Comparability of Raman Spectroscopic Configurations: A Large Scale Cross-Laboratory Study.

The variable configuration of Raman spectroscopic platforms is one of the major obstacles in establishing Raman spectroscopy as a valuable physicochemical method within real-world scenarios such as clinical diagnostics. For such real world applications like diagnostic classification, the models should ideally be usable to predict data from different setups. Whether it is done by training a rugged model with data from many setups or by a primary-replica strategy where models are developed on a 'primary' setup and the test data are generated on 'replicate' setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable. However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if the same samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumental configuration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correct for them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35 Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST (European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allows various instrumental configurations ranging from highly confocal setups to fibre-optic based systems with different excitation wavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts, intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improve the inter-laboratory studies.

Rapid differentiation of Campylobacter jejuni cell wall mutants using Raman spectroscopy, SERS and mass spectrometry combined with chemometrics.

The Gram-negative bacterial pathogen Campylobacter jejuni is a major cause of foodborne gastroenteritis worldwide. Rapid detection and identification of C. jejuni informs timely prescription of appropriate therapeutics and epidemiological investigations. Here, for the first time, we report the applicability of Raman spectroscopy, surface-enhanced Raman scattering (SERS) and matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-TOF-MS) combined with chemometrics, for rapid differentiation and characterisation of mutants of a single isogenic C. jejuni strain that disrupt the production of prominent surface features (capsule, flagella and glycoproteins) of the bacterium. Multivariate analysis of the spectral data obtained from these different physicochemical tools revealed distinctive biochemical differences which consistently discriminated between these mutants. In order to generate biochemical and phenotypic information from different locations in the cell-cell wall versus cytoplasm - we developed two different in situ methods for silver nanoparticle (AgNP) production, and compared this with simple mixing of bacteria with pre-synthesised AgNPs. This SERS trilogy (simple mixing with premade AgNPs and two in situ AgNP production methods) presents an integrated platform with potential for rapid, accurate and confirmatory detection of pathogenic bacteria based on cell envelope or intracellular molecular dynamics. Our spectral findings demonstrate that Raman, SERS and MALDI-TOF-MS are powerful metabolic fingerprinting techniques capable of discriminating clinically relevant cell wall mutants of a single isogenic bacterial strain.

Rapid Detection and Quantification of Novel Psychoactive Substances (NPS) Using Raman Spectroscopy and Surface-Enhanced Raman Scattering.

With more than a million seizures of illegal drugs reported annually across Europe, the variety of psychoactive compounds available is vast and ever-growing. The multitude of risks associated with these compounds are well-known and can be life threatening. Hence the need for the development of new analytical techniques and approaches that allow for the rapid, sensitive, and specific quantitative detection and discrimination of such illicit materials, ultimately with portability for field testing, is of paramount importance. The aim of this study was to demonstrate the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) combined with chemometrics approaches, as rapid and portable techniques for the quantitative detection and discrimination of a wide range of novel psychoactive substances (methcathinone and aminoindane derivatives), both in powder form and in solution. The Raman spectra of the psychoactive compounds provided clear separation and classification of the compounds based on their core chemical structures; viz. methcathinones, aminoindanes, diphenidines, and synthetic cannabinoids. The SERS results also displayed similar clustering patterns, with improved limits of detections down to ~2 mM (0.41 g L-1). As mephedrone is currently very popular for recreational use we performed multiplexed quantitative detection of mephedrone (4-methylmethcathinone), and its two major metabolites (nor-mephedrone and 4-methylephedrine), as tertiary mixtures in water and healthy human urine. These findings readily illustrate the potential application of SERS for simultaneous detection of multiple NPS as mixtures without the need for lengthy prior chromatographic separation or enrichment methods.

Detection of the adulteration of fresh coconut water via NMR spectroscopy and chemometrics.

