David Pierre Louis Enot

Metabolomic analysis reveals a common pattern of metabolic re‐programming during invasion of three host plant species by Magnaporthe grisea

The mechanisms by which biotrophic and hemi-biotrophic fungal pathogens simultaneously subdue plant defences and sequester host nutrients are poorly understood. Using metabolite fingerprinting, we show that Magnaporthe grisea, the causal agent of rice blast disease, dynamically re-programmes host metabolism during plant colonization. Identical patterns of metabolic change occurred during M. grisea infections in barley, rice and Brachypodium distachyon. Targeted metabolite profiling by GC-MS confirmed the modulation of a conserved set of metabolites. In pre-symptomatic tissues, malate and polyamines accumulated, rather than being utilized to generate defensive reactive oxygen species, and the levels of metabolites associated with amelioration of redox stress in various cellular compartments increased dramatically. The activity of NADP-malic enzyme and generation of reactive oxygen species were localized to pathogen penetration sites, and both appeared to be suppressed in compatible interactions. Early diversion of the shikimate pathway to produce quinate was observed, as well as accumulation of non-polymerized lignin precursors. These data are consistent with modulation of defensive phenylpropanoid metabolism by M. grisea and the inability of susceptible hosts to mount a hypersensitive reaction or produce lignified papillae (both involving reactive oxygen species) to restrict pathogen invasion. Rapid proliferation of M. grisea hyphae in plant tissue after 3 days was associated with accelerated nutrient acquisition and utilization by the pathogen. Conversion of photoassimilate into mannitol and glycerol for carbon sequestration and osmolyte production appear to drive hyphal growth. Taken together, our results suggest that fungal pathogens deploy a common metabolic re-programming strategy in diverse host species to suppress plant defence and colonize plant tissue.

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

BackgroundMetabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite fingerprint or metabolite profile data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).ResultsMetabolite structures harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. Rules were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data.ConclusionWe conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation hits in ESI-MS data.

High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry

Flow injection electrospray–mass spectrometry (FIE–MS) is finding utility as a first-pass metabolite fingerprinting tool in many research fields. We provide a protocol that has proved reliable in large-scale research projects involving diverse sample matrices originating from plants, microbes and mammalian biofluids. Using Brachypodium leaf material as an example matrix all steps are summarized from sample extraction to data quality assessment. Alternative procedures for dealing with other common matrices such as bloods and urine are included. With little sample pretreatment, no chromatography and instrument cycle times of <5 min it is feasible to analyze >1,000 samples per week. Analysis of a typical batch of 240 samples (including first-pass data analysis) can be accomplished within 48 h.

Rice blast infection of Brachypodium distachyon as a model system to study dynamic host/pathogen interactions

Interactions between plants and compatible fungal pathogens are spatially and temporally dynamic, posing a major challenge for sampling and data analysis. A protocol is described for the infection of the model grass species Brachypodium distachyon with Magnaporthe grisea (rice blast), together with modifications to extend the use to rice and barley. We outline a method for the preparation of long-term stocks of virulent fungal pathogens and for the generation of fungal inoculants for challenge of host plants. Host plant growth, pathogen inoculation and plant sampling protocols are presented together with methods for assessing the efficiency of both infection and sampling procedures. Included in the anticipated results is a description of the use of metabolite fingerprinting and multivariate data analysis to assess disease synchrony and validate system reproducibility between experiments. The design concepts will have value in any studies using biological systems that contain dynamic variance associated with large compositional changes in sample matrix over time.

Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data

Metabolome analysis by flow injection electrospray mass spectrometry (FIE-MS) fingerprinting generates measurements relating to large numbers of m/z signals. Such data sets often exhibit high variance with a paucity of replicates, thus providing a challenge for data mining. We describe data preprocessing and modeling methods that have proved reliable in projects involving samples from a range of organisms. The protocols interact with software resources specifically for metabolomics provided in a Web-accessible data analysis package FIEmspro (http://users.aber.ac.uk/jhd) written in the R environment and requiring a moderate knowledge of R command-line usage. Specific emphasis is placed on describing the outcome of modeling experiments using FIE-MS data that require further preprocessing to improve quality. The salient features of both poor and robust (i.e., highly generalizable) multivariate models are outlined together with advice on validating classifiers and avoiding false discovery when seeking explanatory variables.

Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a first pass screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de)protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting.

