Metabolic fingerprinting as a tool to monitor whole-cell biotransformations.

Fourier transform infrared (FT-IR) spectroscopy was employed as a rapid high-throughput phenotypic typing technique to generate metabolic fingerprints of Escherichia coli MG1655 pDTG601A growing in fed-batch culture, during the dioxygenase-catalysed biotransformation of toluene to toluene cis-glycol. With toluene fed as a vapour, the final toluene cis-glycol concentration was 83 mM, whereas the product concentration was only 22 mM when the culture was supplied with liquid toluene. Multivariate statistical analysis employing cluster analysis was used to analyse the dynamic changes in the data. The analysis revealed distinct trends and trajectories in cluster ordination space, illustrating phenotypic changes related to differences in the growth and product formation of the cultures. In addition, partial least squares regression was used to correlate the FT-IR metabolic fingerprints with the levels of toluene cis-glycol and acetate, the latter being an indicator of metabolic stress. We propose that this high-throughput metabolic fingerprinting approach is an ideal tool to assess temporal biochemical dynamics in complex biological processes, as demonstrated by this redox biotransformation. Moreover, this approach can also give useful information on product yields and fermentation health indicators directly from the fermentation broth without the need for lengthy chromatographic analysis of the products.

Raman chemical mapping reveals site of action of HIV protease inhibitors in HPV16 E6 expressing cervical carcinoma cells.

It has been shown that the HIV protease inhibitors indinavir and lopinavir may have activity against the human papilloma virus (HPV) type 16 inhibiting HPV E6-mediated proteasomal degradation of p53 in cultured cervical carcinoma cells. However, their mode and site of action is unknown. HPV-negative C33A cervical carcinoma cells and the same cells stably transfected with E6 (C33AE6) were exposed to indinavir and lopinavir at concentrations of 1 mM and 30 µM, respectively. The intracellular distribution of metabolites and metabolic changes induced by these treatments were investigated by Raman microspectroscopic imaging combined with the analysis of cell fractionation products by liquid chromatography-mass spectrometry (LC-MS). A uniform cellular distribution of proteins was found in drug-treated cells irrespective of cell type. Indinavir was observed to co-localise with nucleic acid in the nucleus, but only in E6 expressing cells. Principal components analysis (PCA) score maps generated on the full Raman hypercube and the corresponding PCA loadings plots revealed that the majority of metabolic variations influenced by the drug exposure within the cells were associated with changes in nucleic acids. Analysis of cell fractionation products by LC-MS confirmed that the level of indinavir in nuclear extracts was approximately eight-fold greater than in the cytoplasm. These data demonstrate that indinavir undergoes enhanced nuclear accumulation in E6-expressing cells, which suggests that this is the most likely site of action for this compound against HPV.

Phenotypic characterization of Shewanella oneidensis MR-1 under aerobic and anaerobic growth conditions by using fourier transform infrared spectroscopy and high-performance liquid chromatography analyses.

Shewanella oneidensis is able to conserve energy for growth by reducing a wide variety of terminal electron acceptors during anaerobic respiration, including several environmentally hazardous pollutants. This bacterium employs various electron transfer mechanisms for anaerobic respiration, including cell-bound reductases and secreted redox mediators. The aim of this study was to develop rapid tools for profiling the key metabolic changes associated with these different growth regimes and physiological responses. Initial experiments focused on comparing cells grown under aerobic and anaerobic conditions. Fourier transform infrared (FT-IR) spectroscopy with cluster analysis showed that there were significant changes in the metabolic fingerprints of the cells grown under these two culture conditions. FT-IR spectroscopy clearly differentiated cells of S. oneidensis MR-1 cultured at various growth points and cells grown using different electron acceptors, resulting in different phenotypic trajectories in the cluster analysis. This growth-related trajectory analysis is applied successfully for the first time, here with FT-IR spectroscopy, to investigate the phenotypic changes in contrasting S. oneidensis cells. High-performance liquid chromatography (HPLC) was also used to quantify the concentrations of flavin compounds, which have been identified recently as extracellular redox mediators released by a range of Shewanella species. The partial least-squares regression (PLSR) multivariate statistical technique was combined with FT-IR spectroscopy to predict the concentrations of the flavins secreted by cells of S. oneidensis MR-1, suggesting that this combination could be used as a rapid alternative to conventional chromatographic methods for analysis of flavins in cell cultures. Furthermore, coupling of the FT-IR spectroscopy and HPLC techniques appears to offer a potentially useful tool for rapid characterization of the Shewanella cell metabolome in various process environments.