Here, we applied NMR spectroscopy in combination with chemometrics to quantify the adulteration of fresh coconut water, stretched with water-sugar mixtures. Coconut water was extracted from young Costa Rican coconuts and adulterated with concentrations of various sugar solutions. A total of 45 samples were analysed by 1D proton NMR spectroscopy and chemometrics. Results showed highly sensitive quantification, with a limit of detection of adulteration with sugars of 1.3% and a root-mean-squared error of prediction of 0.58%. Interestingly, we identified a regular drift in the chemical shift and a change in the lineshape of malic acid signals concomitant with increasing levels of adulteration. On further investigation, this was found to originate from changes in the concentration of divalent cations, such as magnesium, within the samples. It can be concluded that 1H NMR spectroscopy enables accurate quantification for the degree of adulteration in this product, with the added discovery finding that the shift and lineshape of the malic acid signal can be utilised as a potential diagnostic marker for partial substitution of fresh coconut water with extrinsic components such as sugar mixtures.

Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics.

Here, for the first time, we developed Raman spectroscopy in combination with chemometrics for the quantification of adulteration of fresh coconut water by dilution, and its masking with sugars. Coconut water was extracted from young Costa Rican coconuts and heat treated to emulate pasteurization. Samples were then adulterated by dilution with water and single sugars, mixtures of sugars, and high-fructose corn syrup (HFCS). A total of 155 samples were analysed with Raman spectroscopy at 785 nm excitation and 620 spectra analysed with chemometrics. Results showed successful quantification of dilution and adulteration with single sugars between 1.9 and 2.6%, masking of dilution with mixtures of sugars at 9.8%, and masking of dilution with HFCS at 7.1%. It can be concluded that Raman spectroscopy has significant potential as a rapid accurate analytical method for the detection of adulteration in this product, with the ability to discern small abnormalities in sugar ratios within coconut water.

Surface-Enhanced Raman Scattering (SERS) in Microbiology: Illumination and Enhancement of the Microbial World.

The microbial world forms a huge family of organisms that exhibit the greatest phylogenetic diversity on Earth and thus colonize virtually our entire planet. Due to this diversity and subsequent complex interactions, the vast majority of microorganisms are involved in innumerable natural bioprocesses and contribute an absolutely vital role toward the maintenance of life on Earth, whilst a small minority cause various infectious diseases. The ever-increasing demand for environmental monitoring, sustainable ecosystems, food security, and improved healthcare systems drives the continuous search for inexpensive but reproducible, automated and portable techniques for detection of microbial isolates and understanding their interactions for clinical, environmental, and industrial applications and benefits. Surface-enhanced Raman scattering (SERS) is attracting significant attention for the accurate identification, discrimination and characterization and functional assessment of microbial cells at the single cell level. In this review, we briefly discuss the technological advances in Raman and Fourier transform infrared (FT-IR) instrumentation and their application for the analysis of clinically and industrially relevant microorganisms, biofilms, and biological warfare agents. In addition, we summarize the current trends and future prospects of integrating Raman/SERS-isotopic labeling and cell sorting technologies in parallel, to link genotype-to-phenotype in order to define community function of unculturable microbial cells in mixed microbial communities which possess admirable traits such as detoxification of pollutants and recycling of essential metals.

Through-container, extremely low concentration detection of multiple chemical markers of counterfeit alcohol using a handheld SORS device.

Major food adulteration incidents occur with alarming frequency and are episodic, with the latest incident, involving the adulteration of meat from 21 producers in Brazil supplied to 60 other countries, reinforcing this view. Food fraud and counterfeiting involves all types of foods, feed, beverages, and packaging, with the potential for serious health, as well as significant economic and social impacts. In the spirit drinks sector, counterfeiters often 'recycle' used genuine packaging, or employ good quality simulants. To prove that suspect products are non-authentic ideally requires accurate, sensitive, analysis of the complex chemical composition while still in its packaging. This has yet to be achieved. Here, we have developed handheld spatially offset Raman spectroscopy (SORS) for the first time in a food or beverage product, and demonstrate the potential for rapid in situ through-container analysis; achieving unequivocal detection of multiple chemical markers known for their use in the adulteration and counterfeiting of Scotch whisky, and other spirit drinks. We demonstrate that it is possible to detect a total of 10 denaturants/additives in extremely low concentrations without any contact with the sample; discriminate between and within multiple well-known Scotch whisky brands, and detect methanol concentrations well below the maximum human tolerable level.

