Metabolomics Standards Initiative Research Articles

Metabolomes and Lipidomes of the Infective Stages of the Gastrointestinal nematodes, Nippostrongylus brasiliensis and Trichuris muris.

Soil-transmitted helminths, including hookworms and whipworms, infect billions of people worldwide. Their capacity to penetrate and migrate through their hosts’ tissues is influenced by the suite of molecules produced by the infective developmental stages. To facilitate a better understanding of the immunobiology and pathogenicity of human hookworms and whipworms, we investigated the metabolomes of the infective stage of Nippostrongylus brasiliensis third-stage larvae (L3) which penetrate the skin and Trichuris muris eggs which are orally ingested, using untargeted liquid chromatography-mass spectrometry (LC-MS). We identified 55 polar metabolites through Metabolomics Standard Initiative level-1 (MSI-I) identification from N. brasiliensis and T. muris infective stages, out of which seven were unique to excretory/secretory products (ESPs) of N. brasiliensis L3. Amino acids were a principal constituent (33 amino acids). Additionally, we identified 350 putative lipids, out of which 28 (all known lipids) were unique to N. brasiliensis L3 somatic extract and four to T. muris embryonated egg somatic extract. Glycerophospholipids and glycerolipids were the major lipid groups. The catalogue of metabolites identified in this study shed light on the biology, and possible therapeutic and diagnostic targets for the treatment of these critical infectious pathogens. Moreover, with the growing body of literature on the therapeutic utility of helminth ESPs for treating inflammatory diseases, a role for metabolites is likely but has received little attention thus far.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners.

The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics. All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

High Resolution GC-Orbitrap-MS Metabolomics Using Both Electron Ionization and Chemical Ionization for Analysis of Human Plasma.

Gas chromatography-mass spectrometry (GC-MS) platforms are typically run in electron ionization (EI) mode for mass spectral matching and metabolite annotation. With the advent of high resolution mass spectrometry (HRMS), soft ionization techniques such as chemical ionization (CI) may provide additional coverage for compound identification. We evaluated NIST SRM 1950 pooled plasma reference sample using a HRGC-MS instrument [GC-Orbitrap-MS with electron ionization (EI), positive chemical ionization (PCI), and negative CI (NCI) capabilities] for metabolite annotation and quantification to assess the suitability of the platform for routine discovery metabolomics. Using both open source and vendor workflows, we validated the spectral matches with an in-house spectral library (Wake Forest CPM GC-MS spectral and retention time libraries) of EI-MS and CI-MS/MS spectra obtained from chemical standards. We confidently [metabolomics standards initiative (MSI) confidence level 2] identified 263, 93, and 65 metabolites using EI, PCI, and NCI modes, respectively, of which 270 metabolites (64%) were validated using our Wake Forest CPM GC-MS spectral libraries. When compared to published LC-MS-based efforts using the same NIST SRM 1950 plasma sample, there was only 17% overlap between the two platforms. In addition, the metabolomics analysis of community approved standard human plasma demonstrated the ability of EI- and CI-MS modes of analysis using a HRGC-MS platform to enable reproducible and interoperable spectral matching.

Functional analysis of isoprenoid precursors biosynthesis by quantitative metabolomics and isotopologue profiling

IntroductionIsoprenoids are amongst the most abundant and diverse biological molecules and are involved in a broad range of biological functions. Functional understanding of their biosynthesis is thus key in many fundamental and applicative fields, including systems biology, medicine and biotechnology. However, available methods do not yet allow accurate quantification and tracing of stable isotopes incorporation for all the isoprenoids precursors.ObjectivesWe developed and validated a complete methodology for quantitative metabolomics and isotopologue profiling of isoprenoid precursors in the yeast Saccharomyces cerevisiae.MethodsThis workflow covers all the experimental and computational steps from sample collection and preparation to data acquisition and processing. It also includes a novel quantification method based on liquid chromatography coupled to high-resolution mass spectrometry. Method validation followed the Metabolomics Standards Initiative guidelines.ResultsThis workflow ensures accurate absolute quantification (RSD < 20%) of all mevalonate and prenyl pyrophosphates intermediates with a high sensitivity over a large linear range (from 0.1 to 50 pmol). In addition, we demonstrate that this workflow brings crucial information to design more efficient phytoene producers. Results indicate stable turnover rates of prenyl pyrophosphate intermediates in the constructed strains and provide quantitative information on the change of the biosynthetic flux of phytoene precursors.ConclusionThis methodology fills one of the last technical gaps for functional studies of isoprenoids biosynthesis and should be applicable to other eukaryotic and prokaryotic (micro)organisms after adaptation of some organism-dependent steps. This methodology also opens the way to 13C-metabolic flux analysis of isoprenoid biosynthesis.

MzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics.

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

Structure Annotation of All Mass Spectra in Untargeted Metabolomics.

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies.

The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The "putatively annotated compound" category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of "putatively annotated compound" assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data. To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to "putatively annotated compound" assignments in NMR-based metabolic profiling studies. The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment. Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to "putatively annotated compounds" in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories. RANCM is intended to reduce the ambiguity in "putatively annotated compound" assignments, to facilitate effective communication of the degree of confidence in "putatively annotated compound" assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.

Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time-Ion Mobility Mass Spectrometry.

Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass ( m/ z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.

Melatonin as a potential treatment for sepsis: metabolomic consequences of high oral dosing

Sepsis is a life-threatening dysregulated host response to infection, and kills over 40 000 people in the UK each year. Mitochondrial dysfunction driven by oxidative stress features and antioxidants that specifically protect mitochondria may offer a therapeutic strategy. Melatonin is a potent antioxidant and can be administered exogenously. It protects against mitochondrial damage in models of sepsis. Exogenous melatonin appears to be very safe, and clinical trials are underway. However, the metabolic consequences of exogenous melatonin are unknown. We undertook a metabolomic analysis of serum samples from the DAMSEL1 Phase I dose escalation trial.1 Briefly, cohorts of five subjects received a single oral dose of melatonin 20–100 mg, and blood samples were obtained over 6 h. Serum samples were extracted into chloroform/methanol/water at 4°C and stored at –80°C until transfer to the University of Glasgow Polyomics Centre. Hydrophilic interaction liquid chromatography (HILIC) was carried out using a zwitterionic-pHILIC column at 30°C. The samples were eluted with a linear gradient (ammonium carbonate/acetonitrile) over 26 min at 0.3 ml min–1. Mass spectrometry operated in polarity switching mode was then undertaken. Data were exported into a custom Excel template for processing of metabolomics to annotated and hyperlinked metabolite (IDentification and Evaluation of Metabolomics Data from LC-MS), and were processed with an mzMatch/XCMS pipeline. Authentic standards were run with each batch to identify metabolites according to the Metabolomics Standards Initiative with verification against the Kyoto Encyclopedia of Genes and Genomes database. Differences in metabolites were identified and relevant pathways interrogated to find changes across connected metabolites. Minor changes did not show recurrent patterns at different doses, showing that the melatonin administration did not affect the overall metabolic processes. Melatonin and its major metabolite 6-hydroxymelatonin had a dose–response pattern consistent with previous analysis patterns. No potential for bioactivation or macromolecule adducts was identified. Metabolomics can identify and quantify thousands of metabolites to define changes in biochemical pathways after administration of drugs. This work confirms the excellent safety profile of this drug. The authors thank the Chief Scientist Office (CSO) for funding the original Dose Assessment of Melatonin in Sepsis Trial 1 (DAMSEL1), and the Glasgow Polyomics Centre for the metabolomic analysis.

NmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

Compliance with minimum information guidelines in public metabolomics repositories

The Metabolomics Standards Initiative (MSI) guidelines were first published in 2007. These guidelines provided reporting standards for all stages of metabolomics analysis: experimental design, biological context, chemical analysis and data processing. Since 2012, a series of public metabolomics databases and repositories, which accept the deposition of metabolomic datasets, have arisen. In this study, the compliance of 399 public data sets, from four major metabolomics data repositories, to the biological context MSI reporting standards was evaluated. None of the reporting standards were complied with in every publicly available study, although adherence rates varied greatly, from 0 to 97%. The plant minimum reporting standards were the most complied with and the microbial and in vitro were the least. Our results indicate the need for reassessment and revision of the existing MSI reporting standards.

Proposed Confidence Scale and ID Score in the Identification of Known-Unknown Compounds Using High Resolution MS Data.