Fourier transform infrared spectroscopy as a metabolite fingerprinting tool for monitoring the phenotypic changes in complex bacterial communities capable of degrading phenol.

The coking process produces great volumes of wastewater contaminated with pollutants such as cyanides, sulfides and phenolics. Chemical and physical remediation of this wastewater removes the majority of these pollutants; however, these processes do not remove phenol and thiocyanate. The removal of these compounds has been effected during bioremediation with activated sludge containing a complex microbial community. In this investigation we acquired activated sludge from an industrial bioreactor capable of degrading phenol. The sludge was incubated in our laboratory and monitored for its ability to degrade phenol over a 48 h period. Multiple samples were taken across the time-course and analysed by Fourier transform infrared (FT-IR) spectroscopy. FT-IR was used as a whole-organism fingerprinting approach to monitor biochemical changes in the bacterial cells during the degradation of phenol. We also investigated the ability of the activated sludge to degrade phenol following extended periods (2-131 days) of storage in the absence of phenol. A reduction was observed in the ability of the microbial community to degrade phenol and this was accompanied by a detectable biochemical change in the FT-IR fingerprint related to cellular phenotype of the microbial community. In the absence of phenol a decrease in thiocyanate vibrations was observed, reflecting the ability of these communities to degrade this substrate. Actively degrading communities showed an additional new band in their FT-IR spectra that could be attributed to phenol degradation products from the ortho- and meta-cleavage of the aromatic ring. This study demonstrates that FT-IR spectroscopy when combined with chemometric analysis is a very powerful high throughput screening approach for assessing the metabolic capability of complex microbial communities.

Multiobjective evolutionary optimisation for surface-enhanced Raman scattering.

In most optimisation experiments, a single parameter is first optimised before a second and then third one are subsequently modified to give the best result. By contrast, we believe that simultaneous multiobjective optimisation is more powerful; therefore, an optimisation of the experimental conditions for the colloidal SERS detection of L-cysteine was carried out. Six aggregating agents and three different colloids (citrate, borohydride and hydroxylamine reduced silver) were tested over a wide range of concentrations for the enhancement and the reproducibility of the spectra produced. The optimisation was carried out using two methods, a full factorial design (FF, a standard method from the experimental design literature) and, for the first time, a multiobjective evolutionary algorithm (MOEA), a method more usually applied to optimisation problems in computer science. Simulation results suggest that the evolutionary approach significantly out-performs random sampling. Real experiments applying the evolutionary method to the SERS optimisation problem led to a 32% improvement in enhancement and reproducibility compared with the FF method, using far fewer evaluations.

Combining metabolic fingerprinting and footprinting to understand the phenotypic response of HPV16 E6 expressing cervical carcinoma cells exposed to the HIV anti-viral drug lopinavir.

Recently, it has been reported that the anti-viral drug, lopinavir, which is currently used as a human immunodeficiency virus (HIV) protease inhibitor, could also inhibit E6-mediated proteasomal degradation of mutant p53 in E6-transfected C33A cells. In this study, C33A parent control cells and HPV16 E6-transfected cells were exposed to lopinavir at concentrations ranging from 0 to 30 microM. The phenotypic response was assessed by Fourier transform infrared (FT-IR) spectroscopy directly on cells (the metabolic fingerprint) and on the cell growth medium (the metabolic footprint). Multivariate analysis of the data using both principal components analysis (PCA) and canonical variates analysis (PC-CVA) showed trends in scores plots that were related to the concentration of the drug. Inspection of the PC-CVA loadings vector revealed that the effect was not due to the drug alone and that several IR spectral regions including proteins, nucleotides and carbohydrates contributed to the separation in PC-CVA space. Finally, partial least squares regression (PLSR) could be used to predict the concentration of the drug accurately from the metabolic fingerprints and footprints, indicating a dose related phenotypic response. This study shows that the combination of metabolic fingerprinting and footprinting with appropriate chemometric analysis is a valuable approach for studying cellular responses to anti-viral drugs.