Quantitative Online Liquid Chromatography-Surface-Enhanced Raman Scattering (LC-SERS) of Methotrexate and its Major Metabolites.

The application of Raman spectroscopy as a detection method coupled with liquid chromatography (LC) has recently attracted considerable interest, although this has currently been limited to isocratic elution. The combination of LC with rapidly advancing Raman techniques, such as surface-enhanced Raman scattering (SERS), allows for rapid separation, identification and quantification, leading to quantitative discrimination of closely eluting analytes. This study has demonstrated the utility of SERS in conjunction with reversed-phase liquid chromatography (RP-LC), for the detection and quantification of the therapeutically relevant drug molecule methotrexate (MTX) and its metabolites 7-hydroxy methotrexate (7-OH MTX) and 2,4-diamino-N(10)-methylpteroic acid (DAMPA) in pure solutions and mixtures, including spikes into human urine from a healthy individual and patients under medication. While the RP-LC analysis developed employed gradient elution, where the chemical constituents of the mobile phase were modified stepwise during analysis, this did not overtly interfere with the SERS signals. In addition, the practicability and clinical utility of this approach has also been demonstrated using authentic patients' urine samples. Here, the identification of MTX, 7-OH MTX and DAMPA are based on their unique SERS spectra, providing limits of detection of 2.36, 1.84, and 3.26 µM respectively. Although these analytes are amenable to LC and LC-MS detection an additional major benefit of the SERS approach is its applicability toward the detection of analytes that do not show UV absorption or are not ionised for mass spectrometry (MS)-based detection. The results of this study clearly demonstrate the potential application of online LC-SERS analysis for real-time high-throughput detection of drugs and their related metabolites in human biofluids.

Quantitative detection of codeine in human plasma using surface-enhanced Raman scattering via adaptation of the isotopic labelling principle.

In this study surface enhanced Raman scattering (SERS) combined with the isotopic labelling (IL) principle has been used for the quantification of codeine spiked into both water and human plasma. Multivariate statistical approaches were employed for the analysis of these SERS spectral data, particularly partial least squares regression (PLSR) which was used to generate models using the full SERS spectral data for quantification of codeine with, and without, an internal isotopic labelled standard. The PLSR models provided accurate codeine quantification in water and human plasma with high prediction accuracy (Q2). In addition, the employment of codeine-d6 as the internal standard further improved the accuracy of the model, by increasing the Q2 from 0.89 to 0.94 and decreasing the low root-mean-square error of predictions (RMSEP) from 11.36 to 8.44. Using the peak area at 1281 cm-1 assigned to C-N stretching, C-H wagging and ring breathing, the limit of detection was calculated in both water and human plasma to be 0.7 µM (209.55 ng mL-1) and 1.39 µM (416.12 ng mL-1), respectively. Due to a lack of definitive codeine vibrational assignments, density functional theory (DFT) calculations have also been used to assign the spectral bands with their corresponding vibrational modes, which were in excellent agreement with our experimental Raman and SERS findings. Thus, we have successfully demonstrated the application of SERS with isotope labelling for the absolute quantification of codeine in human plasma for the first time with a high degree of accuracy and reproducibility. The use of the IL principle which employs an isotopolog (that is to say, a molecule which is only different by the substitution of atoms by isotopes) improves quantification and reproducibility because the competition of the codeine and codeine-d6 for the metal surface used for SERS is equal and this will offset any difference in the number of particles under analysis or any fluctuations in laser fluence. It is our belief that this may open up new exciting opportunities for testing SERS in real-world samples and applications which would be an area of potential future studies.

Rapid, Accurate, and Quantitative Detection of Propranolol in Multiple Human Biofluids via Surface-Enhanced Raman Scattering.

