Adam J. Carroll

Recommendations for Reporting Metabolite Data

The last decade has witnessed dramatic advances in our ability to determine the levels of an ever broadening range of metabolites. That said, while transcriptomic approaches provide almost complete coverage ([Gonzalez-Ballester et al., 2010][1]) and proteomics approaches are now capable of

Mitochondrial complex II has a key role in mitochondrial-derived reactive oxygen species influence on plant stress gene regulation and defense

Mitochondria are both a source of ATP and a site of reactive oxygen species (ROS) production. However, there is little information on the sites of mitochondrial ROS (mROS) production or the biological role of such mROS in plants. We provide genetic proof that mitochondrial complex II (Complex II) of the electron transport chain contributes to localized mROS that regulates plant stress and defense responses. We identify an Arabidopsis mutant in the Complex II subunit, SDH1-1, through a screen for mutants lacking GSTF8 gene expression in response to salicylic acid (SA). GSTF8 is an early stress-responsive gene whose transcription is induced by biotic and abiotic stresses, and its expression is commonly used as a marker of early stress and defense responses. Transcriptional analysis of this mutant, disrupted in stress responses 1 (dsr1), showed that it had altered SA-mediated gene expression for specific downstream stress and defense genes, and it exhibited increased susceptibility to specific fungal and bacterial pathogens. The dsr1 mutant also showed significantly reduced succinate dehydrogenase activity. Using in vivo fluorescence assays, we demonstrated that root cell ROS production occurred primarily from mitochondria and was lower in the mutant in response to SA. In addition, leaf ROS production was lower in the mutant after avirulent bacterial infection. This mutation, in a conserved region of SDH1-1, is a unique plant mitochondrial mutant that exhibits phenotypes associated with lowered mROS production. It provides critical insights into Complex II function with implications for understanding Complex IIs role in mitochondrial diseases across eukaryotes.

The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets

BackgroundStandardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.DescriptionHere, we present MetabolomeExpress, a new File Transfer Protocol (FTP) server and web-tool for the online storage, processing, visualisation and statistical re-analysis of publicly submitted GC/MS metabolomics datasets. Users may search a quality-controlled database of metabolite response statistics from publicly submitted datasets by a number of parameters (eg. metabolite, species, organ/biofluid etc.). Users may also perform meta-analysis comparisons of multiple independent experiments or re-analyse public primary datasets via user-friendly tools for t-test, principal components analysis, hierarchical cluster analysis and correlation analysis. They may interact with chromatograms, mass spectra and peak detection results via an integrated raw data viewer. Researchers who register for a free account may upload (via FTP) their own data to the server for online processing via a novel raw data processing pipeline.ConclusionsMetabolomeExpress https://www.metabolome-express.org provides a new opportunity for the general metabolomics community to transparently present online the raw and processed GC/MS data underlying their metabolomics publications. Transparent sharing of these data will allow researchers to assess data quality and draw their own insights from published metabolomics datasets.

The Arabidopsis Cytosolic Ribosomal Proteome: From form to Function.

The cytosolic ribosomal proteome of Arabidopsis thaliana has been studied intensively by a range of proteomics approaches and is now one of the most well characterized eukaryotic ribosomal proteomes. Plant cytosolic ribosomes are distinguished from other eukaryotic ribosomes by unique proteins, unique post-translational modifications and an abundance of ribosomal proteins for which multiple divergent paralogs are expressed and incorporated. Study of the A. thaliana ribosome has now progressed well beyond a simple cataloging of protein parts and is focused strongly on elucidating the functions of specific ribosomal proteins, their paralogous isoforms and covalent modifications. This review summarises current knowledge concerning the Arabidopsis cytosolic ribosomal proteome and highlights potentially fruitful areas of future research in this fast moving and important area.

From models to crop species: caveats and solutions for translational metabolomics

Although plant metabolomics is largely carried out on Arabidopsis it is essentially genome-independent, and thus potentially applicable to a wide range of species. However, transfer between species, or even between different tissues of the same species, is not facile. This is because the reliability of protocols for harvesting, handling and analysis depends on the biological features and chemical composition of the plant tissue. In parallel with the diversification of model species it is important to establish good handling and analytic practice, in order to augment computational comparisons between tissues and species. Liquid chromatography–mass spectrometry (LC–MS)-based metabolomics is one of the powerful approaches for metabolite profiling. By using a combination of different extraction methods, separation columns, and ion detection, a very wide range of metabolites can be analyzed. However, its application requires careful attention to exclude potential pitfalls, including artifactual changes in metabolite levels during sample preparation under variations of light or temperature and analytic errors due to ion suppression. Here we provide case studies with two different LC–MS-based metabolomics platforms and four species (Arabidopsis thaliana, Chlamydomonas reinhardtii, Solanum lycopersicum, and Oryza sativa) that illustrate how such dangers can be detected and circumvented.

