Untargeted characterisation of dissolved organic matter contributions to rivers from anthropogenic point sources using direct-infusion and high-performance liquid chromatography/Orbitrap mass spectrometry.

RATIONALE: Anthropogenic organic inputs to freshwaters can exert detrimental effects on aquatic ecosystems, raising growing concern for both environmental conservation and water security. Current regulation by the EU water framework directive (European Union, 2000/60/EC) relates to organic pollution by monitoring selected micropollutants; however, aquatic ecosystem responses require a comprehensive understanding of dissolved organic matter (DOM) composition. The introduction of high-resolution mass spectrometry (HRMS) is set to greatly increase our understanding of the composition of DOM of both natural and anthropogenic origin derived from diffuse and point sources. METHODS: DOM was extracted from riverine and treated sewage effluent using solid-phase extraction (SPE) and analysed using dissolved organic carbon analysis, direct-infusion high-resolution mass spectrometry (DI-HRMS) and high-performance liquid chromatography (HPLC)/HRMS. The data obtained were analysed using univariate and multivariate statistics to demonstrate differences in background DOM, anthropogenic inputs and in-river mixing. Compound identifications were achieved based on MS2 spectra searched against on-line databases. RESULTS: DI-HRMS spectra showed the highly complex nature of all DOM SPE extracts. Classification and visualisation of extracts containing many thousands of individual compounds were achieved using principal component analysis (PCA) and hierarchical cluster analysis. Kruskal-Wallis analyses highlighted significant discriminating ions originating from the sewage treatment works for more in-depth investigation by HPLC/HRMS. The generation of MS2 spectra in HPLC/HRMS provided the basis for identification of anthropogenic compounds including; pharmaceuticals, illicit drugs, metabolites and oligomers, although many thousands of compounds remain unidentified. CONCLUSIONS: This new approach enables comprehensive analysis of DOM in extracts without any preconceived ideas of the compounds which may be present. This approach has the potential to be used as a high throughput, qualitative, screening method to determine if the composition of point sources differs from that of the receiving water bodies, providing a new approach to the identification of hitherto unrecognised organic contribution to water bodies.

From waste to food: Optimising the breakdown of oil palm waste to provide substrate for insects farmed as animal feed.

Waste biomass from the palm oil industry is currently burned as a means of disposal and solutions are required to reduce the environmental impact. Whilst some waste biomass can be recycled to provide green energy such as biogas, this investigation aimed to optimise experimental conditions for recycling palm waste into substrate for insects, farmed as a sustainable high-protein animal feed. NMR spectroscopy and LC-HRMS were used to analyse the composition of palm empty fruit bunches (EFB) under experimental conditions optimised to produce nutritious substrate rather than biogas. Statistical pattern recognition techniques were used to investigate differences in composition for various combinations of pre-processing and anaerobic digestion (AD) methods. Pre-processing methods included steaming, pressure cooking, composting, microwaving, and breaking down the EFB using ionic liquids. AD conditions which were modified in combination with pre-processing methods were ratios of EFB:digestate and pH. Results show that the selection of pre-processing method affects the breakdown of the palm waste and subsequently the substrate composition and biogas production. Although large-scale insect feeding trials will be required to determine nutritional content, we found that conditions can be optimised to recycle palm waste for the production of substrate for insect rearing. Pre-processing EFB using ionic liquid before AD at pH6 with a 2:1 digestate:EFB ratio were found to be the best combination of experimental conditions.

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways.

MOTIVATION: Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. RESULTS: We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites.

Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value.

Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula.

