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.

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.

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.

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.

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.

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.

Application of (1)h NMR and multivariate statistics for screening complex mixtures: quality control and authenticity of instant coffee.

Principal components analysis (PCA) followed by linear discriminant analysis (LDA) of the nuclear magnetic resonance (NMR) spectra from 98 instant spray-dried coffees, obtained from 3 different producers, correctly attributed 99% of the samples to their manufacturer. Blind testing of the PCA model with a further 36 samples of instant coffee resulted in a 100% success rate in identifying the samples from the 3 manufacturers. Coffees from one manufacturer were also assigned into 2 groups using these techniques, and the compound 5-(hydroxymethyl)-2-furaldehyde was identified as the primary marker of differentiation.

Polyphenol/peptide binding and precipitation.

Polyphenols are largely responsible for the astringency and "mouthfeel" of tea and wine by their interactions with basic salivary proline-rich proteins. Astringency arises from precipitation of polyphenol/peptide complexes, which is an important protective mechanism in animals that consume polyphenols. This paper presents biophysical studies of the interactions between chemically defined polyphenols and peptides. It is shown that intermolecular binding is dominated by stacking of polyphenolic rings onto planar hydrophobic surfaces and is strengthened by multiple cooperative binding of polyphenolic rings. Affinities weaken at higher temperatures and are unaffected by pH between pH 3.8 and 6.0. Measurements of self-diffusion rates for peptides with increasing concentrations of polyphenol demonstrate that peptides become increasingly coated with polyphenol. When the coating is sufficiently extensive to provide cooperative polyphenol bridges, the peptide dimerizes and precipitates. Light scattering measurements and electron microscopy indicate that the insoluble particles fall into two discrete size classes of ca. 80 and 500 nm diameter. The larger particles are favored at higher temperature and pH, suggesting that the particles are in a colloidal state, with the smaller particles being stabilized by charge repulsion between particles, and that precipitation of the complexes may be a phase separation process.

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.

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).

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.