J. William Allwood

Metabolomic technologies and their application to the study of plants and plant-host interactions

Metabolomics is perhaps the ultimate level of post-genomic analysis as it can reveal changes in metabolite fluxes that are controlled by only minor changes within gene expression measured using transcriptomics and/or by analysing the proteome that elucidates post-translational control over enzyme activity. Metabolic change is a major feature of plant genetic modification and plant interactions with pathogens, pests, and their environment. In the assessment of genetically modified plant tissues, metabolomics has been used extensively to explore by-products resulting from transgene expression and scenarios of substantial equivalence. Many studies have concentrated on the physiological development of plant tissues as well as on the stress responses involved in heat shock or treatment with stress-eliciting molecules such as methyl jasmonic acid, yeast elicitor or bacterial lipopolysaccharide. Plant-host interactions represent one of the most biochemically complex and challenging scenarios that are currently being assessed by metabolomic approaches. For example, the mixtures of pathogen-colonised and non-challenged plant cells represent an extremely heterogeneous and biochemically rich sample; there is also the further complication of identifying which metabolites are derived from the plant host and which are from the interacting pathogen. This review will present an overview of the analytical instrumentation currently applied to plant metabolomic analysis, literature within the field will be reviewed paying particular regard to studies based on plant-host interactions and finally the future prospects on the metabolomic analysis of plants and plant-host interactions will be discussed.

An Introduction to Liquid Chromatography- Mass Spectrometry Instrumentation Applied in Plant Metabolomic Analyses †

Over the past decade the application of non-targeted high-throughput metabolomic analysis within the plant sciences has gained ever increasing interest and has truly established itself as a valuable tool for plant functional genomics and studies of plant biochemical composition. Whilst proton nuclear magnetic resonance ((1)H-NMR) spectroscopy is particularly appropriate for the analysis of bulk metabolites and gas chromatography mass spectrometry (GC-MS) to the analysis of volatile organic compounds (VOCs) and derivatised primary metabolites, liquid chromatography (LC)-MS is highly applicable to the analysis of a wide range of semi-polar compounds including many secondary metabolites of interest to plant researchers and nutritionists. In view of the recent developments in the separation sciences, leading to the advent of ultra high performance liquid chromatography (UHPLC) and MS based technology showing the ever improving resolution of metabolite species and precision of mass measurements (sub-ppm accuracy now being achievable), this review sets out to introduce the background and update the reader upon LC, high performance (HP)LC and UHPLC, as well as the large range of MS instruments that are being applied in current plant metabolomic studies. As well as covering the theory behind modern day LC-MS, the review also discusses the most relevant metabolomics applications for the wide range of MS instruments that are currently being applied to LC.

1H NMR, GC-EI-TOFMS, and data set correlation for fruit metabolomics: Application to spatial metabolite analysis in melon

A metabolomics approach combining (1)H NMR and gas chromatography-electrospray ionization time-of-flight mass spectrometry (GC-EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative (1)H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct (1)H NMR profiling of juice or GC-EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. (1)H NMR and GC-EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.

Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics

The application of gas chromatography–mass spectrometry (GC–MS) to the ‘global’ analysis of metabolites in complex samples (i.e. metabolomics) has now become routine. The generation of these data-rich profiles demands new strategies in data mining and standardisation of experimental and reporting aspects across laboratories. As part of the META-PHOR project’s (METAbolomics for Plants Health and OutReach: http://www.meta-phor.eu/) priorities towards robust technology development, a GC–MS ring experiment based upon three complex matrices (melon, broccoli and rice) was launched. All sample preparation, data processing, multivariate analyses and comparisons of major metabolite features followed standardised protocols, identical models of GC (Agilent 6890N) and TOF/MS (Leco Pegasus III) were also employed. In addition comprehensive GC×GC–TOF/MS was compared with 1 dimensional GC–TOF/MS. Comparisons of the paired data from the various laboratories were made with a single data processing and analysis method providing an unbiased assessment of analytical method variants and inter-laboratory reproducibility. A range of processing and statistical methods were also assessed with a single exemplary dataset revealing near equal performance between them. Further investigations of long-term reproducibility are required, though the future generation of global and valid metabolomics databases offers much promise.