Discovering and linking public omics data sets using the Omics Discovery Index.

Yasset Perez-Riverola,†,*, Mingze Baia,b,c,†, Felipe da Veiga Leprevostd, Silvano Squizzatoa, Young Mi Parka, Kenneth Hauga, Adam J. Carrolle, Dylan Spaldinga, Justin Paschalla, Mingxun Wangf, Noemi del-Toroa, Tobias Ternenta, Peng Zhangd,g, Nicola Busoa, Nuno Bandeiraf, Eric W. Deutschh, David S Campbellh, Ronald C. Beavisi, Reza M. Saleka, Ugis Sarkansa, Robert Petryszaka, Maria Keaysa, Eoin Fahyj, Manish Sudj, Shankar Subramaniamj, Ariana Barberak, Rafael C. Jiménezl, Alexey I. Nesvizhskiid, SusannaAssunta Sansonem, Christoph Steinbecka, Rodrigo Lopeza, Juan Antonio Vizcaínoa, Peipei Pingn, and Henning Hermjakoba,c,* aEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

In vivo stoichiometry of photorespiratory metabolism

Photorespiration is a major light-dependent metabolic pathway that consumes oxygen and produces carbon dioxide. In the metabolic step responsible for carbon dioxide production, two molecules of glycine (equivalent to two molecules of O2) are converted into one molecule of serine and one molecule of CO2. Here, we use quantitative isotopic techniques to determine the stoichiometry of this reaction in sunflower leaves, and thereby the O2/CO2 stoichiometry of photorespiration. We find that the effective O2/CO2 stoichiometric coefficient at the leaf level is very close to 2 under normal photorespiratory conditions, in line with expectations, but increases slightly at high rates of photorespiration. The net metabolic impact of this imbalance is likely to be modest.

PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links

This article describes PhenoMeter (PM), a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of Arabidopsis thaliana metabolism mutants affected in different steps of the photorespiration metabolic pathway to reference phenotypes of mutants affected in the same enzymes by independent mutations. The best performing statistic, the PM score, was a function of both Pearson correlation and Fisher’s Exact Test of directional overlap. This statistic outperformed Pearson correlation, biweight midcorrelation and Fisher’s Exact Test used alone. To demonstrate general applicability, we show that the PM reliably retrieved the most closely functionally linked response in the database when queried with responses to a wide variety of environmental and genetic perturbations. Attempts to match metabolic phenotypes between independent studies were met with varying success and possible reasons for this are discussed. Overall, our results suggest that integration of pattern-based search tools into metabolomics databases will aid functional annotation of newly recorded metabolic phenotypes analogously to the way sequence similarity search algorithms have aided the functional annotation of genes and proteins. PM is freely available at MetabolomeExpress (https://www.metabolome-express.org/phenometer.php).

Metabolomics analysis of postphotosynthetic effects of gaseous O2 on primary metabolism in illuminated leaves

The response of underground plant tissues to O2 limitation is currently an important topic in crop plants since adverse environmental conditions (e.g. waterlogging) may cause root hypoxia and thus compromise plant growth. However, little is known on the effect of low O2 conditions in leaves, probably because O2 limitation is improbable in these tissues under natural conditions, unless under complete submersion. Nevertheless, an O2-depleted atmosphere is commonly used in gas exchange experiments to suppress photorespiration and estimate gross photosynthesis. However, the nonphotosynthetic effects of gaseous O2 depletion, particularly on respiratory metabolism, are not well documented. Here, we used metabolomics obtained under contrasting O2 and CO2 conditions to examine the specific effect of a changing O2 mole fraction from ambient (21%) to 0%, 2% or 100%. In addition to the typical decrease in photorespiratory intermediates (glycolate, glycine and serine) and a build-up in photosynthates (sucrose), low O2 (0% or 2%) was found to trigger an accumulation of alanine and change succinate metabolism. In 100% O2, the synthesis of threonine and methionine from aspartate appeared to be stimulated. These responses were observed in two species, sunflower (Helianthus annuus L.) and Arabidopsis thaliana (L.) Heynh. Our results show that O2 causes a change in the oxygenation : carboxylation ratio and also alters postphotosynthetic metabolism: (i) a hypoxic response at low O2 mole fractions and (ii) a stimulation of S metabolism at high O2 mole fractions. The latter effect is an important piece of information to better understand how photorespiration may control S assimilation.