There has been an increasing demand for rapid and sensitive techniques for the identification and quantification of pharmaceutical compounds in human biofluids during the past few decades, and surface-enhanced Raman scattering (SERS) is one of a number of physicochemical techniques with the potential to meet these demands. In this study we have developed a SERS-based analytical approach for the assessment of human biofluids in combination with chemometrics. This novel approach has enabled the detection and quantification of the ß-blocker propranolol spiked into human serum, plasma, and urine at physiologically relevant concentrations. A range of multivariate statistical analysis techniques, including principal component analysis (PCA), principal component-discriminant function analysis (PC-DFA) and partial least-squares regression (PLSR) were employed to investigate the relationship between the full SERS spectral data and the level of propranolol. The SERS spectra when combined with PCA and PC-DFA demonstrated clear differentiation of neat biofluids and biofluids spiked with varying concentrations of propranolol ranging from 0 to 120 µM, and clear trends in ordination scores space could be correlated with the level of propranolol. Since PCA and PC-DFA are categorical classifiers, PLSR modeling was subsequently used to provide accurate propranolol quantification within all biofluids with high prediction accuracy (expressed as root-mean-square error of predictions) of 0.58, 9.68, and 1.69 for serum, plasma, and urine respectively, and these models also had excellent linearity for the training and test sets between 0 and 120 µM. The limit of detection as calculated from the area under the naphthalene ring vibration from propranolol was 133.1 ng/mL (0.45 µM), 156.8 ng/mL (0.53 µM), and 168.6 ng/mL (0.57 µM) for serum, plasma, and urine, respectively. This result shows a consistent signal irrespective of biofluid, and all are well within the expected physiological level of this drug during therapy. The results of this study demonstrate the potential of SERS application as a diagnostic screening method, following further validation and optimization to improve detection of pharmaceutical compounds and quantification in human biofluids, which may open up new exciting opportunities for future use in various biomedical and forensic applications.

Rapid, accurate, and comparative differentiation of clinically and industrially relevant microorganisms via multiple vibrational spectroscopic fingerprinting.

Despite the fact that various microorganisms (e.g., bacteria, fungi, viruses, etc.) have been linked with infectious diseases, their crucial role towards sustaining life on Earth is undeniable. The huge biodiversity, combined with the wide range of biochemical capabilities of these organisms, have always been the driving force behind their large number of current, and, as of yet, undiscovered future applications. The presence of such diversity could be said to expedite the need for the development of rapid, accurate and sensitive techniques which allow for the detection, differentiation, identification and classification of such organisms. In this study, we employed Fourier transform infrared (FT-IR), Raman, and surface enhanced Raman scattering (SERS) spectroscopies, as molecular whole-organism fingerprinting techniques, combined with multivariate statistical analysis approaches for the classification of a range of industrial, environmental or clinically relevant bacteria (P. aeruginosa, P. putida, E. coli, E. faecium, S. lividans, B. subtilis, B. cereus) and yeast (S. cerevisiae). Principal components-discriminant function analysis (PC-DFA) scores plots of the spectral data collected from all three techniques allowed for the clear differentiation of all the samples down to sub-species level. The partial least squares-discriminant analysis (PLS-DA) models generated using the SERS spectral data displayed lower accuracy (74.9%) when compared to those obtained from conventional Raman (97.8%) and FT-IR (96.2%) analyses. In addition, whilst background fluorescence was detected in Raman spectra for S. cerevisiae, this fluorescence was quenched when applying SERS to the same species, and conversely SERS appeared to introduce strong fluorescence when analysing P. putida. It is also worth noting that FT-IR analysis provided spectral data of high quality and reproducibility for the whole sample set, suggesting its applicability to a wider range of samples, and perhaps the most suitable for the analysis of mixed cultures in future studies. Furthermore, our results suggest that while each of these spectroscopic approaches may favour different organisms (sample types), when combined, they would provide complementary and more in-depth knowledge (structural and/or metabolic state) of biological systems. To the best of our knowledge, this is the first time that such a comparative and combined spectroscopic study (using FT-IR, Raman and SERS) has been carried out on microbial samples.

Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry.