Is Serum or Plasma More Appropriate for Intersubject Comparisons in Metabolomic Studies? An Assessment in Patients with Small-Cell Lung Cancer

In clinical analyses, the most appropriate biofluid should be analyzed for optimal assay performance. For biological fluids, the most readily accessible is blood, and metabolomic analyses can be performed either on plasma or serum. To determine the optimal agent for analysis, metabolic profiles of matched human serum and plasma were assessed by gas chromatography/time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography mass spectrometry (in positive and negative electrospray ionization modes). Comparison of the two metabolomes, in terms of reproducibility, discriminative ability and coverage, indicated that they offered similar analytical opportunities. An analysis of the variation between 29 small-cell lung cancer (SCLC) patients revealed that the differences between individuals are markedly similar for the two biofluids. However, significant differences between the levels of some specific metabolites were identified, as were differences in the intersubject variability of some metabolite levels. Glycerophosphocholines, erythritol, creatinine, hexadecanoic acid, and glutamine in plasma, but not in serum, were shown to correlate with life expectancy for SCLC patients, indicating the utility of metabolomic analyses in clinical prognosis and the particular utility of plasma in relation to the clinical management of SCLC.

Extensive metabolic cross-talk in melon fruit revealed by spatial and developmental combinatorial metabolomics

• Variations in tissue development and spatial composition have a major impact on the nutritional and organoleptic qualities of ripe fleshy fruit, including melon (Cucumis melo). To gain a deeper insight into the mechanisms involved in these changes, we identified key metabolites for rational food quality design. • The metabolome, volatiles and mineral elements were profiled employing an unprecedented range of complementary analytical technologies. Fruits were followed at a number of time points during the final ripening process and tissues were collected across the fruit flesh from rind to seed cavity. Approximately 2000 metabolite signatures and 15 mineral elements were determined in an assessment of temporal and spatial melon fruit development. • This study design enabled the identification of: coregulated hubs (including aspartic acid, 2-isopropylmalic acid, β-carotene, phytoene and dihydropseudoionone) in metabolic association networks; global patterns of coordinated compositional changes; and links of primary and secondary metabolism to key mineral and volatile fruit complements. • The results reveal the extent of metabolic interactions relevant to ripe fruit quality and thus have enabled the identification of essential candidate metabolites for the high-throughput screening of melon breeding populations for targeted breeding programmes aimed at nutrition and flavour improvement.

Optimization of Parameters for the Quantitative Surface-Enhanced Raman Scattering Detection of Mephedrone Using a Fractional Factorial Design and a Portable Raman Spectrometer.

A new optimization strategy for the SERS detection of mephedrone using a portable Raman system has been developed. A fractional factorial design was employed, and the number of statistically significant experiments (288) was greatly reduced from the actual total number of experiments (1722), which minimized the workload while maintaining the statistical integrity of the results. A number of conditions were explored in relation to mephedrone SERS signal optimization including the type of nanoparticle, pH, and aggregating agents (salts). Through exercising this design, it was possible to derive the significance of each of the individual variables, and we discovered four optimized SERS protocols for which the reproducibility of the SERS signal and the limit of detection (LOD) of mephedrone were established. Using traditional nanoparticles with a combination of salts and pHs, it was shown that the relative standard deviations of mephedrone-specific Raman peaks were as low as 0.51%, and the LOD was estimated to be around 1.6 μg/mL (9.06 × 10(-6) M), a detection limit well beyond the scope of conventional Raman and extremely low for an analytical method optimized for quick and uncomplicated in-field use.

Metabolic acclimation to hypoxia revealed by metabolite gradients in melon fruit.

A metabolomics approach using (1)H NMR and GC-MS profiling of primary metabolites and quantification of adenine nucleotides with luciferin bioluminescence was employed to investigate the spatial changes of metabolism in melon fruit. Direct (1)H NMR profiling of juice collected from different locations in the fruit flesh revealed several gradients of metabolites, e.g. sucrose, alanine, valine, GABA or ethanol, with increase in concentrations from the periphery to the center of the fruit. GC-MS profiling of ground samples revealed gradients for metabolites not detected using (1)H NMR, including pyruvic and fumaric acids. The quantification of adenine nucleotides highlighted a strong decrease in both ATP and ADP ratios and the adenylate energy charge from the periphery to the center of the fruit. These concentration patterns are consistent with an increase in ethanol fermentation due to oxygen limitation and were confirmed by observed changes in alanine and GABA concentrations, as well as other markers of hypoxia in plants. Ethanol content in melon fruit can affect organoleptic properties and consumer acceptance. Understanding how and when fermentation occurred can help to manage the culture and limit ethanol production.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

IntroductionThe generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.ObjectivesCurrently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.MethodsWe assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.ResultsOur studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.ConclusionThe appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

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.