Editorial: Metabolome Informatics and Statistics: Current State and Emerging Trends

The Editorial on the Research Topic Metabolome Informatics and Statistics: Current State and Emerging Trends Metabolomics has developed tremendously since the term “metabolome” was coined almost 20 years ago (Oliver et al., 1998). Once the domain of few laboratories, it is now a core capability at most major universities and research institutions. An important developmental indicator for any scientific discipline is the maturity of its informatics – the functional diversity and efficacy of computational and statistical approaches, software tools, databases, and data exchange standards that help transform raw data into understanding. The 13 articles from 81 authors in this frontiers research topic provide a snapshot of the current state of these platforms. Metabolomics offers unique challenges for software developers. One of the most fundamental is the development of databases enabling the structures and properties of metabolites to be queried in ways that enhance research. In this research topic, Johnson and Lange review the development of open-access spectral reference databases to aid natural product identification. Maeda describes 3DMET1 – a database of metabolite three-dimensional (3D) structures – using software to convert 2D representations of 3D structures from printed articles to 3D digital structure models. The diversity of analytical techniques used in metabolomics is wider than in other omics disciplines, and the list of relevant technologies continues to grow. Thus, each technique is typically associated with a swathe of literature describing specialized computational methods and software tools, and there is always demand for new software to support new methods. The review article “Analytical methods in untargeted metabolomics: state of the art in 2015” provides a useful overview of this area (Alonso et al.). For the processing of liquid chromatography mass spectrometry (LC-MS) data, Tsugawa et al. describe multiple reaction monitoring (MRM)-DIFF – a powerful pipeline for MRM-based analysis of lipidomic samples on LC-triple quadrupole MS instruments. As a demonstration of novel approaches for LC-MS peak identification, van der Hooft et al. describe how an analysis of high resolution MSn fragmentation spectra using freely available MAGMa software2 could be used to annotate peaks of 50 different acylcarnitines in human urine. For gas chromatography-MS (GC-MS) metabolomics data processing, Kuich et al. introduce Maui-VIA – a GUI-based tool that streamlines the visual curation of peak identifications and quantifications. Also for GC-MS, Franceschi et al. describe MetaDB, a web application providing an user-friendly web interface and laboratory information system (LIMS)-like handling of workflow metadata to metaMS – their R-based data processing pipeline for untargeted quantitative GC/MS metabolomics. Trutschel et al. demonstrate that the joint analysis of multiple dependent signals from the same metabolite (e.g., multiple fragments) using multivariate statistical tests can provide enhanced statistical power to detect differential metabolite abundance than the typical univariate analysis of single signals. Another exciting area of metabolome informatics and statistics is the development of computational approaches to assist biological interpretation. Sun et al. discuss the potential to derive quantitative information about causality networks responsible for metabolome dynamics from metabolomics data and metabolic models by inverse Jacobian estimation. Kessler et al. demonstrate machine learning-based classification of crops as having been “organically” or “non-organically” grown. Uppal et al. present MetabNet – an R package to detect associations between metabolites of interest and peaks detected in LC-MS experiments for the purposes of detecting likely metabolic pathway connections. Finally, Carroll et al. (2010) presented PhenoMeter – a tool for functionally annotating query metabolic phenotypes by matching them against the MetabolomeExpress phenotypic reference database just as BLAST searches are used to annotate nucleotide or protein sequences (Carroll et al.). With so many metabolomics datasets in the literature and the rate of data generation increasing, the need to systematically index and annotate them has never been more urgent. To this end, Metabolonote3 of Ara et al. takes the innovative approach of using a Wiki style interface to facilitate community-based metadata annotation of metabolomics datasets from the literature, delocalizing the burden of this work. The metadata handling functions of the MetaDB pipeline described by Franceschi et al. also aim to streamline the annotation of datasets while supporting the standard ISA-Tab format for dissemination through the MetaboLights4 data repository (Haug et al., 2012). Metabolome informatics and statistics is incredibly broad and fast moving, and this research topic can therefore offer only a cross-sectional sample of developments at one point in time. The cutting edge developments of the future will be built upon those of the present and in a field as rapidly evolving as metabolomics, it is particularly critical and challenging to ensure one’s own work builds upon and benefits from the efforts of others as much as possible. We hope the reader finds this research topic a useful contemporary reference to the field that informs and inspires exciting future innovations and collaborations that help realize the full potential of metabolomics.