Shining light on the microbial world the application of Raman microspectroscopy.

Raman microspectroscopy is a noninvasive, label-free, and single-cell technology for biochemical analysis of individual mammalian cells, organelles, bacteria, viruses, and nanoparticles. Chemical information derived from a Raman spectrum provides comprehensive and intrinsic information (e.g., nucleic acids, protein, carbohydrates, and lipids) of single cells without the need of any external labeling. A Raman spectrum functions as a molecular "fingerprint" of single cells, which enables the differentiation of cell types, physiological states, nutrient condition, and variable phenotypes. Raman microspectroscopy combined with stable isotope probing, fluorescent in situ hybridization, and optical tweezers offers a culture-independent approach to study the functions and physiology of unculturable microorganisms in the ecosystem. Here, we review the application of Raman microspectroscopy to microbiology research with particular emphasis on single bacterial cells.

Rapid monitoring of recombinant antibody production by mammalian cell cultures using fourier transform infrared spectroscopy and chemometrics.

Fourier transform infrared (FT-IR) spectroscopy combined with multivariate statistical analyses was investigated as a physicochemical tool for monitoring secreted recombinant antibody production in cultures of Chinese hamster ovary (CHO) and murine myeloma non-secreting 0 (NS0) cell lines. Medium samples were taken during culture of CHO and NS0 cells lines, which included both antibody-producing and non-producing cell lines, and analyzed by FT-IR spectroscopy. Principal components analysis (PCA) alone, and combined with discriminant function analysis (PC-DFA), were applied to normalized FT-IR spectroscopy datasets and showed a linear trend with respect to recombinant protein production. Loadings plots of the most significant spectral components showed a decrease in the C-O stretch from polysaccharides and an increase in the amide I band during culture, respectively, indicating a decrease in sugar concentration and an increase in protein concentration in the medium. Partial least squares regression (PLSR) analysis was used to predict antibody titers, and these regression models were able to predict antibody titers accurately with low error when compared to ELISA data. PLSR was also able to predict glucose and lactate amounts in the medium samples accurately. This work demonstrates that FT-IR spectroscopy has great potential as a tool for monitoring cell cultures for recombinant protein production and offers a starting point for the application of spectroscopic techniques for the on-line measurement of antibody production in industrial scale bioreactors.

Monitoring the effects of chiral pharmaceuticals on aquatic microorganisms by metabolic fingerprinting.

The effects of the chiral pharmaceuticals atenolol and propranolol on Pseudomonas putida, Pseudomonas aeruginosa, Micrococcus luteus, and Blastomonas natatoria were investigated. The growth dynamics of exposed cultures were monitored using a Bioscreen instrument. In addition, Fourier-transform infrared (FT-IR) spectroscopy with appropriate chemometrics and high-performance liquid chromatography (HPLC) were employed in order to investigate the phenotypic changes and possible degradation of the drugs in exposed cultures. For the majority of the bacteria studied there was not a statistically significant difference in the organism's phenotype when it was exposed to the different enantiomers or mixtures of enantiomers. In contrast, the pseudomonads appeared to respond differently to propranolol, and the two enantiomers had different effects on the cellular phenotype. This implies that there were different metabolic responses in the organisms when they were exposed to the different enantiomers. We suggest that our findings may indicate that there are widespread effects on aquatic communities in which active pharmaceutical ingredients are present.

Non-invasive metabolomic analysis of breath using differential mobility spectrometry in patients with chronic obstructive pulmonary disease and healthy smokers.

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm(3) volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal-Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (alpha = 19% and beta = 12.4%).

Impact of silver(I) on the metabolism of Shewanella oneidensis.