INTRODUCTION: Nitrogen-fixing legumes are invaluable crops, but are sensitive to physical and biological stresses. Whilst drought and infection from the soil-borne pathogen Fusarium oxysporum have been studied individually, their combined effects have not been widely investigated. OBJECTIVES: We aimed to determine the effect of combined stress using methods usually associated with transcriptomics to detect metabolic differences between treatment groups that could not be identified by more traditional means, such as principal component analysis and partial least squares discriminant analysis. METHODS: Liquid chromatography-high resolution mass spectrometry data from the root and leaves of model legume Medicago truncatula were analysed using Gaussian Process 2-Sample Test, k-means cluster analysis and temporal clustering by affinity propagation. RESULTS: Metabolic differences were detected: we identified known stress markers, including changes in concentration for sucrose and citric acid, and showed that combined stress can exacerbate the effect of drought. Changes in roots were found to be smaller than those in leaves, but differences due to Fusarium infection were identified. The transfer of sucrose from leaves to roots can be seen in the time series using transcriptomic techniques with the metabolomics time series. Other metabolite concentrations that change as a result of treatment include phosphoric acid, malic acid and tetrahydroxychalcone. CONCLUSIONS: Probing metabolomic data with transcriptomic tools provides new insights and could help to identify resilient plant varieties, thereby increasing future crop yield and improving food security.

Improved Drought Stress Response in Alfalfa Plants Nodulated by an IAA Over-producing Rhizobium Strain.

The drought-stress response in plant involves the cross-talk between abscisic acid (ABA) and other phytohormones, such as jasmonates and ethylene. The auxin indole-3-acetic acid (IAA) plays an integral part in plant adaptation to drought stress. Investigation was made to see how the main auxin IAA interacted with other plant hormones under water stress, applied through two different growth conditions (solid and hydroponic). Medicago sativa plants nodulated by the Ensifer meliloti wild type 1021 (Ms-1021) and its IAA-overproducing RD64 derivative strains (Ms-RD64) were subjected to drought stress, comparing their response. When the expression of nifH gene and the activity of the nitrogenase enzyme were measured after stress treatments, Ms-RD64 plants recorded a significantly weaker damage. These results were correlated with a lower biomass reduction, and a higher Rubisco protein level measured for the Ms-RD64-stressed plants as compared to the Ms-1021-stressed ones. It has been verified that the stress response observed for Ms-RD64-stressed plants was related to the production of greater amount of low-molecular-weight osmolytes, such as proline and pinitol, measured in these plants. For the Ms-RD64 plants the immunoblotting analysis of thylakoid membrane proteins showed that some of the photosystem proteins increased after the stress. An increased non-photochemical quenching after the stress was also observed for these plants. The reduced wilting signs observed for these plants were also connected to the significant down-regulation of the MtAA03 gene involved in the ABA biosynthesis, and with the unchanged expression of the two genes (Mt-2g006330 and Mt-8g095330) of ABA signaling. When the expression level of the ethylene-signaling genes was evaluated by qPCR analysis no significant alteration of the key positive regulators was recorded for Ms-RD64-stressed plants. Coherently, these plants accumulated 40% less ethylene as compared to Ms-1021-stressed ones. The results presented herein indicate that the variations in endogenous IAA levels, triggered by the overproduction of rhizobial IAA inside root nodules, positively affected drought stress response in nodulated alfalfa plants.

A batch correction method for liquid chromatography-mass spectrometry data that does not depend on quality control samples.

The need for reproducible and comparable results is of increasing importance in non-targeted metabolomic studies, especially when differences between experimental groups are small. Liquid chromatography-mass spectrometry spectra are often acquired batch-wise so that necessary calibrations and cleaning of the instrument can take place. However this may introduce further sources of variation, such as differences in the conditions under which the acquisition of individual batches is performed. Quality control (QC) samples are frequently employed as a means of both judging and correcting this variation. Here we show that the use of QC samples can lead to problems. The non-linearity of the response can result in substantial differences between the recorded intensities of the QCs and experimental samples, making the required adjustment difficult to predict. Furthermore, changes in the response profile between one QC interspersion and the next cannot be accounted for and QC based correction can actually exacerbate the problems by introducing artificial differences. "Background correction" methods utilise all experimental samples to estimate the variation over time rather than relying on the QC samples alone. We compare non-QC correction methods with standard QC correction and demonstrate their success in reducing differences between replicate samples and their potential to highlight differences between experimental groups previously hidden by instrumental variation.

Identification of novel peptides for horse meat speciation in highly processed foodstuffs.