Metabolite fingerprinting of urine suggests breed-specific dietary metabolism differences in domestic dogs

Selective breeding of dogs has culminated in a large number of modern breeds distinctive in terms of size, shape and behaviour. Inadvertently, a range of breed-specific genetic disorders have become fixed in some pure-bred populations. Several inherited conditions confer chronic metabolic defects that are influenced strongly by diet, but it is likely that many less obvious breed-specific differences in physiology exist. Using Labrador retrievers and miniature Schnauzers maintained in a simulated domestic setting on a controlled diet, an experimental design was validated in relation to husbandry, sampling and sample processing for metabolomics. Metabolite fingerprints were generated from spot urine samples using flow injection electrospray MS (FIE-MS). With class based on breed, urine chemical fingerprints were modelled using Random Forest (a supervised data classification technique), and metabolite features (m/z) explanatory of breed-specific differences were putatively annotated using the ARMeC database (http://www.armec.org). GC-MS profiling to confirm FIE-MS predictions indicated major breed-specific differences centred on the metabolism of diet-related polyphenols. Metabolism of further diet components, including potentially prebiotic oligosaccharides, animal-derived fats and glycerol, appeared significantly different between the two breeds. Analysis of the urinary metabolome of young male dogs representative of a wider range of breeds from animals maintained under domestic conditions on unknown diets provided preliminary evidence that many breeds may indeed have distinctive metabolic differences, with significant differences particularly apparent in comparisons between large and smaller breeds.

Detecting a difference - assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants

There is current debate on whether genetically-manipulated plants might contain unexpected, potentially undesirable, changes in overall metabolite composition relative to that of the progenitor genotype. However, appropriate analytical technology and acceptable metrics of compositional similarity require development, particularly to allow data integration from different laboratories and different harvests. For an initial comprehensive overview of compositional similarity, we explored the use of a rapid and relatively non-selective fingerprinting technique based on flow injection electrospray ionisation mass spectrometry (FIE-MS). Six conventionally-bred potato cultivars and six experimental bioengineered potato genotypes were produced in four field blocks during two growing seasons and analysed on two different analytical instruments (LCT, Micromass in 2001 and LTQ, Thermo Finnigan in 2003). Field effects and overall process variability was found to be negligible when compared to inherited genotype variance. The data derived separately for experiments using tubers from individual harvest years were compared to assess the generalisability of models for the comparison of GM and non-GM potato tubers under investigation. This procedure proved appropriate for not only rapid assessment of similarities between plant genotypes but also to predict the identity of metabolite signals that could explain differences between genotype classes irrespective of the instrument used for analysis. Importantly, despite differences in ionisation and data acquisition properties of the two instruments the generalisation of models could be confirmed after correlation analysis of explanatory variables correctly identified the molecular origin of differences between genotypes. We conclude that FIE-MS metabolomics fingerprinting technology coupled to machine learning data analysis has great potential as a robust tool for first-pass metabolic phenotyping and, therefore, initial assessments of compositional similarities prior to use of more targeted hyphenated gas or liquid chromatography-mass spectrometry techniques.

Statistical measures for validating plant genotype similarity assessments following multivariate analysis of metabolome fingerprint data

Metabolome fingerprinting offers opportunities for ‘first pass’ evaluation of compositional similarity between plant genotypes. Compositional “substantial equivalence” testing is a popular concept in the literature in relation to food safety; however reported studies do not provide a systematic and standard approach to quantify similarity in a high dimensional data context. We have undertaken a large scale screen of Arabidopsis genotypes for evidence that individual genetic modifications effect plant phenotype at the level of the metabolome. From this study we propose pragmatic alternative measures that could in the future be used to assess substantial equivalence in GM foods under realistic data paucity constraints and without prior feature selection. Evaluation of classifier accuracy in supervised data mining approaches by bootstrap error estimation provided a robust tool for model validation. Receiver operating characteristics (such as AUC) provide an alternative measure of predictive ability by displaying the relationship between sensitivity and specificity. Additional specific measures based on scatter matrices and sample margins have also been investigated. We illustrate the application of such metrics on a large metabolic profiling data set derived from analysis of 27 genetically distinct Arabidopsisthaliana mutants. We show that agreement exists between model margins, eigenvalue, accuracies and AUC characteristics produced by three different classifiers (Random Forest, Support Vector Machine and Linear Discriminant Analysis). Comparisons between mutants with no observable phenotypic differences to the parent ecotype provided a baseline for model significance metrics; whilst comparison of mutants with increasingly distinct phenotypic alterations generated predictable changes in these measures of similarity.

On the interpretation of high throughput MS based metabolomics fingerprints with random forest

We discuss application of a machine learning method, Random Forest (RF), for the extraction of relevant biological knowledge from metabolomics fingerprinting experiments. The importance of RF margins and variable significance as well as prediction accuracy is discussed to provide insight into model generalisability and explanatory power. A method is described for detection of relevant features while conserving the redundant structure of the fingerprint data. The methodology is illustrated using two datasets from electrospray ionisation mass spectrometry from 27 Arabidopsis genotypes and a set of transgenic potato lines.