Anaerobic cultures of Shewanella oneidensis MR-1 reduced toxic Ag(I), forming nanoparticles of elemental Ag(0), as confirmed by X-ray diffraction analyses. The addition of 1 to 50 microM Ag(I) had a limited impact on growth, while 100 microM Ag(I) reduced both the doubling time and cell yields. At this higher Ag(I) concentration transmission electron microscopy showed the accumulation of elemental silver particles within the cell, while at lower concentrations the metal was exclusively reduced and precipitated outside the cell wall. Whole organism metabolite fingerprinting, using the method of Fourier transform infrared spectroscopy analysis of cells grown in a range of silver concentrations, confirmed that there were significant physiological changes at 100 microM silver. Principal component-discriminant function analysis scores and loading plots highlighted changes in certain functional groups, notably, lipids, amides I and II, and nucleic acids, as being discriminatory. Molecular analyses confirmed a dramatic drop in cellular yields of both the phospholipid fatty acids and their precursor molecules at high concentrations of silver, suggesting that the structural integrity of the cellular membrane was compromised at high silver concentrations, which was a result of intracellular accumulation of the toxic metal.

Explanatory multivariate analysis of ToF-SIMS spectra for the discrimination of bacterial isolates.

Multivariate analysis (PC-CVA and GA-CVA) was carried out on time-of-flight secondary ion mass spectra (ToF-SIMS) derived from 16 bacterial isolates associated with urinary tract infections, with an objective of extracting the spectral information relevant to their species-level discrimination. The use of spectral pre-processing, such as removal of the dominant peaks prior to analysis and analysis of the dominant peaks alone, enabled the identification of 37 peaks contributing to the principal components-canonical variates analysis (PC-CVA) discrimination of the bacterial isolates in the mass range of m/z 1-1000. These included signals at m/z 70, 84, 120, 134, 140, 150, 175 and 200. A univariate statistical analysis (Kruskal-Wallis) of the signal intensities at the identified m/z enabled an understanding of the discriminatory basis, which can be used in the development of robust parsimonious models for predictive purposes. The utility of genetic algorithm (GA)-based feature selection in identifying the discriminatory variables is also demonstrated. A database search of the identified signals enabled the biochemical origins of some these signals to be postulated.

Towards quantitatively reproducible substrates for SERS.

There is a need for a method to facilitate the development of novel, reproducible colloidal surface-enhanced Raman scattering (SERS) substrates to encourage the use of SERS in applied studies. In this study we show for the first time that by using suitably designed SERS experiments in conjunction with multivariate analysis of variance (MANOVA), an objective assessment of colloidal SERS reproducibility can be made. This is demonstrated with the analyte cresyl violet, but could be extended to any analyte of interest for which reproducible SERS data are needed.

Surface-enhanced Raman scattering from intracellular and extracellular bacterial locations.

While surface-enhanced Raman scattering (SERS) can increase the Raman cross-section by 4-6 orders of magnitude, for SERS to be effective it is necessary for the analyte to be either chemically bonded or within close proximity to the metal surface used. Therefore most studies investigating the biochemical constituents of microorganisms have introduced an external supply of gold or silver nanoparticles. As a consequence, the study of bacteria by SERS has to date been focused almost exclusively on the extracellular analysis of the Gram-negative outer cell membrane. Bacterial cells typically measure as little as 0.5 by 1 mum, and it is difficult to introduce a nanometer sized colloidal metal particle into this tiny environment. However, dissimilatory metal-reducing bacteria, including Shewanella and Geobacter species, can reduce a wide range of high valence metal ions, often within the cell, and for Ag(I) and Au(III) this can result in the formation of colloidal zero-valent particles. Here we report, for the first time, SERS of the bacterium Geobacter sulfurreducens facilitated by colloidal gold particles precipitated within the cell. In addition, we show SERS from the same organism following reduction of ionic silver, which results in colloidal silver depositions on the cell surface.

Characterisation and identification of bacteria using SERS.

