Manfred Beckmann

A proposed framework for the description of plant metabolomics experiments and their results

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

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.

The metabolic transition during disease following infection of Arabidopsis thaliana by Pseudomonas syringae pv. tomato.

The outcome of bacterial infection in plants is determined by the ability of the pathogen to successfully occupy the apoplastic space and deliver a constellation of effectors that collectively suppress basal and effector-triggered immune responses. In this study, we examined the metabolic changes associated with establishment of disease using analytical techniques that interrogated a range of chemistries. We demonstrated clear differences in the metabolome of Arabidopsis thaliana leaves infected with virulent Pseudomonas syringae within 8 h of infection. In addition to confirmation of changes in phenolic and indolic compounds, we identified rapid alterations in the abundance of amino acids and other nitrogenous compounds, specific classes of glucosinolates, disaccharides, and molecules that influence the prevalence of reactive oxygen species. Our data illustrate that, superimposed on defence suppression, pathogens reconfigure host metabolism to provide the sustenance required to support exponentially growing populations of apoplastically localized bacteria. We performed a detailed baseline study reporting the metabolic dynamics associated with bacterial infection. Moreover, we have integrated these data with the results of transcriptome profiling to distinguish metabolomic pathways that are transcriptionally activated from those that are post-transcriptionally regulated.

Regulation of yeast oscillatory dynamics

When yeast cells are grown continuously at high cell density, a respiratory oscillation percolates throughout the population. Many essential cellular functions have been shown to be separated temporally during each cycle; however, the regulatory mechanisms involved in oscillatory dynamics remain to be elucidated. Through GC-MS analysis we found that the majority of metabolites show oscillatory dynamics, with 70% of the identified metabolite concentrations peaking in conjunction with NAD(P)H. Through statistical analyses of microarray data, we identified that biosynthetic events have a defined order, and this program is initiated when respiration rates are increasing. We then combined metabolic, transcriptional data and statistical analyses of transcription factor activity, identified the top oscillatory parameters, and filtered a large-scale yeast interaction network according to these parameters. The analyses and controlled experimental perturbation provided evidence that a transcriptional complex formed part of the timing circuit for biosynthetic, reductive, and cell cycle programs in the cell. This circuitry does not act in isolation because both have strong translational, proteomic, and metabolic regulatory mechanisms. Our data lead us to conclude that the regulation of the respiratory oscillation revolves around coupled subgraphs containing large numbers of proteins and metabolites, with a potential to oscillate, and no definable hierarchy, i.e., heterarchical control.

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.

Use of mass spectrometry fingerprinting to identify urinary metabolites after consumption of specific foods

BACKGROUND The lack of robust biological markers of dietary exposure hinders the quantitative understanding of causal relations between diet and health. OBJECTIVE We aimed to develop an efficient procedure to discover metabolites in urine that may have future potential as biomarkers of acute exposure to foods of high public health importance. DESIGN Twenty-four participants were provided with a test breakfast in which the cereal component of a standardized breakfast was replaced by 1 of 4 foods of high public health importance; 1.5-, 3-, and 4.5-h postprandial urine samples were collected. Flow infusion electrospray-ionization mass spectrometry followed by supervised multivariate data analysis was used to discover signals resulting from consumption of each test food. RESULTS Fasted-state urine samples provided a universal comparator for food biomarker lead discovery in postprandial urine. The filtering of data features associated with consumption of the common components of the standardized breakfast improved discrimination models and readily identified metabolites that showed consumption of specific test foods. A combination of trimethylamine-N-oxide and 1-methylhistidine was associated with salmon consumption. Novel ascorbate derivatives were discovered in urine after consumption of either broccoli or raspberries. Sulphonated caffeic acid and sulphonated methyl-epicatechin concentrations increased dramatically after consumption of raspberries. CONCLUSIONS This biomarker lead discovery strategy can identify urinary metabolites associated with acute exposure to individual foods. Future studies are required to validate the specificity and utility of potential biomarkers in an epidemiologic context.

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.

Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?

The diet is an important environmental exposure, and its measurement is an essential component of much health-related research. However, conventional tools for measuring dietary exposure have significant limitations being subject to an unknown degree of misreporting and dependent upon food composition tables to allow estimation of intakes of energy, nutrients and non-nutrient food constituents. In addition, such tools may be inappropriate for use with certain groups of people. As an alternative approach, the recent techniques of metabolite profiling or fingerprinting, which allows simultaneous monitoring of multiple and dynamic components of biological fluids, may provide metabolic signals indicative of food intake. Samples can be analysed through numerous analytical platforms, followed by multivariate data analysis. In humans, metabolomics has been applied successfully in pharmacology, toxicology and medical screening, but nutritional metabolomics is still in its infancy. Biomarkers of a small number of specific foods and nutrients have been developed successfully but less targeted and more high-throughput methods, that do not need prior knowledge of which signals might be discriminatory, and which may allow a more global characterisation of dietary intake, remain to be tested. A proof a principle project (the MEDE Study) is currently underway in our laboratories to test the hypothesis that high-throughput, non-targeted metabolite fingerprinting using flow injection electrospray mass spectrometry can be applied to human biofluids (blood and urine) to characterise dietary exposure in humans.

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.

The Scale-Free Dynamics of Eukaryotic Cells

Temporal organization of biological processes requires massively parallel processing on a synchronized time-base. We analyzed time-series data obtained from the bioenergetic oscillatory outputs of Saccharomyces cerevisiae and isolated cardiomyocytes utilizing Relative Dispersional (RDA) and Power Spectral (PSA) analyses. These analyses revealed broad frequency distributions and evidence for long-term memory in the observed dynamics. Moreover RDA and PSA showed that the bioenergetic dynamics in both systems show fractal scaling over at least 3 orders of magnitude, and that this scaling obeys an inverse power law. Therefore we conclude that in S. cerevisiae and cardiomyocytes the dynamics are scale-free in vivo. Applying RDA and PSA to data generated from an in silico model of mitochondrial function indicated that in yeast and cardiomyocytes the underlying mechanisms regulating the scale-free behavior are similar. We validated this finding in vivo using single cells, and attenuating the activity of the mitochondrial inner membrane anion channel with 4-chlorodiazepam to show that the oscillation of NAD(P)H and reactive oxygen species (ROS) can be abated in these two evolutionarily distant species. Taken together these data strongly support our hypothesis that the generation of ROS, coupled to redox cycling, driven by cytoplasmic and mitochondrial processes, are at the core of the observed rhythmicity and scale-free dynamics. We argue that the operation of scale-free bioenergetic dynamics plays a fundamental role to integrate cellular function, while providing a framework for robust, yet flexible, responses to the environment.