There is a need for robust analytical methods to support enforcement of food labelling legislation. Proteomics is emerging as a complementary methodology to existing tools such as DNA and antibody-based techniques. Here we describe the development of a proteomics strategy for the determination of meat species in highly processed foods. A database of specific peptides for nine relevant animal species was used to enable semi-targeted species determination. This principle was tested for horse meat speciation, and a range of horse-specific peptides were identified as heat stable marker peptides for the detection of low levels of horse meat in mixtures with other species.

Pet food safety: a shared concern.

The safety of the food supply is a subject of intense interest to consumers, particularly as a result of large-scale outbreaks that involve hundreds and sometimes thousands of consumers. During the last decade, this concern about food safety has expanded to include the diets of companion animals as a result of several incidences of chemical toxicities and infectious disease transmission. This has led to increased research into the causes and controls for these hazards for both companion animals and their owners. The following summary provides an introduction to the issues, challenges and new tools being developed to ensure that commercial pet foods are both nutritious and safe.

Profiling Turkish honeys to determine authenticity using physical and chemical characteristics.

Seventy authentic honey samples of 9 different floral types (rhododendron, chestnut, honeydew, Anzer (thymus spp.), eucalyptus, gossypium, citrus, sunflower, and multifloral) from 15 different geographical regions of Turkey were analyzed for their chemical composition and for indicators of botanical and geographical origin. The profiles of free amino acids, oligosaccharides, and volatile components together with water activity were determined to characterize chemical composition. The microscopic analysis of honey sediment (mellissopalynology) was carried out to identify and count the pollen to provide qualitative indicators to confirm botanical origin. Statistical analysis was undertaken using a bespoke toolbox for Matlab called Metabolab. Discriminant analysis was undertaken using partial least-squares (PLS) regression followed by linear discriminant analysis (LDA). Four data models were constructed and validated. Model 1 used 51 variables to predict the floral origin of the honey samples. This model was also used to identify the top 5 variable important of projection (VIP) scores, selecting those variables that most significantly affected the PLS-LDA calculation. These data related to the phthalic acid, 2-methylheptanoic acid, raffinose, maltose, and sucrose. Data from these compounds were remodeled using PLS-LDA. Model 2 used only the volatiles data, model 3 the sugars data, and model 4 the amino acids data. The combined data set allowed the floral origin of Turkish honey to be accurately predicted and thus provides a useful tool for authentication purposes. However, using variable selection techniques a smaller subset of analytes have been identified that have the capability of classifying Turkish honey according to floral type with a similar level of accuracy.

Application of cryoprobe 1H nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of corsican honey.

Proton nuclear magnetic resonance spectroscopy ((1)H NMR) and multivariate analysis techniques have been used to classify honey into two groups by geographical origin. Honey from Corsica (Miel de Corse) was used as an example of a protected designation of origin product. Mathematical models were constructed to determine the feasibility of distinguishing between honey from Corsica and that from other geographical locations in Europe, using (1)H NMR spectroscopy. Honey from 10 different regions within five countries was analyzed. (1)H NMR spectra were used as input variables for projection to latent structures (PLS) followed by linear discriminant analysis (LDA) and genetic programming (GP). Models were generated using three methods, PLS-LDA, two-stage GP, and a combination of PLS and GP (PLS-GP). The PLS-GP model used variables selected by PLS for subsequent GP calculations. All models were generated using Venetian blind cross-validation. Overall classification rates for the discrimination of Corsican and non-Corsican honey of 75.8, 94.5, and 96.2% were determined using PLS-LDA, two-stage GP, and PLS-GP, respectively. The variables utilized by PLS-GP were related to their (1)H NMR chemical shifts, and this led to the identification of trigonelline in honey for the first time.

Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics.