INTRODUCTION: An exceptionally interesting stress response of Pseudomonas putida strains to toxic substances is the induction of efflux pumps that remove toxic chemical substances from the bacterial cell out to the external environment. To exploit these microorganisms to their full potential a deeper understanding of the interactions between the bacteria and organic solvents is required. Thus, this study focuses on investigation of metabolic changes in P. putida upon exposure to toluene. OBJECTIVE: Investigate observable metabolic alterations during interactions of three strains of P. putida (DOT-T1E, and its mutants DOT-T1E-PS28 and DOT-T1E-18) with the aromatic hydrocarbon toluene. METHODS: The growth profiles were measured by taking optical density (OD) measurement at 660 nm (OD660) at various time points during incubation. For fingerprinting analysis, Fourier-transform infrared (FT-IR) spectroscopy was used to investigate any phenotypic changes resulting from exposure to toluene. Metabolic profiling analysis was performed using gas chromatography-mass spectrometry (GC-MS). Principal component-discriminant function analysis (PC-DFA) was applied to the FT-IR data while multiblock principal component analysis (MB-PCA) and N-way analysis of variance (N-way ANOVA) were applied to the GC-MS data. RESULTS: The growth profiles demonstrated the effect of toluene on bacterial cultures and the results suggest that the mutant P. putida DOT-T1E-18 was more sensitive (significantly affected) to toluene compared to the other two strains. PC-DFA on FT-IR data demonstrated the differentiation between different conditions of toluene on bacterial cells, which indicated phenotypic changes associated with the presence of the solvent within the cell. Fifteen metabolites associated with this phenotypic change, in P. putida due to exposure to solvent, were from central metabolic pathways. Investigation of MB-PCA loading plots and N-way ANOVA for condition | strain × time blocking (dosage of toluene) suggested ornithine as the most significant compound that increased upon solvent exposure. CONCLUSION: The combination of metabolic fingerprinting and profiling with suitable multivariate analysis revealed some interesting leads for understanding the mechanism of Pseudomonas strains response to organic solvent exposure.

Metabolomics Analysis Reveals the Participation of Efflux Pumps and Ornithine in the Response of Pseudomonas putida DOT-T1E Cells to Challenge with Propranolol.

Efflux pumps are critically important membrane components that play a crucial role in strain tolerance in Pseudomonas putida to antibiotics and aromatic hydrocarbons that result in these toxicants being expelled from the bacteria. Here, the effect of propranolol on P. putida was examined by sudden addition of 0.2, 0.4 and 0.6 mg mL-1 of this ß-blocker to several strains of P. putida, including the wild type DOT-T1E and the efflux pump knockout mutants DOT-T1E-PS28 and DOT-T1E-18. Bacterial viability measurements reveal that the efflux pump TtgABC plays a more important role than the TtgGHI pump in strain tolerance to propranolol. Mid-infrared (MIR) spectroscopy was then used as a rapid, high-throughput screening tool to investigate any phenotypic changes resulting from exposure to varying levels of propranolol. Multivariate statistical analysis of these MIR data revealed gradient trends in resultant ordination scores plots, which were related to the concentration of propranolol. MIR illustrated phenotypic changes associated with the presence of this drug within the cell that could be assigned to significant changes that occurred within the bacterial protein components. To complement this phenotypic fingerprinting approach metabolic profiling was performed using gas chromatography mass spectrometry (GC-MS) to identify metabolites of interest during the growth of bacteria following toxic perturbation with the same concentration levels of propranolol. Metabolic profiling revealed that ornithine, which was only produced by P. putida cells in the presence of propranolol, presents itself as a major metabolic feature that has important functions in propranolol stress tolerance mechanisms within this highly significant and environmentally relevant species of bacteria.

Biochemical Analyses of Sorghum Varieties Reveal Differential Responses to Drought.

We have examined the biochemical responses of two sorghum cultivars of differing drought tolerance, Samsorg 17 (more drought tolerant) and Samsorg 40 (less drought tolerant), to sustained drought. Plants were exposed to different degrees of drought and then maintained at that level for five days. Responses were examined in terms of metabolic changes and the expression of drought induced proteins-Heat Shock Proteins (HSPs) and dehydrins (DHNs). Generalised phenotypic changes were studied using Fourier transform infrared (FT-IR) Spectroscopy and non-targeted Gas Chromatography Mass Spectrometry (GC-MS) was employed to detect changes in metabolites, while changes in protein expression were examined using Western blot analysis. Different response profiles of metabolites, HSPs and DHNs were observed in the two cultivars. Metabolic changes involved variation in amino acids, polysaccharides and their derivatives. A total of 188 compounds, with 142 known metabolites and 46 unknown small molecules, were detected in the two sorghum varieties. Under water deficit conditions, Samsorg 17 accumulated sugars and sugar alcohols, while in Samsorg 40 amino acids increased in concentration. This study suggest that the two Sorghum varieties adopt distinct approaches in response to drought, with Samsorg 17 being better able to maintain leaf function under severe drought conditions.