High-resolution (HR) MS instruments recording HR-full scan allow analysts to go further beyond pre-acquisition choices. Untargeted acquisition can reveal unexpected compounds or concentrations and can be performed for preliminary diagnosis attempt. Then, revealed compounds will have to be identified for interpretations. Whereas the need of reference standards is mandatory to confirm identification, the diverse information collected from HRMS allows identifying unknown compounds with relatively high degree of confidence without reference standards injected in the same analytical sequence. However, there is a necessity to evaluate the degree of confidence in putative identifications, possibly before further targeted analyses. This is why a confidence scale and a score in the identification of (non-peptidic) known-unknown, defined as compounds with entries in database, is proposed for (LC-) HRMS data. The scale is based on two representative documents edited by the European Commission (2007/657/EC) and the Metabolomics Standard Initiative (MSI), in an attempt to build a bridge between the communities of metabolomics and screening labs. With this confidence scale, an identification (ID) score is determined as [a number, a letter, and a number] (e.g., 2D3), from the following three criteria: I, a General Identification Category (1, confirmed, 2, putatively identified, 3, annotated compounds/classes, and 4, unknown); II, a Chromatography Class based on the relative retention time (from the narrowest tolerance, A, to no chromatographic references, D); and III, an Identification Point Level (1, very high, 2, high, and 3, normal level) based on the number of identification points collected. Three putative identification examples of known-unknown will be presented. Graphical Abstract ᅟ.

A Nontargeted UHPLC-HRMS Metabolomics Pipeline for Metabolite Identification: Application to Cardiac Remote Ischemic Preconditioning.

In recent years, the number of investigations based on nontargeted metabolomics has increased, although often without a thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated nontargeted metabolomics strategy with pipeline for unequivocal identification of metabolites using the MSMLS molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS, and MS/MS spectra. A UHPLC-HRMS Q-Exactive method was developed, and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from remote ischemic pre-conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy, and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites' VIP values, and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the metabolomics standard initiative and confirmed by both RPLC and HILIC-HRMS.

MetaboLights: An Open-Access Database Repository for Metabolomics Data.

MetaboLights is the first general purpose, open-access database repository for cross-platform and cross-species metabolomics research at the European Bioinformatics Institute (EMBL-EBI). Based upon the open-source ISA framework, MetaboLights provides Metabolomics Standard Initiative (MSI) compliant metadata and raw experimental data associated with metabolomics experiments. Users can upload their study datasets into the MetaboLights Repository. These studies are then automatically assigned a stable and unique identifier (e.g., MTBLS1) that can be used for publication reference. The MetaboLights Reference Layer associates metabolites with metabolomics studies in the archive and is extensively annotated with data fields such as structural and chemical information, NMR and MS spectra, target species, metabolic pathways, and reactions. The database is manually curated with no specific release schedules. MetaboLights is also recommended by journals for metabolomics data deposition. This unit provides a guide to using MetaboLights, downloading experimental data, and depositing metabolomics datasets using user-friendly submission tools.

Chemical Assignment of Structural Isomers of Sulfur-Containing Metabolites in Garlic by Liquid Chromatography−Fourier Transform Ion Cyclotron Resonance−Mass Spectrometry

Background: The chemical assignment of metabolites is crucial to understanding the relation between food composition and biological activity.Objective: This study was designed to detect and chemically assign sulfur-containing metabolites by using LC−Fourier transform ion cyclotron resonance−mass spectrometry (FTICR-MS) in Allium plants.Methods: Ultrahigh resolution (>250,000 full width at half-maximum) and mass accuracy (<1 mDa) by FTICR-MS allowed us to distinguish ions containing sulfur isotopes (32S and 34S).Results: Putative 69 S-containing monoisotopic ions (S-ions) were extracted from the metabolome data of onion (Allium cepa), green onion (Allium fistulosum), and garlic (Allium sativum) on the basis of theoretical mass differences between 32S-ions and their 34S-substituted counterparts and on the natural abundance of 34S. Eight S-ions were chemically assigned by using the reference data according to the guidelines of the Metabolomics Standards Initiative. Three ions detected in garlic were assigned as derived from the isomers γ-glutamyl-S-1-propenylcysteine and γ-glutamyl-S-2-propenylcysteine and as S-2-propenylmercaptoglutathione on the basis of differences in key product ions identified in reference tandem MS spectra.Conclusion: The ability to discriminate between such geometric isomers will be extremely useful for the chemical assignment of unknown metabolites in MS-based metabolomics.

COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access.

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

Profiling a gut microbiota-generated catechin metabolite's fate in human blood cells using a metabolomic approach

The microbial catechin metabolite δ-(3,4-dihydroxy-phenyl)-γ-valerolactone (M1) has been found in human plasma samples after intake of maritime pine bark extract (Pycnogenol®). M1 has been previously shown to accumulate in endothelial and blood cells in vitro after facilitated uptake and to exhibit anti-inflammatory activity. The purpose of the present research approach was to systematically and comprehensively analyze the metabolism of M1 in human blood cells in vitro and in vivo.A metabolomic approach that had been successfully applied for drug metabolite profiling was chosen to detect 19 metabolite peaks of M1 which were subsequently further analyzed and validated. The metabolites were categorized into three levels of identification according to the Metabolomics Standards Initiative with six compounds each confirmed at levels 1 and 2 and seven putative metabolites at level 3.The predominant metabolites were glutathione conjugates which were rapidly formed and revealed prolonged presence within the cells. Although a formation of an intracellular conjugate of M1 and glutathione (M1-GSH) was already known two GSH conjugate isomers, M1-S-GSH and M1-N-GSH were observed in the current study. Additionally detected organosulfur metabolites were conjugates with oxidized glutathione and cysteine. Other biotransformation products constituted the open-chained ester form of M1 and a methylated M1. Six of the metabolites determined in in vitro assays were also detected in blood cells in vivo after ingestion of the pine bark extract by two volunteers.The present study provides the first evidence that multiple and structurally heterogeneous polyphenol metabolites can be generated in human blood cells. The bioactivity of the M1 metabolites and their contribution to the previously determined anti-inflammatory effects of M1 now need to be elucidated.

Proposed quantitative and alphanumeric metabolite identification metrics

In June, 2014 the Metabolomics Identification Task Group published an article entitled ‘‘Metabolite identification: are you sure? And how do your peers gauge your confidence?’’(Creek et al. 2014). This article invited additional feedback and we would like to respond publically with a few more details regarding accurate reporting of metabolite identifications. The community consensus is that the leading challenge of metabolomics is the chemically accurate identification of large numbers of metabolites observed in various nontargeted profiling techniques. Metabolite identification is critically important because it provides the fundamental biological and biochemical context to the metabolomics data. However, accurate structure identification requires significant effort which increases dramatically with the increased scale of nontargeted metabolomics and typically involves hundreds or thousands of metabolites per analysis. The metabolomics community has been discussing the challenges of metabolite identification and minimum reporting criteria for some time, and the Chemical Analysis Working Group of the Metabolomics Standards Initiative proposed some basic guidelines in 2007 (Sumner et al. 2007). This group reported four identification classes as summarized below.

Metabolomic Characterization of Knockout Mutants in Arabidopsis: Development of a Metabolite Profiling Database for Knockout Mutants in Arabidopsis.

Despite recent intensive research efforts in functional genomics, the functions of only a limited number of Arabidopsis (Arabidopsis thaliana) genes have been determined experimentally, and improving gene annotation remains a major challenge in plant science. As metabolite profiling can characterize the metabolomic phenotype of a genetic perturbation in the plant metabolism, it provides clues to the function(s) of genes of interest. We chose 50 Arabidopsis mutants, including a set of characterized and uncharacterized mutants, that resemble wild-type plants. We performed metabolite profiling of the plants using gas chromatography-mass spectrometry. To make the data set available as an efficient public functional genomics tool for hypothesis generation, we developed the Metabolite Profiling Database for Knock-Out Mutants in Arabidopsis (MeKO). It allows the evaluation of whether a mutation affects metabolism during normal plant growth and contains images of mutants, data on differences in metabolite accumulation, and interactive analysis tools. Nonprocessed data, including chromatograms, mass spectra, and experimental metadata, follow the guidelines set by the Metabolomics Standards Initiative and are freely downloadable. Proof-of-concept analysis suggests that MeKO is highly useful for the generation of hypotheses for genes of interest and for improving gene annotation. MeKO is publicly available at http://prime.psc.riken.jp/meko/.

Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data.

Commentary on Shimoyama et al. (2012): three ontologies to define phenotype measurement data.