An efficient method for detecting malicious and accidental contamination of foods has been developed using a combined 1H nuclear magnetic resonance (NMR) and chemometrics approach. The method has been demonstrated using a commercially available carbonated soft drink, as being capable of identifying atypical products and to identify contaminant resonances. Soft-independent modelling of class analogy (SIMCA) was used to compare 1H NMR profiles of genuine products (obtained from the manufacturer) against retail products spiked in the laboratory with impurities. The benefits of using feature selection for extracting contaminant NMR frequencies were also assessed. Using example impurities (paraquat, p-cresol and glyphosate) NMR spectra were analysed using multivariate methods resulting in detection limits of approximately 0.075, 0.2, and 0.06 mM for p-cresol, paraquat and glyphosate, respectively. These detection limits are shown to be approximately 100-fold lower than the minimum lethal dose for paraquat. The methodology presented here is used to assess the composition of complex matrices for the presence of contaminating molecules without a priori knowledge of the nature of potential contaminants. The ability to detect if a sample does not fit into the expected profile without recourse to multiple targeted analyses is a valuable tool for incident detection and forensic applications.

Removal of t(1) noise from metabolomic 2D (1)H-(13)C HSQC NMR spectra by Correlated Trace Denoising.

The presence of t(1) noise artefacts in 2D phase-cycled Heteronuclear Single Quantum Coherence (HSQC) spectra constrains the use of this experiment despite its superior sensitivity. This paper proposes a new processing algorithm, working in the frequency-domain, for reducing t(1) noise. The algorithm has been developed for use in contexts, such as metabolomic studies, where existing denoising techniques cannot always be applied. Two test cases are presented that show the algorithm to be effective in improving the SNR of peaks embedded within t(1) noise by a factor of more than 2, while retaining the intensity and shape of genuine peaks.

Applications of metabolomics in agriculture.

Biological systems are exceedingly complex. The unraveling of the genome in plants and humans revealed fewer than the anticipated number of genes. Therefore, other processes such as the regulation of gene expression, the action of gene products, and the metabolic networks resulting from catalytic proteins must make fundamental contributions to the remarkable diversity inherent in living systems. Metabolomics is a relatively new approach aimed at improved understanding of these metabolic networks and the subsequent biochemical composition of plants and other biological organisms. Analytical tools within metabolomics including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy can profile the impact of time, stress, nutritional status, and environmental perturbation on hundreds of metabolites simultaneously resulting in massive, complex data sets. This information, in combination with transcriptomics and proteomics, has the potential to generate a more complete picture of the composition of food and feed products, to optimize crop trait development, and to enhance diet and health. Selected presentations from an American Chemical Society symposium held in March 2005 have been assembled to highlight the emerging application of metabolomics in agriculture.

The development of cryoprobe nuclear magnetic resonance spectroscopy for the rapid detection of organic contaminants in potable water.

The detection of trace levels of a range of organic contaminants (including pesticides, toxins and an explosive) in potable water, using cryoprobe NMR spectroscopy with limited sample preparation and rapid acquisition times, is described. Emphasis is placed on the applicability of NMR spectroscopy for use in emergency scenarios as the unbiased nature of the technique facilitates the detection and characterization of unknown compounds at levels as low as 50 microg L(-1).

Parahydrogen derived illumination of pyridine based coordination products obtained from reactions involving rhodium phosphine complexes.

The reactions of RhCl(PBz3)3 with H2 and pyridine or 4-methylpyridine yield RhCl(H)2(PBz3)2(py) and RhCl(H)2(PBz3)2(4-Me-py), respectively. These species undergo hydride site exchange via the loss of the pyridyl donor and formation of RhCl(H)2(PBz3)2 which contains equivalent hydride ligands; for the py system the activation free energy, deltaG++300, is 57.4 +/- 0.1 kJ mol(-1) while for 4-Me-py the value is 59.6 +/- 0.3 kJ mol(-1). These products only showed parahydrogen enhancement in the corresponding hydride resonances when a sacrificial substrate was added to promote hydrogen cycling. When RhCl(PPh3)3 was used as the precursor similar observations were made, while when RhCl(PCy3)2(C2H4) was examined, H2 addition led to the formation of the binuclear complex (H)2Rh(PCy3)2(micro-Cl)2Rh(H)2(PCy3)2 which was differentiated from RhCl(H)2(PCy3)2 on the basis of the similarity in diffusion coefficient (5.5 x 10(-9) m2 s(-1)) to that of (H)2Rh(PPh3)2(micro-Cl)2Rh(PPh3)2 (5.3 x 10(-9) m2 s(-1)). The detection of RhCl(H)2(PCy3)2(py) was facilitated when pyridine was added to a solution of RhCl(PCy3)2(C2H4) before the introduction of H2. During these reactions trace amounts of the double substitution products, RhCl(H)2(phosphine)(py)2, were also detected.