Metabolic Fingerprinting of Pseudomonas putida DOT-T1E Strains: Understanding the Influence of Divalent Cations in Adaptation Mechanisms Following Exposure to Toluene.

Pseudomonas putida strains can adapt and overcome the activity of toxic organic solvents by the employment of several resistant mechanisms including efflux pumps and modification to lipopolysaccharides (LPS) in their membranes. Divalent cations such as magnesium and calcium play a crucial role in the development of solvent tolerance in bacterial cells. Here, we have used Fourier transform infrared (FT-IR) spectroscopy directly on cells (metabolic fingerprinting) to monitor bacterial response to the absence and presence of toluene, along with the influence of divalent cations present in the growth media. Multivariate analysis of the data using principal component-discriminant function analysis (PC-DFA) showed trends in scores plots, illustrating phenotypic alterations related to the effect of Mg(2+), Ca(2+) and toluene on cultures. Inspection of PC-DFA loadings plots revealed that several IR spectral regions including lipids, proteins and polysaccharides contribute to the separation in PC-DFA space, thereby indicating large phenotypic response to toluene and these cations. Finally, the saturated fatty acid ratio from the FT-IR spectra showed that upon toluene exposure, the saturated fatty acid ratio was reduced, while it increased in the presence of divalent cations. This study clearly demonstrates that the combination of metabolic fingerprinting with appropriate chemometric analysis can result in practicable knowledge on the responses of important environmental bacteria to external stress from pollutants such as highly toxic organic solvents, and indicates that these changes are manifest in the bacterial cell membrane. Finally, we demonstrate that divalent cations improve solvent tolerance in P. putida DOT­T1E strains.

Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork.

Adulteration of high quality food products with sub-standard and cheaper grades is a world-wide problem taxing the global economy. Currently, many traditional tests suffer from poor specificity, highly complex outputs and a lack of high-throughput processing. Metabolomics has been successfully used as an accurate discriminatory technique in a number of applications including microbiology, cancer research and environmental studies and certain types of food fraud. In this study, we have developed metabolomics as a technique to assess the adulteration of meat as an improvement on current methods. Different grades of beef mince and pork mince, purchased from a national retail outlet were combined in a number of percentage ratios and analysed using GC-MS and UHPLC-MS. These techniques were chosen because GC-MS enables investigations of metabolites involved in primary metabolism whilst UHPLC-MS using reversed phase chromatography provides information on lipophilic species. With the application of chemometrics and statistical analyses, a panel of differential metabolites were found for identification of each of the two meat types. Additionally, correlation was observed between metabolite content and percentage of fat declared on meat products' labelling.

Chicken, beams, and Campylobacter: rapid differentiation of foodborne bacteria via vibrational spectroscopy and MALDI-mass spectrometry.

Campylobacter species are one of the main causes of food poisoning worldwide. Despite the availability of established culturing and molecular techniques, due to the fastidious nature of these microorganisms, simultaneous detection and species differentiation still remains challenging. This study focused on the differentiation of eleven Campylobacter strains from six species, using Fourier transform infrared (FT-IR) and Raman spectroscopies, together with matrix-assisted laser desorption ionisation-time of flight-mass spectrometry (MALDI-TOF-MS), as physicochemical approaches for generating biochemical fingerprints. Cluster analysis of data from each of the three analytical approaches provided clear differentiation of each Campylobacter species, which was generally in agreement with a phylogenetic tree based on 16S rRNA gene sequences. Notably, although C. fetus subspecies fetus and venerealis are phylogenetically very closely related, using FT-IR and MALDI-TOF-MS data these subspecies were readily differentiated based on differences in the lipid (2920 and 2851 cm(-1)) and fingerprint regions (1500-500 cm(-1)) of the FT-IR spectra, and the 500-2000 m/z region of the MALDI-TOF-MS data. A finding that was further investigated with targeted lipidomics using liquid chromatography-mass spectrometry (LC-MS). Our results demonstrate that such metabolomics approaches combined with molecular biology techniques may provide critical information and knowledge related to the risk factors, virulence, and understanding of the distribution and transmission routes associated with different strains of foodborne Campylobacter spp.

