Congo red spectra with red shift, confirmed the amyloid type aggregation of BHC in the presence of various crowding agents. Finally, electron microscopy provided the physical evidence about the formation of amyloid fibrils. Results suggested that among the various crowding agents used, amyloidogenesis of BHC was maximal in case of BSA followed by PEG‐4000 and least for PEG‐1000. The present work makes an important contribution in crowding mediated protein aggregation, which can have implications of potential interest. Copyright © 2015 John Wiley & Sons, Ltd.     

Salt effects on histone subunit interactions as studied by fluorescence spectroscopy

The salt concentration dependence of the aggregation properties of calf thymus and chicken erythrocyte histones has been investigated by using fluorescence spectroscopy. The isolated H2A/H2B and H3/H4 subunit preparations were labeled with 5-(dimethylamino)naphthalene-1- sulfonyl (dansyl). This long-lived fluorescence probe allows for the observation of rotations due to tumbling of the particle and thus is a probe for changes in the size of macromolecular assemblies. The fluorescence polarization and lifetime were measured as a function of salt concentration for these isolated preparations. Next, each labeled preparation was reconstituted with its unlabeled complement, and the salt concentration dependence of histone core octamer interactions was investigated in the same manner. Salt-induced core particle formation was observed by monitoring the dansyl-labeled dimers for both the calf thymus and chicken erythrocyte preparations. Evidence for subunit dissociation of the isolated H2A-H2B preparations was also found, as well as aggregation of the isolated H3/H4 subunits to at least dimers of tetramers. The calf thymus H3/H4 preparation was in aggregated form under all conditions studied, whereas the chicken erythrocyte H3/H4 only formed aggregates at high protein or salt concentrations. We have found evidence that the dimer can displace the tetramer from the higher order aggregate in order to form core particles. Such competition between the subunit interfaces in the histone system suggests that they may play a regulatory role in histone-DNA interactions.     

In situ 2D fluorometry and chemometric monitoring of mammalian cell cultures

The main objective of the present study was to investigate the use of in situ 2D fluorometry for monitoring key bioprocess variables in mammalian cell cultures, namely the concentration of viable cells and the concentration of recombinant proteins. All studies were conducted using a recombinant Baby Hamster Kidney (BHK) cell line expressing a fusion glycoprotein IgG1-IL2 cultured in batch and fed-batch modes. It was observed that the intensity of fluorescence signals in the excitation/emission wavelength range of amino acids, vitamins and NAD(P)H changed along culture time, although the dynamics of single fluorophors could not be correlated with the dynamics of the target state variables. Therefore, multivariate chemometric modeling was adopted as a calibration methodology. 2D fluorometry produced large volumes of redundant spectral data, which were first filtered by principal components analysis (PCA). Then, a partial least squares (PLS) regression was applied to correlate the reduced fluorescence maps with the target state variables. Two validation strategies were used to evaluate the predictive capacity of the developed PLS models. Accurate estimations of viable cells density (r(2) = 0.95; 99.2% of variance captured in the training set; r(2) = 0.91; 97.7% of variance captured in the validation set) and of glycoprotein concentration (r(2) = 0.99 and 99.7% of variance captured in the training set; r(2) = 0.99 and 99.3% of variance captured in the validation set) were obtained over a wide range of reactor operation conditions. The results presented herein confirm that 2D fluorometry constitutes a reliable methodology for on-line monitoring of viable cells and recombinant protein concentrations in mammalian cell cultures.     

Spline and General Regression of Polytomous Data

The paper shows a formulation for a general linear regression as well as a spline regression of multinomial responses on a quantitative input variables. Application of least squares and asymptotic theory yields the F-test for significance of coefficients and a t-test for structural discontinuity.     

Quantitative determination of cellulose dissolved in 1-ethyl-3-methylimidazolium acetate using partial least squares regression on FTIR spectra

Rapid and quantitative measurements of cellulose concentrations in ionic liquids (ILs) are difficult. In this study, FTIR operated in attenuated total reflectance (ATR) mode was investigated as a tool to measure cellulose concentration in 1-ethyl-3-methylimidazolium acetate ([emim][OAc]) and the spectra were subjected to partial least squares (PLS) regression for the quantitative determination of cellulose content. Additionally, the spectra were subjected to 7 data preprocessing methods to reduce physical effects in the spectra. Peak normalization was found to be the technique that most improved the prediction of dissolved cellulose in [emim][OAc]. When peak normalization was used for data preprocessing, a model for the quantitative estimation of cellulose content between 0 wt.% and 4 wt.% with an error of 0.53 wt.% was generated. The methods described here provide the basis for a rapid and facile technique for the determination of dissolved cellulose content in [emim][OAc].