Arjen Lommen

Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 samples.

MetAlign: Interface-Driven, Versatile Metabolomics Tool for Hyphenated Full-Scan Mass Spectrometry Data Preprocessing

Hyphenated full-scan MS technology creates large amounts of data. A versatile easy to handle automation tool aiding in the data analysis is very important in handling such a data stream. MetAlign softwareas described in this manuscripthandles a broad range of accurate mass and nominal mass GC/MS and LC/MS data. It is capable of automatic format conversions, accurate mass calculations, baseline corrections, peak-picking, saturation and mass-peak artifact filtering, as well as alignment of up to 1000 data sets. A 100 to 1000-fold data reduction is achieved. MetAlign software output is compatible with most multivariate statistics programs.

The genetics of plant metabolism

Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography–time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.

MADMAX - Management and analysis database for multiple ~omics experiments

The rapid increase of ~omics datasets generated by microarray, mass spectrometry and next generation sequencing technologies requires an integrated platform that can combine results from different ~omics datasets to provide novel insights in the understanding of biological systems. MADMAX is designed to provide a solution for storage and analysis of complex ~omics datasets. In addition, analysis results (such as lists of genes) will be merged to reveal candidate genes supported by all datasets. The system constitutes an ISA-Tab compliant LIMS part which is independent of different analysis pipelines. A pilot study of different type of ~omics data in Brassica rapa demonstrates the possible use of MADMAX. The web-based user interface provides easy access to data and analysis tools on top of the database.

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.

MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware.

A new, multi-threaded version of the GC–MS and LC–MS data processing software, metAlign, has been developed which is able to utilize multiple cores on one PC. This new version was tested using three different multi-core PCs with different operating systems. The performance of noise reduction, baseline correction and peak-picking was 8–19 fold faster compared to the previous version on a single core machine from 2008. The alignment was 5–10 fold faster. Factors influencing the performance enhancement are discussed. Our observations show that performance scales with the increase in processor core numbers we currently see in consumer PC hardware development.

A non-directed approach to the differential analysis of multiple LC-MS-derived metabolic profiles

An essential element of any strategy for non-targeted metabolomics analysis of complex biological extracts is the capacity to perform comparisons between large numbers of samples. As the most widely used technologies are all based on mass spectrometry (e.g. GCMS, LCMS), this entails that we must be able to compare reliably and (semi)automatically large series of chromatographic mass spectra from which compositional differences are to be extracted in a statistically justifiable manner. In this paper we describe a novel approach for the extraction of relevant information from multiple full-scan metabolic profiles derived from LC–MS analyses. Specifically-designed software has made it possible to combine all mass peaks on the basis of retention time and m/z values only, without prior identification, to produce a data matrix output which can then be used for multivariate statistical analysis. To demonstrate the capacity of this approach, aqueous methanol extracts from potato tuber tissues of eight contrasting genotypes, harvested at two developmental stages have been used. Our results showed that it is possible to discover reproducibly discriminatory mass peaks related both to the genetic origin of the material as well as the developmental stage at which it was harvested. In addition the limitations of the approach are explored by a careful evaluation of the alignment quality.

An untargeted multi-technique metabolomics approach to studying intracellular metabolites of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin

BackgroundIn vitro cell systems together with omics methods represent promising alternatives to conventional animal models for toxicity testing. Transcriptomic and proteomic approaches have been widely applied in vitro but relatively few studies have used metabolomics. Therefore, the goal of the present study was to develop an untargeted methodology for performing reproducible metabolomics on in vitro systems. The human liver cell line HepG2, and the well-known hepatotoxic and non-genotoxic carcinogen 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), were used as the in vitro model system and model toxicant, respectively.ResultsThe study focused on the analysis of intracellular metabolites using NMR, LC-MS and GC-MS, with emphasis on the reproducibility and repeatability of the data. State of the art pre-processing and alignment tools and multivariate statistics were used to detect significantly altered levels of metabolites after exposing HepG2 cells to TCDD. Several metabolites identified using databases, literature and LC-nanomate-Orbitrap analysis were affected by the treatment. The observed changes in metabolite levels are discussed in relation to the reported effects of TCDD.ConclusionsUntargeted profiling of the polar and apolar metabolites of in vitro cultured HepG2 cells is a valid approach to studying the effects of TCDD on the cell metabolome. The approach described in this research demonstrates that highly reproducible experiments and correct normalization of the datasets are essential for obtaining reliable results. The effects of TCDD on HepG2 cells reported herein are in agreement with previous studies and serve to validate the procedures used in the present work.

Metabolomics Approach to Anabolic Steroid Urine Profiling of Bovines Treated with Prohormones

In livestock production, illegal use of natural steroids is hard to prove because metabolites are either unknown or not significantly above highly fluctuating endogenous levels. In this work we outlined for the first time a metabolomics based strategy for anabolic steroid urine profiling. Urine profiles of controls and bovines treated with the prohormones dehydroepiandrosterone (DHEA) and pregnenolone were analyzed with ultraperformance liquid chromatography in combination with time-of-flight accurate mass spectrometry (UPLC-TOFMS). The obtained full scan urinary profiles were compared using sophisticated preprocessing and alignment software (MetAlign) and multivariate statistics, revealing hundreds of mass signals which were differential between untreated control and prohormone-treated animals. Moreover, statistical testing of the individual accurate mass signals showed that several mass peak loadings could be used as biomarkers for DHEA and pregnenolone abuse. In addition, accurate mass derived elemental composition analysis and verification by standards or Orbitrap mass spectrometry demonstrated that the observed differential masses are most likely steroid phase I and glucuronide metabolites excreted as a direct result from the DHEA and pregnenolone administration, thus underlining the relevance of the findings from this untargeted metabolomics approach. It is envisaged that this approach can be used as a holistic screening tool for anabolic steroid abuse in bovines and possibly in sports doping as well.

Plant members of the α1→3/4‐fucosyltransferase gene family encode an α1→4‐fucosyltransferase, potentially involved in Lewisa biosynthesis, and two core α1→3‐fucosyltransferases1

Three putative α1→3/4‐fucosyltransferase (α1→3/4‐FucT) genes have been detected in the Arabidopsis thaliana genome. The products of two of these genes have been identified in vivo as core α1→3‐FucTs involved in N‐glycosylation. An orthologue of the third gene was isolated from a Beta vulgaris cDNA library. The encoded enzyme efficiently fucosylates Galβ1→3GlcNAcβ1→3Galβ1→4Glc. Analysis of the product by 400 MHz 1H‐nuclear magnetic resonance spectroscopy showed that the product is α1→4‐fucosylated at the N‐acetylglucosamine residue. In vitro, the recombinant B. vulgaris α1→4‐FucT acts efficiently only on neutral type 1 chain‐based glycan structures. In plants the enzyme is expected to be involved in Lewisa formation on N‐linked glycans.