Metabolomics investigation of recombinant mTNFα production in Streptomyces lividans.

BACKGROUND: Whilst undergoing differentiation, Streptomyces produce a large quantity of hydrolytic enzymes and secondary metabolites, and it is this very ability that has focussed increasing interest on the use of these bacteria as hosts for the production of various heterologous proteins. However, within this genus, the exploration and understanding of the metabolic burden associated with such bio-products has only just begun. In this study our overall aim was to apply metabolomics approaches as tools to get a glimpse of the metabolic alterations within S. lividans TK24 when this industrially relevant microbe is producing recombinant murine tumour necrosis factor alpha (mTNFα), in comparison to wild type and empty (non-recombinant protein containing) plasmid-carrying strains as controls. RESULTS: Whilst growth profiles of all strains demonstrated comparable trends, principal component-discriminant function analysis of Fourier transform infrared (FT-IR) spectral data, showed clear separation of wild type from empty plasmid and mTNFα-producing strains, throughout the time course of incubation. Analysis of intra- and extra-cellular metabolic profiles using gas chromatography-mass spectrometry (GC-MS) displayed similar trends to the FT-IR data. Although the strain carrying the empty plasmid demonstrated metabolic changes due to the maintenance of the plasmid, the metabolic behaviour of the recombinant mTNFα-producing strain appeared to be the most significantly affected. GC-MS results also demonstrated a significant overflow of several organic acids (pyruvate, 2-ketoglutarate and propanoate) and sugars (xylitol, mannose and fructose) in the mTNFα-producing strain. CONCLUSION: The results obtained in this study have clearly demonstrated the metabolic impacts of producing mTNFα in S. lividans TK24, while displaying profound metabolic effects of harbouring the empty PIJ486 plasmid. In addition, the level of mTNFα produced in this study, further highlights the key role of media composition towards the efficiency of a bioprocess and metabolic behaviour of the host cells, which directly influences the yield of the recombinant product.

A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.

Metabolic Profiling of Geobacter sulfurreducens during Industrial Bioprocess Scale-Up.

During the industrial scale-up of bioprocesses it is important to establish that the biological system has not changed significantly when moving from small laboratory-scale shake flasks or culturing bottles to an industrially relevant production level. Therefore, during upscaling of biomass production for a range of metal transformations, including the production of biogenic magnetite nanoparticles by Geobacter sulfurreducens, from 100-ml bench-scale to 5-liter fermentors, we applied Fourier transform infrared (FTIR) spectroscopy as a metabolic fingerprinting approach followed by the analysis of bacterial cell extracts by gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. FTIR results clearly differentiated between the phenotypic changes associated with different growth phases as well as the two culturing conditions. Furthermore, the clustering patterns displayed by multivariate analysis were in agreement with the turbidimetric measurements, which displayed an extended lag phase for cells grown in a 5-liter bioreactor (24 h) compared to those grown in 100-ml serum bottles (6 h). GC-MS analysis of the cell extracts demonstrated an overall accumulation of fumarate during the lag phase under both culturing conditions, coinciding with the detected concentrations of oxaloacetate, pyruvate, nicotinamide, and glycerol-3-phosphate being at their lowest levels compared to other growth phases. These metabolites were overlaid onto a metabolic network of G. sulfurreducens, and taking into account the levels of these metabolites throughout the fermentation process, the limited availability of oxaloacetate and nicotinamide would seem to be the main metabolic bottleneck resulting from this scale-up process. Additional metabolite-feeding experiments were carried out to validate the above hypothesis. Nicotinamide supplementation (1 mM) did not display any significant effects on the lag phase of G. sulfurreducens cells grown in the 100-ml serum bottles. However, it significantly improved the growth behavior of cells grown in the 5-liter bioreactor by reducing the lag phase from 24 h to 6 h, while providing higher yield than in the 100-ml serum bottles.