Within microbiology Raman spectroscopy is considered as a very important whole-organism fingerprinting technique, which is used to characterise, discriminate and identify microorganisms and assess how they respond to abiotic or biotic stress. Enhancing the sensitivity of Raman spectroscopy is very beneficial for the rapid analysis of bacteria (and indeed biological systems in general), where the ultimate goal is to achieve this without the need for lengthy cell culture. Bypassing this step would provide significant benefits in many areas such as medical, environmental and industrial microbiology, microbial systems biology, biological warfare countermeasures and bioprocess monitoring. In this tutorial review we will report on the advances made in bacterial studies, a relatively new and exciting application area for SERS.

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy.

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.

PYCHEM: a multivariate analysis package for python.

UNLABELLED: We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. AVAILABILITY: http://sourceforge.net/projects/pychem

Surface-enhanced Raman scattering for the rapid discrimination of bacteria.

Raman spectroscopy is attracting interest for the rapid identification of bacteria and fungi and is now becoming accepted as a potentially powerful whole-organism fingerprinting technique. However, the Raman effect is so weak that collection times are lengthy, and this insensitivity means that bacteria must be cultured to gain enough biomass, which therefore limits its usefulness in clinical laboratories where high-throughput analyses are needed. The Raman effect can fortunately be greatly enhanced (by some 10(3)-10(6)-fold) if the molecules are attached to, or microscopically close to, a suitably roughened surface; a technique known as surface-enhanced Raman scattering (SERS). In this study we investigated SERS, employing an aggregated silver colloid substrate, for the analysis of a closely related group of bacteria belonging to the genus Bacillus. Each spectrum took only 20 s to collect and highly reproducible data were generated. The multivariate statistical technique of principal components-discriminant function analysis (PC-DFA) was used to group these bacteria based on their SERS fingerprints. The resultant ordination plots showed that the SERS spectra were highly discriminatory and gave accurate identification at the strain level. In addition, Bacillus species also undergo sporulation, and we demonstrate that SERS peaks that could be attributed to the dipicolinic acid biomarker, could be readily generated from Bacillus spores.

Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data.

MOTIVATION: The major difficulties relating to mathematical modelling of spectroscopic data are inconsistencies in spectral reproducibility and the black box nature of the modelling techniques. For the analysis of biological samples the first problem is due to biological, experimental and machine variability which can lead to sample size differences and unavoidable baseline shifts. Consequently, there is often a requirement for mathematical correction(s) to be made to the raw data if the best possible model is to be formed. The second problem prevents interpretation of the results since the variables that most contribute to the analysis are not easily revealed; as a result, the opportunity to obtain new knowledge from such data is lost. METHODS: We used genetic algorithms (GAs) to select spectral pre-processing steps for Fourier transform infrared (FT-IR) spectroscopic data. We demonstrate a novel approach for the selection of important discriminatory variables by GA from FT-IR spectra for multi-class identification by discriminant function analysis (DFA). RESULTS: The GA selects sensible pre-processing steps from a total of approximately 10(10) possible mathematical transformations. Application of these algorithms results in a 16% reduction in the model error when compared against the raw data model. GA-DFA recovers six variables from the full set of 882 spectral variables against which a satisfactory DFA model can be formed; thus inferences can be made as to the biochemical differences that are reflected by these spectral bands.

Surface-enhanced Raman spectroscopy for bacterial discrimination utilizing a scanning electron microscope with a Raman spectroscopy interface.

Surface-enhanced Raman scattering (SERS) utilizing colloidal silver has already been shown to provide a rapid means of generating "whole-organism fingerprints" for use in bacterial identification and discrimination. However, one of the main drawbacks of the technique for the analysis of microbiological samples with optical Raman microspectroscopy has been the inability to acquire pre-emptively a region of the sample matrix where both the SERS substrate and biomass are both present. In this study, we introduce a Raman interface for scanning electron microscopy (SEM) and demonstrate the application of this technology to the reproducible and targeted collection of bacterial SERS spectra. In secondary electron mode, the SEM images clearly reveal regions of the sample matrix where the sodium borohydride-reduced silver colloidal particles are present, Stokes spectra collected from these regions are rich in vibrational bands, whereas spectra taken from other areas of the sample elicit a strong fluorescence response. Replicate SERS spectra were collected from two bacterial strains and show excellent reproducibility both by visual inspection and as demonstrated by principal components analysis on the whole SERS spectra.