NMR profiling of transgenic peas.

A high throughput proton nuclear magnetic resonance spectroscopy method for the metabolite fingerprinting of plants was applied to genetically modified peas (Pisum sativum) to determine whether biochemical changes, so called 'unintended effects', beyond those intended by incorporation of a transgene, were detectable. Multivariate analysis of 1H NMR (nuclear magnetic resonance) spectra obtained from uniformly grown glasshouse plants revealed differences between the transgenic and control group that exceeded the natural variation of the plants. When a larger data set of six related transgenic lines was analysed, including a null segregant in addition to the wild-type control, multivariate analysis showed that the distribution of metabolites in the transgenics was different from that of the null segregant. However, the profile obtained from the wild-type material was diverse in comparison with both the transgenics and the null segregant, suggesting that the primary cause of the observed differences was that the transformation process selects for a subset of individuals able to undergo the transformation and selection procedures, and that their descendants have a restricted variation in metabolite profile, rather than that the presence of the transgene itself generates these differences.

Purity assessment of crystalline zearalenone.

Commercially available solid zearalenone (ZON) to be used as a certified liquid calibrant (BCR-699) in a project funded by the European Commission within the Standard Measurement and Testing program was characterized and its purity determined. The degree of purity of the ZON was examined by UV spectrophotometer, liquid chromatography (LC) with diode array and fluorescence detection, 1H and 13C-NMR spectrometry, LC-mass spectrometry (LC/MS/MS), ion chromatography (IC), and differential scanning calorimetry (DSC). The diagrams obtained from DSC analysis and the UV spectrum showed no detectable impurities. Likewise, no impurities were observed by LC analysis with both diode array and fluorescence detection. IC determination revealed negligible contamination of ZON with chloride of 0.020 +/- 0.005% and nitrate of 0.016 +/- 0.006%. Zearalanone (ZAN) was identified as one of 2 minor (0.2%) impurities by LC/MS/MS. The 1H-NMR measurements revealed an additional peak, which has not been previously reported in the literature. It could be identified as part of the ZON spectrum as the signal arising from the phenolic proton attached to C4'. The manufacturer states an additional contamination with 0.2% methylene chloride, which could be confirmed to an extent of 0.1% by 1H-NMR. Minor impurities, whose structures remain unknown, were discovered at 3.5 and < 1 ppm. Total percentage of impurities based on NMR measurement was estimated not to exceed 1%. A purity of 99.5% with a tolerance of +/- 0.5% was finally attributed to the ZON studied in this project.

Multiple conformations of the proline-rich protein/epigallocatechin gallate complex determined by time-averaged nuclear Overhauser effects.

The structure of the complex between the heptapeptide Gln-Gly-Arg-Pro-Pro-Gln-Gly and the polyphenol (-)-epigallocatechin gallate (EGCG) has been determined using time-averaged nuclear Overhauser effects. Effective parameters for the force constant and time constant have been derived, allowing rapid and efficient calculation of structures that satisfy the input restraints. By using multiple start conformations, it is shown that conformational space is covered adequately and that the complex exists in one major conformation, in which the A ring of the EGCG is positioned over Pro5 and the D ring is over Pro4, with the B ring frequently close to the arginine side chain. Alternative conformations are also found, in which the prolines are almost always both involved in stacking interactions, with a strong preference for Pro4 to be involved. The structures are consistent with previous models for the interaction and suggest how precipitation of the complex could occur, which leads to the oral phenomenon of astringency. The method has promise as a general way of docking ligands onto receptors.